WO2018117890A1

WO2018117890A1 - A method and a cognitive system for predicting a hydraulic fracture performance

Info

Publication number: WO2018117890A1
Application number: PCT/RU2016/000907
Authority: WO
Inventors: Ivan Lvovich SOFRONOV; Dmitry Anatolievich Koroteev; Marina Nikolaevna Bulova; Kreso Kurt Butula; Dean Willberg
Original assignee: Schlumberger Technology Corporation; Schlumberger Canada Limited; Services Petroliers Schlumberger; Schlumberger Technology B.V.
Priority date: 2016-12-21
Filing date: 2016-12-21
Publication date: 2018-06-28

Abstract

A method can include receiving, via a network, data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site; accessing a database to retrieve information associated with the field site; executing, based at least in part on the data and the information, a trained machine learning algorithm using one or more processors to generate a result; and, based at least in part on the result, predicting an outcome for the hydraulic fracturing operation at the field site.

Description

A METHOD AND A COGNITIVE SYSTEM FOR PREDICTING A HYDRAULIC

FRACTURE PERFORMANCE

BACKGROUND

[0001] A stimulation treatment can be a treatment performed to restore or enhance the productivity of a well that is disposed at least in part in a reservoir of a geologic environment. Stimulation treatments can include hydraulic fracturing treatments and matrix treatments. As an example, a fracturing treatment can be performed above a fracture pressure of a reservoir formation and create a conductive flow path between the reservoir and a wellbore. As an example, a matrix treatment can be performed below a reservoir fracture pressure and may aim to restore or enhance permeability of the reservoir (e.g., following damage to a near-wellbore area). As an example, stimulation in a shale gas reservoir can include hydraulic fracturing.

SUMMARY

[0002] A method can include receiving, via a network, data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site; accessing a database to retrieve information associated with the field site; executing, based at least in part on the data and the information, a trained machine learning algorithm using one or more processors to generate a result; and, based at least in part on the result, predicting an outcome for the hydraulic fracturing operation at the field site. A system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive, analyze and store information associated with hydraulic fracturing operations; select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations; execute the at least one of the plurality of trained machine learning algorithms; predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and output the at least one outcome. One or more computer-readable storage media can include processor- executable instructions to instruct a computing system to: receive, analyze and store information associated with hydraulic fracturing operations; select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations; execute the at least one of the plurality of trained machine learning algorithms; predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and output the at least one outcome for each of the at least one of the plurality of trained machine learning algorithms. Various other apparatuses, systems, methods, etc., are also disclosed.

[0003] This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

[0005] Fig. 1 illustrates an example system that includes various components for simulating a geological environment;

[0006] Fig. 2 illustrates examples of a basin, a convention and a system;

[0007] Fig. 3 illustrates an example of a method;

[0008] Fig. 4 illustrates an example of a system;

[0009] Fig. 5 illustrates an example of a system and an example of a method;

[0010] Fig. 6 illustrates an example of a system and an example of a method;

[0011] Fig. 7 illustrates an example of an architecture;

[0012] Fig. 8 illustrates an example of a system;

[0013] Fig. 9 illustrates an example of a system;

[0014] Fig. 10 illustrates an example of a system;

[0015] Fig. 11 illustrates an example of a system;

[0016] Fig. 12 illustrates an example of a system;

[0017] Fig. 13 illustrates an example of a system;

[0018] Fig. 14 illustrates an example of a plot and examples of planning and operations phases of a system;

[0019] Fig. 15 illustrates an example of a geologic environment and examples of equipment;

[0020] Fig. 16 illustrates examples of geologic environments and examples of equipment; [0021] Fig. 17 illustrates examples of computer and network equipment; and

[0022] Fig. 18 illustrates example components of a system and a networked system.

DETAILED DESCRIPTION

[0023] This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.

[0024] Fig. 1 shows an example of a system 100 that includes various management components 1 10 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153, one or more fractures 159, etc.). For example, the management components 1 10 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 1 10).

[0025] In the example of Fig. 1 , the management components 1 10 include a seismic data component 112, an additional information component 114 (e.g., well/logging data, etc.), a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, as an example, seismic data and other information provided per the components 1 12 and 1 14 may be input to the simulation component 120.

[0026] In the example of Fig. 1, the seismic data component 112 may provide seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks.

[0027] Field acquisition equipment may be utilized to acquire seismic data, which may be in the form of traces where a trace can include values organized with respect to time and/or depth (e.g., consider ID, 2D, 3D or 4D seismic data). For example, consider acquisition equipment that acquires digital samples at a rate of one sample per approximately 4 ms. Given a speed of sound in a medium or media, a sample rate may be converted to an approximate distance. For example, the speed of sound in rock may be on the order of around 5 km per second. Thus, a sample time spacing of approximately 4 ms would correspond to a sample "depth" spacing of about 10 meters (e.g., assuming a path length from source to boundary and boundary to sensor). As an example, a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries. If the 4 second trace duration of the foregoing example is divided by two (e.g., to account for reflection), for a vertically aligned source and sensor, a deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).

[0028] As an example, the simulation component 120 may include features that allow for building a model or models of a geologic environment. As an example, a model may be a simulated version of a geologic environment. As an example, a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. As an example, one or more of the management components 110 may be part of a seismic-to-simulation framework and may include, for example, one or more components that can simulate physical phenomena in a geologic environment.

[0029] In an example embodiment, the simulation component 120 may include accessing entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that may be reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., consider entities based at least in part on the seismic data 1 12 and/or other information 114). As an example, an entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property, etc.). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0030] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT™ .NET™ framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET™ framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data. A model of a basin, a reservoir, etc. may include one or more boreholes where a borehole may be, for example, for measurements, injection, production, etc. As an example, a borehole may be a wellbore of a well, which may be a completed well (e.g., for production of a resource from a reservoir, for injection of material, etc.).

[0031] In the example of Fig. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes (e.g., consider a library that includes seismic attributes, etc.). Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 1 16). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be utilized to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Fig. 1, the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0032] As an example, the simulation component 120 may include one or more features of a simulator such as, for example, the ECLIPSE® reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECT® reservoir simulator (Schlumberger Limited, Houston Texas), the VISAGE® geomechanics simulator (Schlumberger Limited, Houston Texas), the PETROMOD® petroleum systems simulator (Schlumberger Limited, Houston Texas), the PIPESIM® network simulator (Schlumberger Limited, Houston Texas), and the MANGROVE® stimulation simulator (Schlumberger Limited, Houston Texas), which may be operable with the FRACCADE® fracture design and evaluation framework (Schlumberger Limited, Houston Texas).

[0033] In an example embodiment, the management components 1 10 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0034] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET™ tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0035] Fig. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and an instructions layer 175. As an example, the instructions layer 175 can include various sets of instructions that may be stored in a computer-readable storage medium or media where the instructions can be executable by one or more processors to instruct a computing device, a computing system, etc. to perform one or more operations. As an example, a component may be or include a set of instructions or sets of instructions. In the example of Fig. 1, the framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization. Such a model may include one or more grids.

[0036] The model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components. As an example, a user interface may be a graphical user interface (GUI) that can be rendered to a display, via a virtual reality (VR) system, etc. As an example, a VR system may include one or more features of a VR system such as, for example, the HOLOLENS® VR system marketed by Microsoft Corporation (Redmond, Washington). For example, a VR system may include goggles and/or one or more other types of wearables that can facilitate generation of and/or interaction with a virtual environment.

[0037] In the example of Fig. 1 , the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0038] In the example of Fig. 1 , data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. As an example, the model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. As an example, upon completion of a modeling session, a user may store a project. In such an example, at a later time, the project may be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0039] In the example of Fig. 1 , the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Fig. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0040] Fig. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with the one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0041] As mentioned, one or more simulator may be utilized to simulate one or more types of physical phenomena. The ECLIPSE® simulator includes numerical solvers that may provide simulation results such as, for example, results that may predict dynamic behavior for one or more types of reservoirs, results that may assist with one or more development schemes, results that may assist with one or more production schemes, etc. The VISAGE® simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, C0₂ disposal, etc. The PETROMOD® simulator includes finite element numerical solvers that may provide simulations results such as, for example, results as to structural evolution, temperature, and pressure history and as to effects of such factors on generation, migration, accumulation, and loss of oil and gas in a petroleum system through geologic time. Such a simulator can provide properties such as, for example, gas/oil ratios (GOR) and API gravities, which may be analyzed, understood, and predicted as to a geologic environment. The PIPESIM® simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM® simulator may be integrated, for example, with the AVOCET® production operations framework (Schlumberger Limited, Houston Texas). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as steam-assisted gravity drainage (SAGD), etc.).

[0042] As to simulation of a stimulation (e.g., a stimulation treatment), the

MANGROVE® framework can be operatively coupled with the PETREL® framework to share information germane to engineered stimulation designs and simulation of such designs. The MANGROVE® framework includes a hydraulic fracturing simulator that can integrate petrophysical analysis, complex fracture engines, and comprehensive geomechanics in a comprehensive, workflow. With a 3D finite-element geomechanical simulator, the MANGROVE® framework allows for simulation of fracture initiation and diversion, as well as geomechanical changes during fracturing and production. Output from the MANGROVE® framework can facilitate decision making as to field operations, which may aim to maximize production, etc. As an example, a workflow can include stimulation design and simulation, which may integrate geological and geophysical (G&G), petrophysical, geomechanical, and microseismic data.

[0043] As to the FRACCADE® framework, 2D, 3D and P3D models may be utilized that can be integrated with real-time data monitoring, pressure matching, etc. An optimization algorithm of the FRACCADE® framework can utilize information about a well, a reservoir, fluid and proppant in conjunction with one or more of operational constraints, cost constraints, production constraints, etc., to optimize stimulation design (e.g., based on net present value (NPV)). Parameters of a design can include, for example, propped fracture length or length ranges, selected fluids, selected proppants, selected proppant concentrations. As an example, transient inflow performance relationships (IPRs) may be generated for use in forecasting production.

[0044] The FRACCADE® framework includes a numerical hydraulic fracture simulator that uses a fracture geometry model that can provide for modeling of fracture growth, for example, into layers that may be above and/or below a pay zone as well as, for example, fracture extension and rock mechanics that may allow for screenouts and slurry dehydration to be stimulated. Stimulation may be simulated in a manner that can account for proppant bridging, slurry dehydration or one or more other relevant phenomena.

[0045] The FRACCADE® framework allows for acid analysis and simulation, which may account for gel-pad flush treatments, gelled acid systems, LCA systems, retarded systems, etc. Such an approach can account for plugged flow along a fracture, cross-section etch area based on averaged rock properties along a fracture height, etc.

[0046] The FRACCADE® framework can provide for simulation of simultaneous initiation and extension of multiple hydraulic fractures. Such an approach may utilize a Perkins-Kern-Nordgren (PKN) geometry, for example, where fracture length tends to be much greater than fracture height. A workflow may consider, for example, use of a Khristianovic- Geertsma-de Klerk (KGD) geometry, for example, where fracture height tends to be greater than fracture length (e.g., according to one or more physical, geometrical models). As an example, a simulation may calculate cross-flow after pumping of fluid ceases and, perforation friction may be calculated on a layer basis.

[0047] The FRACCADE® framework can provide for analysis of fracture data generated by a stimulation treatment, which may be utilized, for example, to design a propped fracture treatment. Fracturing parameters as to an ongoing, past or future treatment may include fracture closure pressure, model type, fluid efficiency, leakoff coefficient, etc. An Optimized Pressure Analysis (OPAL) component may provide for importing results, for example, to determining reservoir closure pressure, fracture fluid efficiency, fracture height growth, confinement, etc. Such a component may be utilized in real-time as data are generated and/or post-operation, on historical data.

[0048] As an example, a falloff test can include measurement and analysis of pressure data taken after an injection well is shut in. These data can be transient well-test data, which may be acquired via on-site equipment and transmitted via one or more network interfaces of on-site equipment to remote computing equipment (e.g., cloud-based resources, etc.). In a falloff test, wellhead pressure can rise during injection, and if the well remains relatively full of liquid after shut-in of an injector, the pressure can be measured at the surface, and bottomhole pressures may be calculated by adding the pressure from the hydrostatic column to the wellhead pressure. As various types of water-injection wells can be fractured during injection, and injection wells may go on vacuum, the fluid level can fall below the surface. In such a scenario, a workflow can include acquiring information from sensors such as one or more bottomhole pressure gauges and/or sonic devices.

[0049] As an example, a workflow can include performing a minifrac operation and/or acquiring data from a minifrac operation and/or accessing a framework that can analyze minifrac operations (e.g., via modeling, etc.). A minifrac operation is a small fracturing treatment that can be performed before a planned operational hydraulic fracturing treatment where the minifrac operation aims to acquire data (e.g., job design and execution data) and to confirm a predicted response of a treatment interval. A minifrac procedure can provide design data from parameters associated with injection of fluids and a subsequent pressure decline. As an example, in a planning phase and/or in an operational phase of a framework, either of which may include online field equipment, a job procedure and/or one or more treatment parameters may be refined according to results of one or more minifrac treatments.

[0050] As an example, a workflow may include acquiring data as to wireline imaging and minifrac testing. As an example, a framework may be accessed that can analyze such data. As an example, a framework may be accessed that can analyze falloff data (e.g., falloff test data, etc.).

[0051] As an example, in the absence of leakoff and/or minifrac tests, a workflow may aim to resolve uncertainty in a fracture gradient and mud-weight window for a drilling design to reach a deeper targeted reservoir. In such an example, the SONIC SCANNER™ tool system (Schlumberger Limited, Houston, Texas) or other type of sonic tool system may be implemented for acoustic scanning to measure sonic velocities at multiple depths of investigation, which may provide a multidimensional (e.g., 3D) characterization from which stress magnitudes and a stress regime can be calculated. Such calculated information may be utilized as input to a drilling design and model calibration framework. As an example, such an approach may aim to accurately specify mud weights for different hole sections and offset well locations based on continuous elastic properties and a calibrated mechanical earth model (MEM), which may be calculated with increased confidence by using a fracture gradient profile based on stress estimations from sonic tool system measurements.

[0052] The FRACCADE® framework can provide for pressure matching. For example, measured pressure data and simulated pressure data may be matched as to injection, decline, etc. Such an approach can account for changes such as, for example, changes due to rate and fluid viscosity variations. Pressure matching may be part of a workflow that utilizes prescribed treatment parameters (e.g., slurry injection rates, proppant concentration, etc.) to predict fracturing pressures for a given set of fracture parameters (e.g., stresses, fluid leakoff, etc.). As an example, additives, foams, etc. may be analyzed, optionally via one or more injection points.

[0053] The FRACCADE® framework may provide for design and analysis of tubing and/or surface equipment specification (e.g., amount of water, types of fluids, types of proppant(s), types of pumps, etc.). As an example, forces and effects thereof on tubing, packers, etc. may be determined and analyzed. A simulator may, for example, provide for simulation of one or more of applied force, pressure-induced force, frictional force and thermally induced force. Calculations may account for tubing to packer motion, well completion type, current well conditions, changes that occur during treatment, etc.

[0054] The FRACCADE® framework may provide for generation of a pumping schedule. Such a schedule may be implemented in the field, simulated, etc. As an example, feedback from an ongoing stimulation treatment may be utilized to dynamically adjust a pumping schedule.

[0055] As an example, a workflow can include predicting hydraulic fracture (HF) performance, optimization of HF design, control of fracturing job and evaluation of near wellbore formation effects. Such a workflow may combine physics-based and data-driven methods for modeling and forecasting reservoir production.

[0056] As mentioned, the system 100 of Fig. 1 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® framework, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access instructions such as instructions of a plug-in (e.g., external executable code, etc.). [0057] Fig. 1 also shows instructions 198, which may operate in conjunction with the framework 170. For example, the instructions 198 may be implemented as one or more plug- ins, one or more external sets of instructions, one or more components, etc. As an example, the instructions 198 may include sets of instructions associated with the commercially available TECHLOG® framework (Schlumberger Limited, Houston, TX), which can provide wellbore- centric, cross-domain workflows based on a data management layer. The TECHLOG® framework includes features for petrophysics (core and log), geology, drilling, reservoir and production engineering, and geophysics.

[0058] As an example, the InterACT® system (Schlumberger Limited, Houston,

Texas) may be implemented to provide for connectivity, collaboration, information handling, etc. Such a multifunction system may provide for collaboration to facilitate planning and implementation of downhole, desktop or other workflows. Such workflows may include one or more of a stimulation operation, a drilling operation, wireline logging, a testing operation, production monitoring, downhole monitoring, etc. (e.g., as workflow steps, workflow processes, workflow algorithms, etc.). Processor-executable instructions may provide for a variety of graphical user interfaces (e.g., for devices such as desktop terminals or computers, tablets, mobile devices, smart phones, etc.). As an example, data may be exchanged between devices, frameworks, components, etc., using a markup language. An example of a markup language is the WITSML™ markup language. The use of WITSML™ data objects and the data access application programming interface (API) can allow for development of an application that may exchange data with one or more other applications, to combine multiple data sets from different entities (e.g., services, vendors, etc.), for example, into an application (e.g., for analysis, visualization, collaboration, etc.).

[0059] Fig. 2 shows an example of a sedimentary basin 210 (e.g., a geologic environment), an example of a method 220 for model building (e.g., for a simulator, etc.), an example of a formation 230, an example of a borehole 235 in a formation, an example of a convention 240 and an example of a system 250.

[0060] As an example, data acquisition, reservoir simulation, petroleum systems modeling, etc. may be applied to characterize various types of subsurface environments, including environments such as those of Fig. 1.

[0061] In Fig. 2, the sedimentary basin 210, which is a geologic environment, includes horizons, faults, one or more geobodies and facies formed over some period of geologic time. These features are distributed in two or three dimensions in space, for example, with respect to a Cartesian coordinate system (e.g., x, y and z) or other coordinate system (e.g., cylindrical, spherical, etc.). As shown, the model building method 220 includes a data acquisition block 224 and a model geometry block 228. Some data may be involved in building an initial model and, thereafter, the model may optionally be updated in response to model output, changes in time, physical phenomena, additional data, etc. As an example, data for modeling may include one or more of the following: depth or thickness maps and fault geometries and timing from seismic, remote-sensing, electromagnetic, gravity, outcrop and well log data. Furthermore, data may include depth and thickness maps stemming from facies variations (e.g., due to seismic unconformities) assumed to following geological events ("iso" times) and data may include lateral facies variations (e.g., due to lateral variation in sedimentation characteristics).

[0062] To proceed to modeling of geological processes, data may be provided, for example, data such as geochemical data (e.g., temperature, kerogen type, organic richness, etc.), timing data (e.g., from paleontology, radiometric dating, magnetic reversals, rock and fluid properties, etc.) and boundary condition data (e.g., heat-flow history, surface temperature, paleowater depth, etc.).

[0063] In basin and petroleum systems modeling, quantities such as temperature, pressure and porosity distributions within the sediments may be modeled, for example, by solving partial differential equations (PDEs) using one or more numerical techniques. Modeling may also model geometry with respect to time, for example, to account for changes stemming from geological events (e.g., deposition of material, erosion of material, shifting of material, etc.).

[0064] The aforementioned commercially available modeling framework marketed as the PETROMOD® framework (Schlumberger Limited, Houston, Texas) includes features for input of various types of information (e.g., seismic, well, geological, etc.) to model evolution of a sedimentary basin. The PETROMOD® framework provides for petroleum systems modeling via input of various data such as seismic data, well data and other geological data, for example, to model evolution of a sedimentary basin. The PETROMOD® framework may predict if, and how, a reservoir has been charged with hydrocarbons, including, for example, the source and timing of hydrocarbon generation, migration routes, quantities, pore pressure and hydrocarbon type in the subsurface or at surface conditions. In combination with a framework such as the PETREL® framework, workflows may be constructed to provide basin- to-prospect scale exploration solutions. Data exchange between frameworks can facilitate construction of models, analysis of data (e.g., PETROMOD® framework data analyzed using PETREL® framework capabilities), and coupling of workflows. As an example, the TECHLOG® framework may be implemented in a workflow, for example, using one or more features for petrophysics (core and log), geology, drilling, reservoir and production engineering, and geophysics.

[0065] As shown in Fig. 2, the formation 230 includes a horizontal surface and various subsurface layers. As an example, a borehole may be vertical. As another example, a borehole may be deviated. In the example of Fig. 2, the borehole 235 may be considered a vertical borehole, for example, where the z-axis extends downwardly normal to the horizontal surface of the formation 230. As an example, a tool 237 may be positioned in a borehole, for example, to acquire information. As mentioned, a borehole tool can include one or more sensors that can acquire borehole images via one or more imaging techniques. A data acquisition sequence for such a tool can include running the tool into a borehole with acquisition pads closed, opening and pressing the pads against a wall of the borehole, delivering electrical current into the material defining the borehole while translating the tool in the borehole, and sensing current remotely, which is altered by interactions with the material.

[0066] As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology.

[0067] As an example, one or more probes may be deployed in a borehole via a wireline or wirelines. As an example, a probe may be deployed via slickline or coiled tubing. As an example, a probe may be wired or wireless, as to deployment and/or communication of information. As an example, a probe may emit energy and receive energy where such energy may be analyzed to help determine mineral composition of rock surrounding a wellbore. As an example, nuclear magnetic resonance may be implemented (e.g., via a wireline, downhole NMR probe, etc.), for example, to acquire data as to nuclear magnetic properties of elements in a formation (e.g., hydrogen, carbon, phosphorous, etc.).

[0068] As an example, lithology scanning technology may be employed to acquire and analyze data. For example, consider the commercially available LITHO SCANNER™ technology marketed by Schlumberger Limited (Houston, Texas). As an example, a LITHO SCANNER™ tool may be a gamma ray spectroscopy tool. [0069] As an example, a tool may be positioned to acquire information in a portion of a borehole. Analysis of such information may reveal vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc. As an example, a tool may acquire information that may help to characterize a fractured reservoir, optionally where fractures may be natural and/or artificial (e.g., hydraulic fractures). Such information may assist with completions, stimulation treatment, etc. As an example, information acquired by a tool may be analyzed using a framework such as the aforementioned TECHLOG® framework (Schlumberger Limited, Houston, Texas).

[0070] As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.). As an example, one or more tools may provide data that can be used in a workflow or workflows that may implement one or more frameworks (e.g., PETREL®, TECHLOG®, PETROMOD®, MANGROVE®, FRACCADE®, etc.).

[0071] As to the convention 240 for dip, as shown in Fig. 2, the three dimensional orientation of a plane can be defined by its dip and strike. Dip is the angle of slope of a plane from a horizontal plane (e.g., an imaginary plane) measured in a vertical plane in a specific direction. Dip may be defined by magnitude (e.g., also known as angle or amount) and azimuth (e.g., also known as direction). As shown in the convention 240 of Fig. 2, various angles□ indicate angle of slope downwards, for example, from an imaginary horizontal plane (e.g., flat upper surface); whereas, dip refers to the direction towards which a dipping plane slopes (e.g., which may be given with respect to degrees, compass directions, etc.). Another feature shown in the convention of Fig. 2 is strike, which is the orientation of the line created by the intersection of a dipping plane and a horizontal plane (e.g., consider the flat upper surface as being an imaginary horizontal plane).

[0072] Some additional terms related to dip and strike may apply to an analysis, for example, depending on circumstances, orientation of collected data, etc. One term is "true dip" (see, e.g., ϋϊρτ in the convention 240 of Fig. 2). True dip is the dip of a plane measured directly perpendicular to strike (see, e.g., line directed northwardly and labeled "strike" and angle□—) and also the maximum possible value of dip magnitude. Another term is "apparent dip" (see, e.g., Dip_A in the convention 240 of Fig. 2). Apparent dip may be the dip of a plane as measured in any other direction except in the direction of true dip (see, e.g., DA as DipA for angle□); however, it is possible that the apparent dip is equal to the true dip (see, e.g.,□ as DipA = ϋϊρτ for angle□ z i with respect to the strike). In other words, where the term apparent dip is used (e.g., in a method, analysis, algorithm, etc.), for a particular dipping plane, a value for "apparent dip" may be equivalent to the true dip of that particular dipping plane.

[0073] As shown in the convention 240 of Fig. 2, the dip of a plane as seen in a cross- section perpendicular to the strike is true dip (see, e.g., the surface with□ as Dip_A = Dip_T for angle U 9 with respect to the strike). As indicated, dip observed in a cross-section in any other direction is apparent dip (see, e.g., surfaces labeled DipA). Further, as shown in the convention 240 of Fig. 2, apparent dip may be approximately 0 degrees (e.g., parallel to a horizontal surface where an edge of a cutting plane runs along a strike direction).

[0074] In terms of observing dip in wellbores, true dip is observed in wells drilled vertically. In wells drilled in any other orientation (or deviation), the dips observed are apparent dips (e.g., which are referred to by some as relative dips). In order to determine true dip values for planes observed in such boreholes, as an example, a vector computation (e.g., based on the borehole deviation) may be applied to one or more apparent dip values.

[0075] As mentioned, another term that finds use in sedimentological interpretations from borehole images is "relative dip" (e.g., DipR). A value of true dip measured from borehole images in rocks deposited in very calm environments may be subtracted (e.g., using vector- subtraction) from dips in a sand body. In such an example, the resulting dips are called relative dips and may find use in interpreting sand body orientation.

[0076] A convention such as the convention 240 may be used with respect to an analysis, an interpretation, an attribute, etc. (see, e.g., various blocks of the system 100 of Fig. 1). As an example, various types of features may be described, in part, by dip (e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikes and sills, metamorphic foliation, etc.). As an example, dip may change spatially as a layer approaches a geobody. For example, consider a salt body that may rise due to various forces (e.g., buoyancy, etc.). In such an example, dip may trend upward as a salt body moves upward.

[0077] Seismic interpretation may aim to identify and/or classify one or more subsurface boundaries based at least in part on one or more dip parameters (e.g., angle or magnitude, azimuth, etc.). As an example, various types of features (e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikes and sills, metamorphic foliation, etc.) may be described at least in part by angle, at least in part by azimuth, etc. [0078] As an example, equations may be provided for petroleum expulsion and migration, which may be modeled and simulated, for example, with respect to a period of time. Petroleum migration from a source material (e.g., primary migration or expulsion) may include use of a saturation model where migration-saturation values control expulsion. Determinations as to secondary migration of petroleum (e.g., oil or gas), may include using hydrodynamic potential of fluid and accounting for driving forces that promote fluid flow. Such forces can include buoyancy gradient, pore pressure gradient, and capillary pressure gradient.

[0079] As shown in Fig. 2, the system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270. As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing instructions, for example, consider the instructions 270 as including instructions executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards (e.g., one or more GPUs, etc.), a display interface (e.g., wired or wireless), etc. As an example, imagery such as surface imagery (e.g., satellite, geological, geophysical, etc.) may be stored, processed, communicated, etc. As an example, data may include SAR data, GPS data, etc. and may be stored, for example, in one or more of the storage devices 252.

[0080] As an example, the instructions 270 may include instructions (e.g., stored in memory) executable by one or more processors to instruct the system 250 to perform various actions. As an example, the system 250 may be configured such that the instructions 270 provide for establishing the framework 170 of Fig. 1 or a portion thereof. As an example, one or more methods, techniques, etc. may be performed at least in part via instructions, which may be, for example, instructions of the instructions 270 of Fig. 2.

[0081] As an example, a framework can include various components. For example, a framework can include one or more components for prediction of reservoir performance, one or more components for optimization of an operation or operations, one or more components for control of production engineering operations, etc. As an example, a framework can include components for prediction of reservoir performance, optimization and control of production engineering operations performed at one or more reservoir penetrating wells. Such a framework may, for example, allow for implementation of various methods. For example, consider an approach that allows for a combination of physics-based and data-driven methods for modeling and forecasting a reservoir production.

[0082] Fig. 3 shows an example of a method 300 that includes generating fractures as part of a stimulation treatment (e.g., hydraulic fracturing). As shown, the method 300 can include various operational blocks such as one or more of the blocks 301, 302, and 303. The block 301 may be a drilling block that includes drilling into a formation 310 that includes layers 312, 314 and 316 to form a wellbore 330 with a kickoff 332 to a portion defined by a heel 334 and a toe 336, for example, within the layer 314.

[0083] As illustrated with respect to the block 302, the bore 330 may be at least partially cased with casing 340 into which a string or line 350 may be introduced that carries a perforator 360. As shown, the perforator 360 can include a distal end 362 and charge positions 365 associated with activatable charges that can perforate the casing 340 and form channels 315-1 in the layer 314. Next, per the block 303, fluid may be introduced into the bore 330 between the heel 334 and the toe 336 where the fluid passes through the perforations in the casing 340 and into the channels 315-1. Where such fluid is under pressure, the pressure may be sufficient to fracture the layer 314, for example, to form fractures 317-1. In the block 303, the fractures 317-1 may be first stage fractures, for example, of a multistage fracturing operation.

[0084] In a method such as the method 300 of Fig. 3, it may be desirable that a plug degrades, that a plug seat degrades, that at least a portion of a borehole tool degrades, etc. For example, a plug may be manufactured with properties such that the plug withstands, for a period of time, conditions associated with an operation and then degrades (e.g., when exposed to one or more conditions). In such an example, where the plug acts to block a passage for an operation, upon degradation, the passage may become unblocked, which may allow for one or more subsequent operations.

[0085] As an example, a component may be degradable upon contact with a fluid such as an aqueous ionic fluid (e.g., saline fluid, etc.). As an example, a component may be degradable upon contact with well fluid that includes water (e.g., consider well fluid that includes oil and water, etc.). As an example, a component may be degradable upon contact with a fracturing fluid (e.g., a hydraulic fracturing fluid). As an example, a degradation time may depend on a component dimension or dimensions and can differ for various temperatures where a component is in contact with a fluid that is at least in part aqueous (e.g., include water as a medium, a solvent, a phase, etc.). [0086] In a method such as the method 300 of Fig. 3, the fluid introduced into the bore

430 can include proppant and one or more chemicals. Proppant can be sized particles mixed with fracturing fluid to hold fractures open after a hydraulic fracturing treatment. Proppant may include naturally occurring sand grains, man-made or specially engineered particles such as, for example, resin-coated sand or high-strength ceramic materials like sintered bauxite. Proppant materials can be sorted for size and shape to provide an efficient conduit for production of fluid from a reservoir to a wellbore.

[0087] As to chemicals, one or more of the chemicals of the OPENFRAC™ fluid family of chemicals (Schlumberger Limited, Houston, Texas) may be utilized or, for example, one or more other chemicals. As an example, consider sodium chloride, magnesium chloride, amphoteric alkyl amine, calcium magnesium sodium phosphate, propan-2-ol, acrylamide copolymer, ammonium sulfate, sodium sulfate, potassium chloride, urea, hypochlorous acid, non-crystalline silica, dimethyl siloxanes, silicones, guar gum, hemicellulase (enzyme), boric acid, calcium chloride, etc.

[0088] As an example, a fluid can include one or more scale inhibitors that may act to reduce scaling of proppant. As an example, a fluid can provide for crosslinking, gel formation, linear gel formation, slickwater, etc. As an example, one or more chemicals can provide for drag reduction, load-water recovery, and/or formation stabilization. As an example, a chemical may provide for degradation of a component that is intended to be degraded during and/or after an operation.

[0089] As an example, a fluid may be formulated to facility transport of proppant (e.g., propping agent) in a fracture, may be formulated to be compatible with formation rock and fluid, may be formulated to generate enough pressure drop along a fracture to create a fracture of a desired width, may be formulated to minimize friction pressure losses during injection, may be formulated using chemical additives that are approved according to local environmental regulations, may be formulated to exhibit controlled-break to a low- viscosity fluid for cleanup after treatment, and may be formulated as to cost-effectiveness.

[0090] As an example, one or more workflows may be implemented to optimize formulation of fluid that transports proppant to a fracture such that the proppant forms a proppant pack in the fracture. As an example, a workflow can include determining effective permeability of a proppant pack in a manner that depends on one or more chemicals that are present in hydraulic fracturing fluid. [0091] As an example, viscosity of a fluid may be optimized via chemical composition.

As an example, density of a fluid may be optimized via chemical composition. As an example, viscosity and density of a fluid may be optimized via chemical composition. In such examples, optimization can include modeling of a proppant pack and simulating one or more physical phenomena, which can include flow, temperature, reaction rate or rates of various reactions, etc.

[0092] As an example, a method may optimize chemistry based at least in part on a type of fracture to be generated. For example, low-viscosity fluids pumped at high rates may aim to generate narrow, complex fractures with low-concentrations of propping agent (e.g., about 0.2 to about 5 lbm proppant added (PPA) per gallon (e.g., about 24 g/1 to about 600 g/1)).

[0093] To minimize risk of premature screenout, a pumping rate can be selected to transport proppant over a desired distance, which may be along a horizontal wellbores. For a wide-biwing fracture, fluid can be selected to be of a viscosity for suspension and transport of higher proppant concentrations. Such a treatment fluid may be pumped at a lower pump rate and may create wider fractures (e.g., about 0.5 cm to about 2.5 cm).

[0094] Fluid density can affect the surface injection pressure and the ability of the fluid to flow back after treatment. In low-pressure reservoirs, low-density fluids, like foam, can be used to assist in fluid cleanup. Conversely, in certain deep reservoirs (including offshore), higher density fracturing fluids may be utilized.

[0095] Fig. 3 also shows a pump 382, pump equipment 384 and monitoring equipment

386 that may be utilized to perform at least a portion of a method such as the method 300 of Fig. 3. As shown in Fig. 3, a rig 388 can be located at a surface location along with the pump 382, the pump equipment 384 and the monitoring equipment 386. As an example, one or more supplies of proppant, chemicals, etc. may be available at a field site where, for example, formulation and mixing may be performed, optionally according to real-time or near real-time analysis of proppant conductivity, etc. As an example, a computer may be operated to output results that can be communicated to a controller and/or an operator to formulate fluid (e.g., including proppant) on site.

[0096] As to a fracturing operation, a pressure weight may be of the order of thousands of pounds per square inch (psi). As an example, a flow rate may be of the order of tens of barrels of fluid per minute. As an example, a plurality of pumps may be provided, which may be vehicle-based pumps (e.g., pump trucks). [0097] As to monitoring, a fiber cable may extend into a well where the fiber cable can include one or more individual fibers such as, for example, optical fibers that can provide for frequency and/or temperature sensing. As to frequency, an outer surface that is in fluid may sense characteristics of flow of the fluid in the well. For example, fluid flowing in a conduit (e.g., tubing, a casing, etc.) can result in vortex formation where vortices may shed at one or more frequencies that can impart energy that is sensed by the fiber cable. As an example, a method can include analyzing temperature of fluid as sensed via a fiber cable to determine one or more aspects as to fluid flow in a well. As an example, a fiber cable may be arranged to sense one or more physical phenomena, directly and/or indirectly, such as, for example, strain, temperature, pressure, frequency, vibration, flow, etc. As an example, a fiber cable may be part of a distributed monitoring system (DMS) for distributed pressure sensing (DPS), distributed temperature sensing (DTS), distributed frequency sensing (DFS), etc.

[0098] Fig. 4 shows an example of a geologic environment 401 that includes monitoring equipment 402, a pump 403, equipment 404, a seismic sensor or receiver array 405 and a remote facility 406. As shown, various types of communication may be implemented such that one or more pieces of equipment can communicate with one or more other pieces of equipment. As an example, equipment can include geopositioning equipment (e.g., GPS, etc.). As an example, equipment can include one or more satellites and one or more satellite links (e.g., dishes, antennas, etc.).

[0099] In the example of Fig. 4, a monitoring well 410 and a treatment well 420 are disposed in the geologic environment 401. The monitoring well 410 includes a plurality of sensors 412-1 and 412-2 and a fiber cable sensor 414 and the treatment well 420 includes a fiber cable sensor 424 and one or more sets of perforations 425-1, 425-2, 425-N.

[00100] Equipment in the example of Fig. 4 can be utilized to perform one or more methods. As an example, data associated with hydraulic fracturing events may be acquired via various sensors. As an example, P-wave data (compressional wave data) can be utilized to assess such events (e.g., microseismic events). Such information may allow for adjusting one or more field operations. As an example, data acquired via the fiber cable sensor 424 can be utilized to generate information germane to a fluid flow-based treatment process (e.g., to determine where fluid pumped into a well may be flowing, etc.).

[00101] In the example of Fig. 4, the set of perforations 425-1 are shown as including associated fractures and microseismic events that generate energy that can be sensed by various sensors in the geologic environment 401. Arrows indicate a type of wave that may be sensed by an associate sensor. For example, as mentioned with respect to the table or data structure 408, the seismic sensor array 405 can sense P, SV and SH waves while the fiber cable sensor 424 can sense P waves.

[00102] As an example, the fiber cable sensor 424 can sense seismic energy as associated with fluid flow, for example, as associated with vortex shedding and/or one or more other phenomena of fluid flow in a well (e.g., a casing, tubing, a conduit, etc.). As an example, such seismic energy may be sensed as seismic traces that include information as to vibrations associated with fluid flow (e.g., fluid flow noise). As an example, the fiber cable sensor 424 may sense one or more of strain and temperature in addition to sensing seismic energy.

[00103] As an example, the equipment 402 can be operatively coupled to various sensors in the monitor well 410 and the treatment well 420. As an example, the equipment 402 may be on-site where wires are coupled from sensors to the equipment 402, which may be vehicle- based equipment (e.g., a data acquisition and/or control truck, etc.). As an example, the equipment 404 may control the pump 403 (e.g., or pumps) that can direct fluid into the treatment well 420. For example, a line is shown as a conduit that is operatively coupled between the pump 403 and the treatment well 420.

[00104] As an example, information acquired by the equipment 402 may be utilized to control one or more treatment processes controlled by the equipment 404. For example, the equipment 402 and the equipment 404 may be in direct and/or indirect communication via one or more communication links (e.g., wire, wireless, local, remote, etc.). In such an example, information acquired during a treatment process can be utilized in real-time (e.g., near realtime) to control the treatment process. For example, the equipment 402 can acquire data via sensors in the wells 410 and 420 and output information to the equipment 404 for purposes of controlling an on-going treatment process. As an example, such information may be utilized to control and/or to plan a subsequent treatment process, for example, additionally or alternatively to controlling an on-going treatment process.

[00105] As an example, a treatment process can include hydraulic fracturing. As an example, acquired data can include microseismic event data. As an example, a method can include determining the extent of rock fracturing induced by a treatment process, which may aim to stimulate a reservoir. [00106] As an example, a method can include hydraulic fracture monitoring (HFM). As an example, a method can include monitoring one or more types of reservoir stimulation processes where one or more of such processes may be performed in stages. As an example, a stage may be of a duration of the order of hours or longer (e.g., several days). As an example, a method can include determining the presence, extent, and/or associated volume of induced fractures and fracture networks, which may be utilized for calculating an estimated reservoir stimulation volume (e.g., ESV) that may assist, for example, in economic evaluation of well performance. As an example, an analysis may aim to increase ESV, for example, a conductivity analysis may output results that can be utilized to estimate ESV and to selected and/or adjust one or more parameters (e.g., parameter values) in an effort to increase ESV with respect to a field operation (e.g., a stimulation treatment).

[00107] As an example, real-time data may be rendered to a display (e.g., as a plot, plots, etc.). As an example, real-time data may be assessed in real-time (e.g., near real-time that includes computation and transmission times) during perforation flow for one or more sets of perforations. In such an example, such assessments may allow a treatment process to be optimized during the treatment process in real-time (e.g., near real-time). Such assessments may be utilized for one or more post treatment analyses, for example, to plan, perform, control, etc. one or more future treatments (e.g., in a same well, a different well, etc.).

[00108] As an example, a method can include acquiring data germane to flow in one or more wells and/or via perforations in one or more wells. As an example, a method can include acquiring data germane to locating one or more fractures. As an example, a method can include a real-time portion and a post-process portion.

[00109] As an example, a framework or frameworks may be utilized prior to, during and/or after performing one or more stimulation operations. For example, a graphical user interface rendered to a display may be utilized to control a framework to determine proppant and or chemical compositions of a hydraulic fracturing fluid prior to, during and/or after performing one or more stimulation operations. As an example, chemical composition may aim to meet one or more criteria. As mentioned, one criterion may be associated with degradation of a degradable component. Other criteria can be associated with flow of proppant, distribution of proppant, packing of proppant, flow of fluid through a porous network formed by proppant, etc. [00110] As an example, a workflow may aim to optimize hydrocarbon reservoir productivity via one or more hydraulic fracturing processes, which may be germane to a value such as ESV. Such a workflow can include comparing analyses for multiple fracture conductivity scenarios. These scenarios can be realized at least in part through multiple numerical simulations of inflow and outflow processes in one or more three-dimensional models of a sand pack (e.g., a proppant pack) and an attached formation representing a portion of a reservoir fracture. Results of such a comparative analysis or analyses can be utilized to determine a selected chemistry, reservoir and operational parameters optimized for a field fracturing operation.

[00111] As an example, a method can include improving hydrocarbon reservoir productivity through optimization of fracture conductivity of proppant by evaluating multiple fracture properties through numerical modeling on three-dimensional fracture models.

[00112] As an example, one or more operations may aim to comport with API RP 61 "Recommended Practices for Evaluating Short term Proppant Pack Conductivity" and/or API RP 60 "Recommended Practices for testing High-Strength Proppants Used in Hydraulic Fracturing Operations".

[00113] In hydraulic fracture treatment design, choice of proppant can impact overall job economics, treatment operations, and ultimate productivity of a well. A choice of proppant can be based at least in part on a balance between effective fracture length and conductivity against reservoir flow capacity. An accurate assessment of proppant pack conductivity under reservoir stress and flow conditions along with knowledge of reservoir formation deliverability can facilitate hydraulic fracture treatment parameter selection.

[00114] As an example, a workflow may utilize one or more machine learning algorithms as implemented using a computing system. For example, a workflow may include training a machine learning algorithm using historical or exploratory data and generating a synthetic elastic property log of a reservoir by supplying the trained machine learning algorithm with data acquired from a production wellbore.

[00115] As an example, machine learning may include use of one or more artificial neural networks (ANNs). An ANN can be part of a model trained with data where input can be received to generate output, which may be data, settings, ranked output, etc. A workflow may include calibration and/or validation of a model or models. [00116] As an example, a system can be a cognitive advisory system (CAS) that can be operated to perform at least a portion of a prediction workflow, an optimization workflow, and a control of HF design and production workflow, etc.

[00117] As an example, a CAS can utilize one of or both of simulation data (e.g., synthetic data) and/or real field data. As an example, a CAS can be operated as a self-learning computerized system that may runs in one or more modes. For example, consider a two mode operation scheme that includes a permanent online machine learning mode (e.g., a slow background mode) and an operational mode (e.g., a fast optimization/prediction/control mode). As an example, a CAS can provide for continuous improvement of its "expertise" (e.g., in the machine learning mode) and, thereby, enhance accuracy of its operation mode. As an example, a CAS can continuously renew one or more associated databases, for example, by accounting and assimilation of available data on various HF jobs and by providing recognition of relevant cases (fingerprinting) and classification. In such an example, fingerprinting may be utilized as a data reduction technique, a pattern recognition technique and/or a classification technique. As an example, fingerprint analysis may utilize one or more visual imagery pattern recognition techniques that may be amenable to operation using multiple processing cores such as in graphics processing units (GPUs). As an example, a CAS can allows for assessing on-line and off-line information for generation of recommendations for control actions, for example, to help prevent hazardous situations and mitigate risks of HF jobs operational failures and ensures reaching the design outcomes. As an example, a CAS may monitor, record, process and use job data acquired from pertinent surface and downhole sensors (e.g., pressure, flow rate, density, viscosity, etc.) and derived/calculated data (e.g., pressure, concentration, addition rate, etc.) during one or more HF operations. As an example, a CAS may operate within preset engineering technical, operational and economic ranges and limits.

[00118] Fig. 5 shows an example of a CAS 500 that includes a prediction block 510 for prediction of HF production, a determination block 520 for determining the effect of HF production on fluids (e.g., hydrocarbon and water), an optimization block 530 for optimization of one or more HF parameters (e.g., design parameters), a control block 540 for controlling one or more HF operations (e.g., surface control of one or more HF jobs) and an analysis block 550 for analyzing in-situ or post-fracturing adjustment of near wellbore formation characteristics.

[00119] Fig. 5 also shows an example of a method 580 that includes a reception block 582 for receiving, via a network, data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site; an access block 584 for accessing a database to retrieve information associated with the field site; an execution block 586 for executing, based at least in part on the data and the information, a trained machine learning algorithm using one or more processors to generate a result; and a prediction block 588 for, based at least in part on the result, predicting an outcome for the hydraulic fracturing operation at the field site. As shown, the method 580 can include an output block 590 for outputting one or more outcomes. In such an example, the output block 590 may transmit one or more outcomes via a network, for example, to a field site to control one or more pieces of equipment at the field site. As an example, the output block 590 may transmit one or more outcomes to the database, for example, to store the outcomes as being associated with hydraulic fracturing.

[00120] As an example, the prediction block 588 can include, for example, predicting one or more conditions associated with hydraulic fracturing (HF) of a reservoir, and the output block 590 can include, for example, provide for outputting information that controls one or more operations associated with hydraulic fracturing, which may be, for example, associated with production of fluid from the reservoir.

[00121] As an example, the method 580 may be implemented using the system 500. As an example, the method 580 may be a workflow or a part of a workflow or workflows.

[00122] As an example, the method 580 of Fig. 5 may include implementing one or more algorithms, which may be or include, for example, one or more machine learning based algorithms, etc. As an example, the method 580 of Fig. 5 may include training one or more algorithms, which may be or include, for example, one or more machine learning based algorithms, etc.

[00123] As an example, a framework can implement machine learning, for example, as a method that can devise one or more algorithms that can be utilized for generating predictions. In such an example, an algorithm may be a model based algorithm where, for example, a model may be formulated, adjusted, etc. and utilized, at least in part, to predict one or more conditions, which may be or include one or more future conditions (e.g., a condition that may occur and that may be characterized by a likelihood of occurrence, optionally contingent on occurrence of one or more other conditions). A framework may provide relatively reliable and repeatable predictions and, for example, may help to uncover insights through learning (e.g., from historical relationships, trends in data, etc.). [00124] As to learning, a framework may implement one or more types of learning. For example, consider one or more of decision tree learning, association rule learning, artificial neural network (ANN) learning, deep learning (e.g., multiple hidden layers in an artificial neural network, etc.), inductive logic programming (ILP) learning, support vector machines (SVM) learning (e.g., a set of related supervised learning methods used for classification and regression), cluster analysis learning, Bayesian network learning (e.g., a belief network or directed acyclic graphical model that includes a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG), etc.), reinforcement learning (e.g., how an agent ought to take actions in an environment so as to maximize some notion of long-term reward, etc.), representation learning (e.g., to discover representations of inputs provided during training, etc.), manifold learning (e.g., low-dimensional space, etc.), similarity and/or metric learning (e.g., learning a similarity function (or a distance metric function) that can predict if items are similar/dissimilar), sparse dictionary learning (e.g., a datum represented as a linear combination of basis functions), and genetic learning (e.g., a search heuristic that mimics a process of natural selection via mutation and/or crossover).

[00125] The method 580 is shown in Fig. 5 in association with various computer- readable media (CRM) blocks 583, 585, 587, 589 and 591 (e.g., non-transitory media that are not carrier waves and that are not signals). Such blocks generally include instructions suitable for execution by one or more processors (or cores) to instruct a computing device or system to perform one or more actions. While various blocks are shown, a single medium may be configured with instructions to allow for, at least in part, performance of various actions of the method 580. A computer-readable storage medium is non-transitory, not a carrier wave and not a signal. As an example, one or more CRM blocks may be provided for graphical user interfaces (GUIs), etc.

[00126] As an example, a framework can be part of a cognitive advisory system. For example, consider a cognitive advisory system (CAS) for prediction, optimization, and/or control of hydraulic fracturing (HF). As an example, a CAS may utilize simulation data and/or real field data. As an example, a CAS may be a self-learning computer system that can runs in one or more modes such as, for example, an online machine learning (e.g., optionally a slow background mode) and an operational mode (e.g., optionally a fast optimization/prediction/control mode). As an example, a CAS may periodically and/or continuously improve expertize (e.g., in a machine learning mode), which can act to improve accuracy (e.g., of an operational mode). As an example, a CAS may periodically and/or continuously renew one or more databases, for example, by accounting for various impacts on one or more reservoirs linked with one or more applications (e.g., of different technologies), which may be associated with one or more reservoir wells (e.g., consider fracturing, well intervention, artificial lift systems deployment, intelligent completions deployment, etc.). As an example, a CAS may receive non-technology information relevant to a reservoir or reservoirs, which may be analyzed and maintained in as internal knowledge and/or as one or more information database (e.g., market, economics, local specific knowledge, climate, etc.). As an example, a CAS may allow for generation of short, medium and/or long term recommendations on production optimization based on one or more of comparative analysis of reservoir development scenario and immediate smart data mining within historical reservoir performance. As an example, a CAS may allow for generation of recommendations for control actions to prevent hazardous situations, mitigate risks and/or to provide planned production volume and rates.

[00127] As an example, a CAS may be implemented for prediction, optimization, and/or control of performance of an oil and gas reservoir. In such an example, the CAS may discover knowledge through interactions with various kinds of input data, and improve its prediction accuracy.

[00128] As an example, a workflow or workflows may include various interactions such as, for example, interactions between one or more of machine learning component(s), integrated data-driven and physics-driven modeling tools, an optimization engine, and an interactive knowledge database. Such features may be components of a framework such as, for example, a CAS framework.

[00129] Where a CAS operates particularly for hydraulic fracturing, it may be referred to as a Cognitive Fracturing System (CFS). As an example, a CFS can output information, which may be information that can be considered "new knowledge", as generated through interactions with various kinds of input data, through collection of data into cloud storage and through continuous runs of multiple data analytics engines aimed at continuous improvement of its prediction accuracy.

[00130] As an example, a CFS can be implemented for performing one or more workflows and/or portions of one or more workflows. As an example, a CFS can include various interacting components. As an example, a CFS can include one or more machine learning algorithms that execute via one or more processing cores, can integrated data-driven and physics-driven modeling tools that can execute via one or more processing cores, can utilize one or more optimization engines (e.g., operating via search, objective function(s), etc.), can include interfaces that can operatively couple to one or more databases (e.g., for interactive access to, storage of, interaction with data).

[00131] Fig. 6 shows an example of a system 600 that includes an input component 610, a database component 620, an algorithm component 630, a tool component 640, a predictor component 660 and an output component 680. In the example system 600, various examples of links are illustrated, which may include, for example, a data exchange and/or triggering link, a simulation link, a learning and/or training link and a feedback link.

[00132] Fig. 6 also shows an example of a method 690 where an input block 691 can include receiving information, a prediction block 696 can include predicting one or more conditions and an output block 698 can include outputting information based at least in part on at least one predicted condition.

[00133] As an example, a CAS, which may be a CFS, can include the database component 620, the algorithm component 630 and the predictor component 660, which may be components of a framework (e.g., a CAS framework). In such an example, the CAS can receive information as input and can transmit information as output. As an example, such a CAS may be utilized to implement at least a portion of the method 690 of Fig. 6 (e.g., for predicting one or more conditions).

[00134] The illustrated example system 600 of Fig. 6 includes various links that can represent a high-level workflow of the system 600, for example, for prediction optimization, and/or control of performance of an oil and gas reservoir. As an example, the system 600 may include features to adjust a reservoir model, which may include design, analysis, performance, operation, etc. of one or more stimulation treatments that can be applied to a reservoir.

[00135] As an example, a system can receive a block of input data (e.g., which may include one or more of a reservoir description, streaming data from one or more sensors, production data, technology description, human perception of technology efficiency at a particular well/reservoir/geology, market data, environmental data, human-defined scenario for DFP etc.). In such an example, the input data may be stored in a smart database. As an example, a smart database can be used for training one or more machine learning algorithms that can provide for outputting various predictions tools for modeling different field (reservoir) characteristics. As an example, a set of prediction tools may include software for production prediction, FDP optimization, production control, and reservoir model adjustment. As an example, a smart database may be periodically and/or continuously updated by knowledge generated by a system. As an example, output of a system may be generated as one or several types of output such as, for example, one or more of optimal scenario(s) for field development with uncertainty estimation, FDP at a pre-defined condition and/or conditions, immediate advice on short term optimization of a production process, an adjusted reservoir model, etc.

[00136] As an example, a scenario can include fluids debit forecasting with regard to a plan of technological operations to be applied for a field. In such an example, consider one or more types of plans such as, for example, a water-flooding plan, a completions deployment plan, a plan of artificial lift system(s) implementation, a plan of well stimulation, an enhanced oil recovery (EOR) implementation plan, an optimal chocking sequence plan, etc.

[00137] As an example, at a learning stage, a system may communicate with one or more reservoir modeling and/or optimization tools. As an example, after sufficient learning, a system may implement various workflows that may occur optionally without additional learning.

[00138] As an example, a CAS can include one or more artificial intelligence components, which may be, for example, prediction components. As an example, a CAS can include a set of machine learning and prompt prediction tools aimed at different aspects of forward and inverse modeling of various processes related to well and reservoir performance where, for example, a smart database may provide for storing knowledge and data relevant to a reservoir or reservoirs. As an example, where a CAS is directed to a reservoir, it may be referred to as a cognitive reservoir system (CRS). As mentioned, where a CAS is directed to fracturing, it may be referred to as a cognitive fracturing system (CFS). As an example, a workflow may implement a CRS and a CFS using, at least in part, instances of various components a common framework. An instance may be an instantiated component that executes using one or more processing cores, which may provide for execution of operating system instructions, virtual machine instructions, etc.

[00139] As to the database component 620, as an example, access may be at least in part via a database management platform. As an example, a STUDIO™ framework may be utilized such as, for example, features of the STUDIO™ FIND search framework. Such a framework may be operatively coupled to a search engine that can provide for searching one or more data stores (e.g., databases, etc.). As an example, the STUDIO E&P™ knowledge environment (Schlumberger Ltd., Houston, Texas) includes STUDIO FIND™ search functionality, which provides a search engine. The STUDIO FIND™ search functionality also provides for indexing content, for example, to create one or more indexes. As an example, search functionality may provide for access to public content, private content or both, which may exist in one or more databases, for example, optionally distributed and accessible via an intranet, the Internet or one or more other networks. As an example, a search engine may be configured to apply one or more filters from a set or sets of filters, for example, to enable users to filter out data that may not be of interest.

[00140] As an example, one or more learning algorithms described in Mohri et al. (Foundations of Machine Learning, The MIT Press ISBN 9780262018258) may be utilized in a machine learning component or components (e.g., in blocks 630 and/or 660). As an example, an optimization component (e.g., as implemented in the PETREL® framework, MEPO™ framework (Schlumberger Limited, Houston Texas), etc.) may be utilized to enhance performance of prompt optimization and, for example, help to avoiding a huge amount of playable scenarios while forming synthetic training sets (TSs).

[00141] As an example, one or more components of the system 600 may be implemented in a cloud environment. For example, the algorithm component 630 and the predictor component 660 may be components of a framework (e.g., CAS, CRS, CFS, etc.) that operate using computing, communication and data storage resources of a cloud environment, which may be structured via a cloud architecture.

[00142] Fig. 7 shows an example of a cloud architecture 700, which corresponds to the AZURE™ platform architecture (Microsoft Corporation, Redmond, Washington). As shown, the architecture 700 includes a client layer 710, an integration layer 720, an application layer 740 and a data layer 740. The client layer 710 can include features for one or more types of computing device, which may be information handling devices (e.g., desktop computers, workstations, smartphones, tablets, notebook computers, etc.). The integration layer 720 can provide logistics as to Web-based connections and communications with the client layer 710 and with the application layer 730 and/or the data layer 740. As shown, the integration layer 720 can include a content delivery network (CDN), a traffic manager, data synchronization services for servicing operations with respect to one or more databases, etc. [00143] As shown in the example of Fig. 7, the application layer 730 can include media services and compute resources, which can include Web role, worker role and virtual machine (VM) role compute resources, which can be operatively coupled to the data layer 740. As an example, the application layer 730 can include HADOOP™ services (Apache Software Foundation, Forest Hills, Maryland), which are provided via a framework that can facilitate distributed storage and processing of large data sets, for example, via one or more computer clusters. Such services may handle hardware failures occurrences in an automated manner to help assure availability of data, etc.

[00144] As shown in Fig. 7, the data layer 740 can include various data storage features (e.g., drives, blobs, tables, queues, etc.), caching features and database access features (e.g., SQL, etc.).

[00145] As an example, a cloud computing platform can be utilized to implement a cloud-based system. For example, consider the AZURE™ platform (Microsoft Corporation, Redmond, Washington), which is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of data centers.

[00146] A cloud computing platform can offer, for example, virtual machines, infrastructure as a service (IaaS) that provide for launch of virtual machines and/or preconfigured machine images, App services, a platform as a service (PaaS) environment (e.g., to publish and/or manage Web sites), Websites, high density hosting of websites (e.g., optionally using one or more of ASP.NET, PHP, Node.js, Python, etc.), etc. As an example, a cloud-based system may utilize Websites in PHP, ASP.NET, Node.js, Python, or one or more other languages. As an example, a cloud computing platform may offer WebJobs as applications that can be deployed to a Web App to implement background processing. Such an approach may be invoked on a schedule, on-demand and/or run continuously. As an example, a cloud computing platform may offer blob (data storage/structure), table and queue services, which may be utilized to communicate between Web Apps and WebJobs and, for example, to provide state information.

[00147] A cloud computing platform can provide one or more of SaaS, PaaS and IaaS services and, for example, supports different programming languages, tools and frameworks.

[00148] As mentioned, cloud services can dynamically scale, for example, to meet demands of users. Provisioning may be automated in a cloud environment where a cloud infrastructure provider supplies hardware and software. [00149] As an example, a cloud environment can provide an "Internet of Things" (IoT) hub. For example, an IoT hub can provide for adding devices, connecting to existing devices, using device SDKs for multiple platforms, including LINUX® OS, WINDOWS® OS, and real-time operating systems (RTOSs). As an example, an IoT hub can scale from just a few devices (e.g., sensors, etc.) to hundreds of simultaneously connected devices (e.g., sensors, etc.) with distributed availability of the cloud.

[00150] As an example, a device can be a sensor device, a control device, or other device that may include an embedded microcontroller with an operating system (e.g., a RTOS, etc.). As an example, a device can include communication circuitry that allows for communication via one or more protocols. For example, consider BLUETOOTH® communication circuitry that communicates via a BLUETOOTH® protocol, WiFi communication circuitry that communicates via an Internet protocol (IP), GSM communication circuitry, etc.

[00151] As an example, a field site may be instrumented with various types of devices that include communication circuitry that allows for access via a network or networks that include or operatively coupled to the Web. As an example, a field site may be a seismic survey field site, a rigsite, a hydraulic fracturing site, etc. As an example, a rigsite can be a wellsite where a well exists, as may be drilled according to a well plan. For example, a well plan can specify a well trajectory and optionally completion specifications. As an example, a well plan may specify a treatment such as a stimulation treatment. Various types of equipment can be present at a rigsite, which may be a wellsite, where such equipment can be control and/or sensor equipment that can form part of an IoT infrastructure at the site.

[00152] Fig. 8 shows an example of a system 800 that includes an input portion 810, a database portion 820, an algorithms portion 830, a predictor portion 860 and an output portion 880. The system 800 can be a CAS that is configured as a CFS. As an example, various portions of the system 800 may be implemented in a cloud environment utilizing resources structured according to a cloud architecture.

[00153] As an example, the system 800 can be a cognitive advisory system for prediction of hydraulic fracture performance, optimization of hydraulic fracture design, control of one or more fracturing jobs and evaluation of near wellbore formation(s).

[00154] In the example of Fig. 8, the input portion 810 includes technology application history as a type of historical information (e.g., historical data), wellbore (WB) data as a type of initial information, raw near-wellbore data as a type of initial information, processed near- wellbore data as a type of initial information, specification information as to available technologies for performing one or more field operations (e.g., proppant technologies, fluid technologies, pump technologies, etc.), a pre- and post-job data as a type of data (e.g., cleanup and flowback data, data as to fracturing records, etc.), and fracturing job data that can be real-time (e.g., live) data from one or more pieces of equipment that are at a field site where hydraulic fracturing or one or more associated field operations are being performed, have been performed and/or are to be performed.

[00155] As an example, wellbore data may include production history, pressures, temperatures, trajectories, geometries (e.g., of one or more trajectories), casing specifications, completions specifications, workover history as to one or more portions of a wellbore, etc. As an example, raw near-wellbore data may include logs (e.g., log data), core data (e.g., from extracted cores, synthetic cores via modeling, constructed cores from material(s), etc.), petrological data, well test data, microseismic data (e.g., microseismic records), etc. As an example, processed near-wellbore data can include formation productivity index, porosity, permeability, net pay, GM properties, formation fluids information, etc.

[00156] As an example, one or more of wellbore data, raw near-wellbore data and processed near-wellbore data may be provided via a framework such as, for example, the TECHLOG® framework.

[00157] As shown in the example of Fig. 8, the input portion 810 can be operatively coupled to the database portion 820, which can include, for example: a data analyzer as part of a cognitive system; and a knowledge and information data storage as part of the cognitive system that is operatively coupled to the data analyzer.

[00158] As shown in the example of Fig. 8, the database portion 820 of the system 800 can be operatively coupled to the algorithms portion 830 of the system 800. As shown, the algorithms portion 830 can include one or more machine learning performance prediction algorithms, one or more machine learning job design advising algorithms, one or more job control and failure prevention algorithms (e.g., one or more risk assessment and/or risk management algorithms), and one or more machine learning inversion algorithms (e.g., for inversion of information, whether real-data, synthetic data or a combination thereof). As indicated in Fig. 8, the machine learning components may be part of a cognitive system (e.g., a CAS, a CFS, etc.). [00159] As mentioned, a system can include or be operatively coupled to one or more frameworks. For example, the system 800 includes a hydraulic fracturing (HF) design tool or framework and an optimization engine or framework. As an example, the system 800 may include or be operatively coupled to one or more frameworks such as, for example, the MANGROVE® framework and/or the FRACCADE® framework.

[00160] In the example of Fig. 8, small double headed arrows represent some examples of data exchange and/or triggering links. As shown, the machine learning performance prediction algorithms may be operatively coupled to the HF design tool or framework and the machine learning job design advising algorithms may be operatively coupled to the optimization engine or framework. As mentioned, the system 800 may optionally be, at least in part, implemented in a cloud environment, which may include the machine learning algorithms and frameworks such as the MANGROVE® framework and/or the FRACCADE® framework. In such an example, a workflow may utilize cloud resources in a cloud architecture to instantiate and/or scale instances of one or more frameworks and/or one or more machine learning algorithms.

[00161] As an example, a cloud environment may scale and/or provision resources in a data-driven manner. For example, where input and/or database information is received by the algorithms portion 830 of the system 800, the amount and/or rate of data received may trigger scaling and/or provisioning of resources for execution of algorithms, frameworks, etc. In such an example, a user or users concerned with a particular field operation or operations may be assured of timings. For example, where a piece of equipment comes on-line as part of a fracturing job, data that is generated and transmitted by that equipment may trigger a cloud hosting platform to instantiate, scale, provision, etc. to accommodate workflow tasks such that output of the output portion 880 is provided in a timely manner (e.g., optionally real-time or near real-time). In such an example, where output of the output portion 880 aims to control and/or adjust an ongoing field operation, the timeliness of output may be assured via commanding an appropriate amount of resources in the cloud.

[00162] The system 800 may be data-driven where demands are assessed based on data received, data expected to be generated and received, equipment to be controlled, etc. As an example, data may be a "gas pedal" for a cloud environment where a cloud hosting platform responds to data flows to achieve a desired rate of output, which, as mentioned, may be utilized to control one or more pieces of equipment of an ongoing field operation. As an example, where data are received from multiple sources, a rate of output may be limited by one of the sources. As an example, a system such as a CFS may optionally "fill-in" data from a rate limiting source using synthetic data. For example, where a rate of output is desired that would otherwise be limited by a rate of input, a CFS may implement one or more simulators that can generate synthetic data, which may be combined with other data from other sources to achieve a desired rate of output. In such an example, cloud resources to perform the simulation or simulations to generate synthetic data may spike over intervals between times of receipt of real data. As an example, synthetic data may be generated in a relatively continuous manner and, when real data are received, comparisons may be made. In such an example, a simulator or simulators may be updated to more closely model dynamic behavior of physical phenomena in one or more field operations.

[00163] Referring again to Fig. 8, the predictor portion 860 can receive information via learning and/or training links from various components of the algorithms portion 830. As shown, various components of the predictor portion 860 can be part of a cognitive system (e.g., a CAS, a CFS, etc.). As shown, a prompt hydraulic fracturing (HF) performance prediction component can receive information from a machine learning performance prediction component and can be operatively coupled for data exchange and/or triggering via one or more other components. Such other components may include an HF job design optimization component, a streaming control and online failure prediction component (e.g., for risk assessment, risk management, etc.), a near wellbore data adjustment component, etc. In such examples, a prediction may be a real-time or near real-time prediction that can account for one or more factors that may be based on one or more other predictions (e.g., operation of one or more other prediction components), which may be based on one or more results from one or more trained machine learning algorithms (e.g., operation of one or more machine learning algorithm components). As an example, the predictor portion 860 may operate in an online and/or an offline mode. As an example, a mixed online and offline mode may be implemented where one or more of the predictor components are offline mode operated and where one or more of the predictor components are online mode operated.

[00164] As an example, the predictor portion 860 can include one or more adjustable predictor models that can operate based at least in part on one or more results of one or more trained machine learning algorithms and/or one or more outcomes of another model, which may be an adjustable predictor model. In the system 800, adjustability of one or more predictor models can allow for adjustments to accommodate specifics of a job, a site, an operator, equipment, etc. For example, trained machine learning algorithms may be fixed or otherwise slowly adjustable via training, which can include training based on relatively large datasets. In such an example, the trained machine learning algorithms may be fixed or adjustable over a relatively long period of time compared to a period of time associated with performance of one or more types of field operations. In such an example, adaptability and/or adjustability may be implemented in the predictor portion 860 of the system 800 for a job, which may allow for realtime or near real-time control of one or more pieces of equipment associated with the job.

[00165] As an example, information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860. As an example, a trained machine learning algorithm may be trained using data from a number of past jobs while results from such an algorithm are utilized in a predictor model that is specific to a job to be performed (e.g., offline mode) and/or specific to a job that is being performed (e.g., online mode). In such an approach, the system 800 can be cognitive as to generalizations of the past (e.g., via trained machine learning algorithms) and be cognitive as to specifics of the present (e.g., via adjustable predictor models).

[00166] In the example of Fig. 8, the HF job design optimization component may receive information via learning and/or training links from the machine learning job design advisor component, the streaming control and online failure prediction component may receive information via learning and/or training links from the machine learning job control and failure prediction component, and the near wellbore data adjustment component may receive information via learning and/or training links from the machine learning inversion component. As to inversion, information may be inverted to provide one or more wellbore property estimates, which may be considered to be wellbore data (e.g., near wellbore data). As an example, the machine learning inversion component and/or the near wellbore data adjustment component may be operatively coupled to a framework such as, for example, the TECHLOG® framework, which can handle near wellbore data and analysis thereof.

[00167] In the example of Fig. 8, the output portion 880 of the system 800 includes various blocks or components as to outcomes that are based at least in part on cognition, which can be machine learning algorithm-based cognition. In the example of Fig. 8, the outcomes of the output portion 880 can receive information from the prediction portion 860 via one or more simulation links (e.g., information based at least in part on data generated at least in part via one or more simulators and/or models). As shown, one or more of the outcomes may be utilized as feedback, for example, feedback to the data analyzer component of the database portion 820 of the system 800.

[00168] In the example of Fig. 8, a fluids debit change and decline index outcome can be based at least in part on output of the prompt HF performance prediction component, an optimal HF job design and job failure prediction outcome can be based at least in part on output of the HF job design optimization component, an immediate action advice outcome can be based at least in part on output of the streaming control and online failure prediction component, an adjusted near wellbore data outcome can be based at least in part on output of the near wellbore data adjuster component.

[00169] In Fig. 8, the system 800 may be operated to provide one or more of the outcomes or, for example, one or more other outcomes, which may be one or more intermediate results of the system 800.

[00170] In Fig. 8, the system 800 includes various arrows that may define one or more workflows or portions of workflows. Arrows shown in Fig. 8 include data exchange and triggering arrows, simulation arrows, learning/training arrows and feedback arrows. As to the output portion 880, it can be operatively coupled to a client layer such as the client layer 710 of Fig. 7 such that one or more outcomes can be transmitted to one or more client devices, which may include one or more pieces of equipment at a field site that can be implemented to perform one or more operations at the field site (e.g., one or more stimulation operations, etc.).

[00171] Fig. 8 also shows a legend with various symbols that are shown in various blocks or components. For example, a black-filled box represents cognitive system components, an open circle represents outcome components, an open box represents initial information components, a black-filled star represents technical specification components, a black-filled triangle represents real-time data components, an open triangle represents other or additional data components, and an open star represents tools, which may be frameworks.

[00172] As an example, a CFS scheme can include receiving various types of data as input. For example, consider input of one or more of the following: technology application history, wellbore data, raw near-wellbore data, processed near-wellbore data, specifications of available technologies, pre- and post-job data, job data (e.g., optionally real-time or near realtime), economic data (e.g., for fracturing treatment), field production data, etc. [00173] As an example, technology application history can be information about application of various types of HF technologies to various types of wells and various types of reservoirs. Such information may include fracturing job specifics, impact of a job (e.g., change in fluids production), pre- and post-job data like mini-fracturing and flowback recordings.

[00174] As an example, wellbore data can be information that helps to describe specifics of a well, which may be a candidate well (e.g., a candidate for HF technology to be applied). Wellbore data may include well production history, available pressure and temperature recordings at different parts of the well, trajectory of the well, and diameters or its sections, casing specifics, completions specifics, workover history, etc. Wellbore data may include information as to one or more offset wellbores.

[00175] As an example, raw near- wellbore data can be information about reservoir properties including, for example, data from logs, core data, petrography information, well test data, reservoir fluids data (e.g., fluid PVT properties, rheology, IFTs) and microseismic records as may be acquired during one or more portions of a fracturing job.

[00176] As an example, processed near-wellbore data can be information that escribes formation properties such as, for example, productivity index, porosity and permeability, net pay, geotechnical properties, formation fluids (e.g., relative saturations, chemistry), etc.

[00177] As an example, specifications as to available technologies can be information that describes types of equipment and/or materials that may be available for a particular job at a particular well, for example, consider pumps, fluids and chemistry, proppants, etc.

[00178] As an example, pre- and post-job data may include mini-fracturing records, cleanup and flowback data. As an example, fracturing job data can be measured data, as may be measured by one or more sensors, meters, etc. Fracturing job data can include a collection of sensor records that may be generated in real-time or near real-time during a fracturing job or jobs (e.g., during one or more field operations).

[00179] As an example, a scheme can include a smart database. As mentioned with respect to the system 800 of Fig. 8, input information blocks may be operatively coupled to communicate with a knowledge and information data storage through data analyzer. A smart database may be a storage of data from a variety of available sources (e.g., real data, modeling results and "lessons learned" from technology application or actions taken at HF job, etc.).

[00180] As an example, a data analyzer (e.g., data analyzer component) can implement one or more artificial intelligence algorithms (AIAs) as one or more tools for one or more data related tasks. For example, consider data cleaning, filtering, structuring data, estimating data quality and value, etc. A knowledge and information data storage may transmit data to one or more different machine learning blocks. For example, a machine learning block may poll for available data such that a data storage (e.g., server-based data storage) can receive a poll to determine whether such data is available. In such an example, the poll may include information as to a format for data where the data storage (e.g., server-based data storage) may format available data in a suitable manner for the machine learning block.

[00181] As an example, a method may include data polling. As an example, data may be stored in a data storage that can implement a Structured Query Language (SQL), which may be used for managing data held in a relational database management system (RDBMS), for stream processing in a relational data stream management system (RDSMS), etc. As an example, a method can include polling where polling may be linked to resource management and execution of instructions in a cloud environment. As mentioned, data availability may be associated with a metric or metrics (e.g., rate, amount, timings, etc.) that can be utilized to manage resources in a cloud environment.

[00182] As mentioned, a system can include a machine learning portion. As an example, a system can include one or more machine learning blocks (e.g., components) such as one or more of a performance prediction block, a job design advisor block, a job control and failure prevention block, and an inversion block. As an example, a learning tool, which may be a machine learning block or component, can be associated with a corresponding training set (TS), which may be, for example, accessible from one or more data storages. In such an example, one or more TSs may be used for building one or more corresponding prediction models. As an example, a TS might be subdivided on historical data including initial and live information from a "smart" data set and on synthetic data as may be obtained from modeling. In the latter example, learning blocks may trigger an HF design tool (e.g., framework) and/or an optimization tool (e.g., framework).

[00183] As an example, machine learning based performance prediction may be built on one or more common data mining approaches that work with definite types of training data such as one or more of the following types: technology application history; wellbore data; near wellbore data (one or both raw or processed); specifications of available technologies; pre- and post-job data; and data generated by HF design tool (e.g., HF framework). [00184] As an example, a machine learning based design advisor may be built on one or more common data mining approaches that work with definite types of training data such as one or more of the following types: technology application history; wellbore data; near wellbore data (one or both raw or processed); specification of available technologies; pre- and post-job data; and data generated by an optimization engine (e.g., optimization framework, etc.).

[00185] As an example, a machine learning job control and failure prevention block can be built on one or more Al-based decision making approaches and may utilizes one or a combination of the following items: pre-job data; job data; technology application history; engineering, economical, technical and operational limits.

[00186] As an example, a machine learning inversion block can be a type of interpretation component that uses elements of inverse problem approaches and data mining possibilities. As an example, a machine learning inversion block can utilizes one or a combination of the following items: processed near wellbore data; pre- and post-job data; fracturing job data; and technology application history.

[00187] As an example, a prediction section or portion of a system can include modeling tools that can provide for one or more of prompt HF performance prediction, HF design optimization (e.g., including advisory on new one or more datafrac designs), streaming control and failure prediction of HF job, and adjustment of near-wellbore formation data (e.g., including geomechanical values). Such blocks may be tools that can be updated (e.g., periodically or continuously) with corresponding information (e.g., new from a site, new from an analysis, new from an outside data source, etc.) and that can communicate with one another (see, e.g., Fig. 8) to produce output.

[00188] As an example, an output section or portion of a system can include features for receiving and transmitting outcomes for modelling in a prediction section or portion of the system. For example, consider outcomes such as change of fluids inflow and decline index after HF job with pre-defined parameters (HF job design); optimal HF job design (e.g., including advisory on new datafrac designs) and production failure probability estimate at particular well; immediate action advice for helping a fracturing operations engineer and/or equipment to adjust job parameters on-the-fly when something goes wrong or to optimize an operation; adjusted near-wellbore data (e.g., via a GM model and other near-wellbore properties); and impact on economics. [00189] As an example, the system 800 of Fig. 8 may implement one or more types of artificial intelligence techniques or technologies. Such an approach can involve use of one or more of hybrid intelligent systems, decision making, mining association rules, decision tree learning, online learning, and inductive learning.

[00190] As an example, machine learning blocks can include algorithms that may be amenable to supervised and/or unsupervised learning. For example, consider use of one or more of hierarchical clustering, neural networks, dimensionality reduction, support vector machines, evolutionary programming, genetic algorithms, regression and correlation analysis, case based reasoning, association rules mining, combining multiple learners, reinforcement learning, Bayesian estimation, visualization techniques, etc.

[00191] Fig. 9 shows an example of a system 900 that includes an input portion 910, a database portion 920, an algorithms portion 930, a predictor portion 960 and an output portion 980. In comparison with the system 800, the system 900 has fewer inputs in the input portion 910. In particular, the system 900 can operate with technology application history information, wellbore data, raw near-wellbore data, specifications of available technologies, and fracturing job data.

[00192] Fig. 10 shows an example of a system 1000 that includes an input portion 1010, a database portion 1020, an algorithms portion 1030, a predictor portion 1060 and an output portion 1080. In comparison with the system 800, the system 1000 has fewer inputs in the input portion 1010. In particular, the system 1000 can operate with technology application history information, wellbore data, processed near-wellbore data, specifications of available technologies, and fracturing job data.

[00193] Fig. 1 1 shows an example of a system 1 100 that includes an input portion 11 10, a database portion 1 120, an algorithms portion 1130, a predictor portion 1 160 and an output portion 1180. In comparison with the system 800, the system 1100 has fewer inputs in the input portion 1110 and without various hydraulic fracturing control capabilities. In particular, the system 1100 can operate with technology application history information, wellbore data, and pre- and post-fracturing job data.

[00194] As shown in the system 1 100 of Fig. 11 , in comparison to the system of Fig. 8, various components may be deactivated and/or not instantiated. As an example, such an approach may be based on one or more factors, which may be equipment related, job related, computing resource related, etc. As an example, where equipment at a site does not provide a particular type of data, which may be utilized in an algorithm, the algorithm may be disabled and a cloud environment may refrain from generating associated instances of objects, frameworks, etc. As an example, where equipment is delivered to a field operation (e.g., or activated, repaired, etc.), which may supply the type of data, a workflow can include enabling one or more components via provisioning of appropriate resources in a cloud environment. In such an example, the system 800 of Fig. 8 may be dynamically managed. For example, the system 800 may be tailored on-the-fly and optionally automatically responsive to types of information that may become available, stage of an operation, etc.

[00195] Fig. 12 shows an example of a system 1200 that includes an input portion 1210, a database portion 1220, an algorithms portion 1230, a predictor portion 1260 and an output portion 1280. In comparison with the system 800, the system 1200 has fewer inputs in the input portion 1210 and without various optimization capabilities. In particular, the system 1200 can operate with technology application history information, pre- and post-fracturing job data and fracturing job data.

[00196] As shown in the system 1200 of Fig. 12, in comparison to the system of Fig. 8, various components may be deactivated and/or not instantiated. As an example, such an approach may be based on one or more factors, which may be equipment related, job related, computing resource related, etc. As an example, where equipment at a site does not provide a particular type of data, which may be utilized in an algorithm, the algorithm may be disabled and a cloud environment may refrain from generating associated instances of objects, frameworks, etc. As an example, where equipment is delivered to a field operation (e.g., or activated, repaired, etc.), which may supply the type of data, a workflow can include enabling one or more components via provisioning of appropriate resources in a cloud environment. In such an example, the system 800 of Fig. 8 may be dynamically managed. For example, the system 800 may be tailored on-the-fly and optionally automatically responsive to types of information that may become available, stage of an operation, etc.

[00197] In the example of Fig. 12, the system 1200 can operate without hydraulic fracturing design optimization, for example, as in the predictor portion 860 of the system 800, which can output an optimized hydraulic fracturing design and/or associated risk management information. As an example, where an ongoing hydraulic fracturing operation experiences an issue, which may be an unexpected issue, the design of the operation may come into question. In such an example, the system 1200 may be instructed to activate one or more components that can allow for design optimization, re-design, risk assessment, etc. As an example, where an immediate action notice or advice is an output that may not result in optimal operation if implemented, a user may instruct a system to activate one or more components that can perform optimization under the circumstances that gave rise to the particular immediate action notice or advice. In such an example, a framework may be instantiated such as an optimization framework that can execute using cloud-based resources (e.g., one or more processors, one or more virtual machines, etc.). As an example, where an optimum design is output, a system may optionally revert back to a prior state (e.g., configuration) of operation (e.g., disabling the optimization components and/or framework or otherwise shutting them down to release computing resources, etc.).

[00198] Fig. 13 shows an example of a system 1300 that includes an input portion 1310, a database portion 1320, an algorithms portion 1330, a predictor portion 1360 and an output portion 1380. In comparison with the system 800, the system 1300 has fewer inputs in the input portion 1310 and without various optimization capabilities. In particular, the system 1300 can operate with technology application history information and fracturing job data.

[00199] The system 1300 may be implemented for control of one or more fracturing operations where data from a site can be received by the system 1300 and where the system 1300 can transmit data to the site. Such data may be information for one or more operators, control instructions for equipment, etc. As an example, the system 1300 may be implemented using site-based resources and/or using remote resources. As an example, the system 1300 may be implemented at least in part using cloud-based resources. For example, a platform may be implemented in the "cloud" to manage resources to implement the system 1300 and optionally one or more features of the system 800 of Fig. 8 in an on-demand manner. As an example, an on-demand instruction may be generated automatically or manually. As an example, a system may operate and change according to a plan where features of the system come and go according to the plan and where, for example, a platform may manage computing resources to implement such features.

[00200] Fig. 14 shows an example plot 1410 that includes various data plotted versus time during a hydraulic fracturing operation. The plot 1410 may be rendered to a display as part of a graphical user interface, which may be, for example, a rendered in part via a Web browser application executing on a client device with a network interface that is operatively coupled to the Internet and, for example, to a cloud environment. As an example, such a client device may be part of or operatively coupled to a client layer such as the client layer 710 of the architecture 700 of Fig. 7. As an example, such a client device may be operatively coupled to a system such as the system 800 of Fig. 8 via one or more networks.

[00201] In the example plot 1410, data can include measured data and optionally synthetic data. For example, proppant concentration may be modeled via a simulation model, which may be a dynamic simulation model that can receive information from a site during a fracturing operation and generate synthetic data in real-time or near real-time (e.g., of the order of minutes) that can be integrated into one or more analyses of a system such as the system 800.

[00202] Specifically, the plot 1410 shows microseismic event rate and various fluid pumping parameters as part of a fracture stimulation job to demonstrate performance during treatment of a well in a shale formation. In the plot 1410, the pump rate, surface pressure and proppant concentration are shown. A system such as the system 800 may utilize such data to identify a time-dependent response of microseismic events to the stimulation. As an example, an increase in cumulative seismic moment may indicate that deformation increased at a point in time during a planned pumping schedule, which may, for example, be dynamically adjusted according to output from a system such as the system 800. A system such as a CFS may include data and algorithm that are machine learning algorithms that are trained based on data from multiple treatments at various sites, which may be, for example, sites for a common field (e.g., a common laterally expansive shale formation). A CFS may analyze microseismicity and respond to changes in microseismicity by outputting parameters that can directly or indirectly be utilized to control equipment at a site during an ongoing operation or operations. For example, pumping may be adjusted via control of one or more pump trucks.

[00203] The plot 1410 also shows a vertical line at a time of about 3:30 am, which occurs at approximately 3 hours into the hydraulic fracturing operation. As shown, a state "2" exists to the left side of the line (earlier times) and a state "3" exists to the right side of the line (later times). Such states can correspond to states of a system such as the system 800 of Fig. 8 where various features are activated and/or deactivated where, for example, a cloud platform may instantiate or de-instantiate various components as executable using cloud-based resources. In the example plot 1410, the state transition may be due to the drop in the treatment pressure or one or more other factors (see, e.g., decrease in pressure just prior to the transition from state "2" to state "3"). [00204] Fig. 14 also shows a diagram that includes a planning phase 1420, which may correspond to the system 800 of Fig. 8 executing in an offline mode with respect to equipment at a site, and that includes an operations phase 1440, which may correspond to the system 800 of Fig. 8 executing in an online mode with respect to equipment at the site. As an example, a portion of the operations phase 1440 can correspond to data such as data of the plot 1410.

[00205] As an example, a planning phase may optionally be in an online mode that may be prior to execution of a stimulation plan such as a hydraulic fracturing plan to generate hydraulic fractures that are part of a stimulation treatment. As mentioned, a minfrac procedure may be implemented onsite, a falloff procedure may be implemented onsite, an imaging procedure may be implemented onsite, a sonic scanning procedure may be implemented onsite, etc. One or more of such procedures may be performed where data acquired therefrom may be transmitted to a system such as the system 800 during a planning phase. As an example, a planning phase may generate information and/or requests for additional data acquisition from onsite equipment. For example, where a planning phase deems data insufficient to determine a plan or a portion of a plan, a system may generate a request (e.g., a control command, etc.) for additional data as may be acquired by onsite equipment. As an example, a planning phase may dynamically alter a system such as the system 800, optionally in a stage-by-stage manner, where output may include a system configuration for the system 800 to be implemented during an operational phase for one or more stages.

[00206] In the example of Fig. 14, the planning phase 1420 may optionally be implemented in an offline mode, in an online mode or in part in an offline mode and in part in an online mode. As an example, an online mode may be an online planning mode, which occurs prior to commencement of a plan. As an example, an online mode may be an online execution mode, which occurs during implementation of a plan.

[00207] In the example planning phase 1420, planning occurs for a four stage operation as output 1422, which is to be implemented during the operations phase 1440. As shown, stage 1 is performed using the system 800 in state "1", stage 2 is performed using the system in state "2" and in state "3", stage 3 is performed using the system in state "4" and in state "5" and stage 4 is performed using the system in state "6". At the end of stage 4, the system 800 as in state "6" may proceed to transmit information and/or otherwise store information to one or more databases, generate one or more reports, performing machine learning, etc. As an example, the operations of the operations phase 1440 may be archived. [00208] As an example, the system 800 can include a fingerprinting component that generates a fingerprint or fingerprints for the four stages of the operations. For example, one or more dynamic fingerprints and/or one or more static fingerprints may be generated. A fingerprint may be a graphical representation of a job, which may evolve over time. As an example, states of a system may be stored in graphical form such that a user may review such states to see when features were utilized or not utilized. As an example, the plot 1410 may be an example of a fingerprint. As an example, the graphics illustrated in the operations phase 1440 of Fig. 14 may be part of a fingerprint. As an example, a fingerprint or fingerprints can include information as to physical phenomena during an operation and information as to states of a system such as the system 800 during an operation (e.g., online mode states). In such an approach, a user may understand physical phenomena and tools that were utilized to control and/or manage equipment at a site that may have had an impact on the nature of or dynamics of the physical phenomena.

[00209] While Fig. 14 shows data that correspond to a shale formation, one or more other types of formations can be candidates for hydraulic fracture stimulation. For example, consider high permeability sandstone (e.g., greater than approximately 1 D) where fracturing and/and packing stimulation treatments may be applied and/or carbonate rocks that may benefit from acid fracturing and/or proppant fracturing where such formations may be relatively soft such as the chalk (e.g., North Sea) to hard dolomites. As an example, a formation may include low permeability metamorphic rock such as granite or gneiss, low perm -high porosity diatomites, coal beds, tight sandstone rock (e.g., approximately 0.01 mD to approximately 0.5 mD) to shale formations (e.g., less than approximately 0.01 mD).

[00210] As an example, a CFS can be a system that can plan and control an HF job for a selected well drilled through a specific formation based on the experience on multiple jobs at various wells having similar properties. Such a system can include components to generate prompt advice as to adjustments that can be made, automatically and/or manually, during a fracturing job (e.g., to avoid an operational failure, to optimize one or more parameters, etc.). A system can include one or more components that can check for the consistency of input, acquired and generated data, for example, to reduce risk of misleading predictions. As an example, a system can include various tools which can be used independently (prompt performance predictor, advisor, optimizer, adjuster, etc.). As an example, a system can be continuously updated with new live and physical modeling data. In such an example, the system can accumulate new knowledge (e.g., machine learning expertise) and may automatically refine prediction models.

[00211] As shown in the examples of Figs. 8 to 14, a system may be associated with workflows. For example, the system 800 can be dynamically configured to perform various workflows. As an example, one or more portions of the system 800 may be instantiated in a cloud environment and available to a plurality of sites, whether in online or offline modes.

[00212] As mentioned, a system can be scalable. As an example, a system may be implemented on a local server or workstation utilizing the data from boreholes of a particular configuration (e.g., borehole configurations that can be a set of wellbore and near-wellbore characteristics relevant to HF job). As an example, a system may be run for a reservoir that includes a plurality of wells to be fractured. In such an example, the reservoir can have an associated common smart data base and frameworks but different machine learning and prediction blocks for boreholes (e.g., wells) of each configurations present within the reservoir.

[00213] As an example, a system may be run for several reservoirs simultaneously having, for example, a common smart data base and frameworks but different machine learning and prediction blocks for each separate reservoir or borehole configurations.

[00214] As an example, a system may be run at least in part in a cloud environment and, for example, operatively coupled to a mobile app that executes on a mobile computing device, which may provide for receipt of immediate action advice and/or alerts, for monitoring new data inflows and corresponding changes in strategic plans within on optimal treatment scenario, etc.

[00215] Fig. 15 shows an example of a geologic environment 1500 as including various types of equipment and features. As shown, the geologic environment 1500 includes a plurality of wellsites 1502 operatively connected to a processing facility 1554. In the example of Fig. 15, individual wellsites 1502 can include equipment that can form individual wellbores 1536 (e.g., rigs, etc.). Such wellbores can extend through subterranean formations 1506 including one or more reservoirs 1504. Such reservoirs 1504 can include fluids, such as hydrocarbons. As an example, wellsites can draw fluid from one or more reservoirs and pass them to one or more processing facilities via one or more surface networks 1544. As an example, a surface network can include tubing and control mechanisms for controlling flow of fluids from a wellsite to a processing facility. [00216] Fig. 16 shows an example of portion of a geologic environment 1601 and an example of a larger portion of a geologic environment 1610. As shown, a geologic environment can include one or more reservoirs 1611-1 and 161 1-2, which may be faulted by faults 1612-1 and 1612-2. Fig. 16 also shows some examples of offshore equipment 1614 for oil and gas operations related to the reservoirs 161 1-1 and 1611-2 and onshore equipment 1616 for oil and gas operations related to the reservoir 1611-1. As an example, a system may be implemented for operations associated with one or more of such reservoirs.

[00217] As to the geologic environment 1601, Fig. 16 shows a schematic view where the geologic environment 1601 can include various types of equipment. As shown in Fig. 16, the environment 1601 can includes a wellsite 1602 and a fluid network 1644. The wellsite 1602 includes a wellbore 1606 extending into earth as completed and prepared for production of fluid from a reservoir 161 1.

[00218] In the example of Fig. 16, wellbore production equipment 1664 extends from a wellhead 1666 of the wellsite 1602 and to the reservoir 161 1 to draw fluid to the surface. As shown, the wellsite 1602 is operatively connected to the fluid network 1644 via a transport line 1661. As indicated by various arrows, fluid can flow from the reservoir 1611, through the wellbore 1606 and onto the fluid network 1644. Fluid can then flow from the fluid network 1644, for example, to one or more fluid processing facilities.

[00219] In the example of Fig. 16, sensors (S) are located, for example, to monitor various parameters during operations. The sensors (S) may measure, for example, pressure, temperature, flowrate, composition, and other parameters of the reservoir, wellbore, gathering network, process facilities and/or other portions of an operation. As an example, the sensors (S) may be operatively connected to a surface unit (e.g., to instruct the sensors to acquire data, to collect data from the sensors, etc.).

[00220] In the example of Fig. 16, a surface unit can include computer facilities, such as a memory device, a controller, one or more processors, and display unit (e.g., for managing data, visualizing results of an analysis, etc.). As an example, data may be collected in the memory device and processed by the processor(s) (e.g., for analysis, etc.). As an example, data may be collected from the sensors (S) and/or by one or more other sources. For example, data may be supplemented by historical data collected from other operations, user inputs, etc. As an example, analyzed data may be used to in a decision making process. [00221] As an example, a transceiver may be provided to allow communications between a surface unit and one or more pieces of equipment in the environment 1601. For example, a controller may be used to actuate mechanisms in the environment 1601 via the transceiver, optionally based on one or more decisions of a decision making process. In such a manner, equipment in the environment 1601 may be selectively adjusted based at least in part on collected data. Such adjustments may be made, for example, automatically based on computer protocol, manually by an operator or both. As an example, one or more well plans may be adjusted (e.g., to select optimum operating conditions, to avoid problems, etc.).

[00222] To facilitate data analyses, one or more simulators may be implemented (e.g., optionally via the surface unit or other unit, system, etc.). As an example, data fed into one or more simulators may be historical data, real time data or combinations thereof. As an example, simulation through one or more simulators may be repeated or adjusted based on the data received.

[00223] In the example of Fig. 16, simulators can include a reservoir simulator 1628, a wellbore simulator 1630, a surface network simulator 1632, a process simulator 1634 and an economics simulator 1636. As an example, the reservoir simulator 1628 may be configured to solve for hydrocarbon flow rate through a reservoir and into one or more wellbores. As an example, the wellbore simulator 1630 and surface network simulator 1632 may be configured to solve for hydrocarbon flow rate through a wellbore and a surface gathering network of pipelines. As to the process simulator 1634, it may be configured to model a processing plant where fluid containing hydrocarbons is separated into its constituent components (e.g., methane, ethane, propane, etc.), for example, and prepared for further distribution (e.g., transport via road, rail, pipe, etc.) and optionally sale. As an example, the economics simulator 1636 may be configured to model costs associated with at least part of an operation. For example, consider MERAK™ framework (Schlumberger Limited, Houston, Texas), which may provide for economic analyses.

[00224] As an example, a system can include and/or be operatively coupled to one or more of the simulators 1628, 1630, 1632, 1634 and 1636 of Fig. 16. As an example, such simulators may be associated with frameworks and/or may be considered tools. As an example, the system 800 of Fig. 8 may be operatively coupled to and/or include one or more of the simulators 1628, 1630, 1632, 1634 and 1636 of Fig. 16. As an example, a system may include one or more application programming interfaces (APIs), which may allow for monitoring the system, interacting with the system, transmitting information to the system, etc. As an example, a system such as the system 800 of Fig. 8 may receive an API call and, in response, return information according to an API specification. As an example, a system such as the system 800 of Fig. 8 may make one or more API calls and, in response, receive information. For example, where the system 800 is operatively coupled to field equipment (e.g., via one or more networks), the system 800 may make an API call or calls to one or more pieces of the field equipment, which, in response, may transmit information to the system 800 or, for example, take one or more actions (e.g., control actions, which may include one or more actions such as data acquisition, parameter adjustment, actuation, de-actuation, etc.).

[00225] As an example, a system such as the system 800 of Fig. 8 may issue one or more calls for provisioning of one or more resources, which may be cloud-based resources. As an example, a cloud architecture can include one or more API management tools. For example, consider the AZURE™ API management tool, which includes the following components: (a) an API gateway that is an endpoint that can accept API calls and routes them to backends; can verify API keys, J WT tokens, certificates, and other credentials; can enforce usage quotas and rate limits; can transform an API on-the-fly; can cache backend responses where set up; and can log call metadata for analytics purposes; (b) a publisher portal that is an administrative interface to set up an API program to, for example, define or import API schema, package APIs into products, set up policies like quotas or transformations on the APIs, get insights from analytics, and manage users; and (c) a developer portal that can serve as a Web presence for developers such that developers (e.g., as authorized) can access API documentation, test an API via an interactive console, create an account and subscribe to get API keys, and access analytics on their usage. As an example, an API management service can be utilized to create an API facade (e.g., an API layer, etc.) for a diverse set of devices and associated services. As an example, an API layer can include an API portal, which may provide documentation and samples, metering support, protection from abuse and overuse, monitoring, tracking, analytics, etc.

[00226] As an example, one or more pieces of equipment that may be site equipment may include instructions executable on a processor of the equipment that allows the equipment to generate and/or receive one or more API calls.

[00227] As an example, a framework may be run on a local server or, for example, a workstation utilizing information associated with a particular reservoir. For example, a workstation may be at a wellsite (e.g., in a driller cabin, etc.) and/or at another location that can receive information for the particular reservoir (e.g., one or more wells that can acquire data, inject fluid and/or produce fluid). As an example, a framework may be run in a cloud environment utilizing cloud-based resources.

[00228] As an example, a method can include receiving, via a network, data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site; accessing a database to retrieve information associated with the field site; executing, based at least in part on the data and the information, a trained machine learning algorithm using one or more processors to generate a result; and, based at least in part on the result, predicting an outcome for the hydraulic fracturing operation at the field site. In such an example, accessing, executing and predicting occur during the hydraulic fracturing operation at the field site.

[00229] As an example, a method can include outputting, via a network, an outcome. In such an example, the outcome can include a control instruction for one or more pieces of field equipment at a field site.

[00230] As an example, a method can include provisioning resources in a cloud environment based at least in part on receiving the data. In such an example, provisioning can include instantiating one or more components using provisioned resources for executing a trained machine learning algorithm.

[00231] As an example, a method can include executing a hydraulic fracturing simulation framework to generate simulation results and executing a trained machine learning algorithm based at least in part on the simulation results.

[00232] As an example, accessing a database can include accessing borehole data associated with a well at the field site where a hydraulic fracturing operation is performed via the well. In such an example, a method can include generating an outcome that includes adjusted borehole data.

[00233] As an example, a method can include utilizing a trained machine learning algorithm that is one of a plurality of different trained machine learning algorithms of a computing system. In such an example, the method can include selecting the trained machine learning algorithm based at least in part on data, based at least in part on the information or based at least in part on the data and the information. Such a method may include selecting two or more of the trained machine learning algorithms and predicting two or more corresponding outcomes. [00234] As an example, a method can include generating information for a graphical user interface where the information includes state information for a state of a computing system that includes a trained machine learning algorithm. In such an example, the method can include transmitting the information for the graphical user interface via a network.

[00235] As an example, a system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive, analyze and store information associated with hydraulic fracturing operations; select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations; execute the at least one of the plurality of trained machine learning algorithms; predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and output the at least one outcome for each of the at least one of the plurality of trained machine learning algorithms. In such an example, the system can include a plurality of processors associated with servers managed by a cloud hosting platform. In such an example, the system can include processor- executable instruction to instruct the system to provision one or more of the plurality of processors based at least in part on receipt of information.

[00236] As an example, a system can include processor-executable instructions to execute at least one of a plurality of trained machine learning algorithms in an offline mode with respect to field equipment at a field site for performing a hydraulic fracturing operation and/or a system can include processor-executable instructions to execute at least one of a plurality of trained machine learning algorithms in an online mode with respect to field equipment at a field site for performing a hydraulic fracturing operation.

[00237] As an example, one or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive, analyze and store information associated with hydraulic fracturing operations; select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations; execute the at least one of the plurality of trained machine learning algorithms; predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and output the at least one outcome for each of the at least one of the plurality of trained machine learning algorithms.

[00238] In some embodiments, a method or methods may be executed by a computing system. Fig. 17 shows an example of a system 1700 that can include one or more computing systems 1701-1, 1701-2, 1701-3 and 1701-4, which may be operatively coupled via one or more networks 1709, which may include wired and/or wireless networks.

[00239] As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of Fig. 17, the computer system 1701-1 can include one or more modules 1702, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.).

[00240] As an example, a module may be executed independently, or in coordination with, one or more processors 1704, which is (or are) operatively coupled to one or more storage media 1706 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1704 can be operatively coupled to at least one of one or more network interface 1707. In such an example, the computer system 1701-1 can transmit and/or receive information, for example, via the one or more networks 1709 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

[00241] As an example, the computer system 1701-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1701 -2, etc. A device may be located in a physical location that differs from that of the computer system 1701-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

[00242] As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[00243] As an example, the storage media 1706 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.

[00244] As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY® disks, or other types of optical storage, or other types of storage devices.

[00245] As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[00246] As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

[00247] As an example, a system may include a processing apparatus that may be or include a general purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.

[00248] Fig. 18 shows components of an example of a computing system 1800 and an example of a networked system 1810. The system 1800 includes one or more processors 1802, memory and/or storage components 1804, one or more input and/or output devices 1806 and a bus 1808. In an example embodiment, instructions may be stored in one or more computer- readable media (e.g., memory/storage components 1804). Such instructions may be read by one or more processors (e.g., the processor(s) 1802) via a communication bus (e.g., the bus 1808), which may be wired or wireless. The one or more processors may execute such instructions to implement (wholly or in part) one or more attributes (e.g., as part of a method). A user may view output from and interact with a process via an I/O device (e.g., the device 1806). In an example embodiment, a computer-readable medium may be a storage component such as a physical memory storage device, for example, a chip, a chip on a package, a memory card, etc. (e.g., a computer-readable storage medium).

[00249] In an example embodiment, components may be distributed, such as in the network system 1810. The network system 1810 includes components 1822-1 , 1822-2, 1822- 3, . . . 1822-N. For example, the components 1822-1 may include the processor(s) 1802 while the component(s) 1822-3 may include memory accessible by the processor(s) 1802. Further, the component(s) 1822-2 may include an I/O device for display and optionally interaction with a method. The network may be or include the Internet, an intranet, a cellular network, a satellite network, etc. [00250] As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH™, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.

[00251] As an example, a system may be a distributed environment, for example, a so- called "cloud" environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).

[00252] As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

[00253] Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus- function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. § 1 12, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words "means for" together with an associated function.

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6. Agrawal, R.; Imielinski, T.; Swami, A. (1993). "Mining association rules between sets of items in large databases". Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93. p. 207.

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Claims

1. A method for predicting a hydraulic fracture performance, the method comprising: receiving, via a network, data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site;

accessing a database to retrieve information associated with the field site;

executing, based at least in part on the data and the information, a trained machine learning algorithm using one or more processors to generate a result; and

based at least in part on the result, predicting an outcome for the hydraulic fracturing operation at the field site.

2. The method of claim 1 wherein accessing, executing, and predicting occur during the hydraulic fracturing operation at the field site.

3. The method of claim 1 comprising outputting, via a network, the outcome.

4. The method of claim 3 wherein the outcome comprises a control instruction for one or more pieces of the field equipment at the field site.

5. The method of claim 1 comprising provisioning resources in a cloud environment based at least in part on receiving the data.

6. The method of claim 5 wherein the provisioning comprises instantiating one or more components using provisioned resources for executing the trained machine learning algorithm.

7. The method of claim 1 comprising executing a hydraulic fracturing simulation framework to generate simulation results and executing the trained machine learning algorithm based at least in part on the simulation results.

8. The method of claim 1 wherein accessing a database comprises accessing borehole data associated with a well at the field site wherein the hydraulic fracturing operation is performed via the well.

9. The method of claim 8 wherein the outcome comprises adjusted borehole data.

10. The method of claim 1 wherein the trained machine learning algorithm comprises one of a plurality of different trained machine learning algorithms of a computing system.

1 1. The method of claim 10 comprising selecting the trained machine learning algorithm based at least in part on the data, based at least in part on the information or based at least in part on the data and the information.

12. The method of claim 10 comprising selecting two or more of the trained machine learning algorithms and predicting two or more corresponding outcomes.

13. The method of claim 1 comprising generating information for a graphical user interface wherein the information comprises state information for a state of a computing system that comprises the trained machine learning algorithm.

14. The method of claim 13 comprising transmitting the information for the graphical user interface via the network.

15. A cognitive system for predicting a hydraulic fracture performance, the system comprising:

a processor;

memory accessible to the processor;

processor-executable instructions stored in the memory and executable by the processor to instruct the system to:

receive, analyze and store information associated with hydraulic fracturing operations;

select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations;

execute the at least one of the plurality of trained machine learning algorithms; predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and

output the at least one outcome.

16. The system of claim 15 comprising a plurality of processors associated with servers managed by a cloud hosting platform.

17. The system of claim 16 comprising processor-executable instructions to instruct the system to provision one or more of the plurality of processors based at least in part on receipt of information.

18. The system of claim 15 comprising processor-executable instructions to instruct the system to execute the at least one of the plurality of trained machine learning algorithms in an offline mode with respect to field equipment at a field site for performing a hydraulic fracturing operation.

19. The system of claim 15 comprising processor-executable instructions to instruct the system to execute the at least one of the plurality of trained machine learning algorithms in an online mode with respect to field equipment at a field site for performing a hydraulic fracturing operation.

20. One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to:

receive, analyze and store information associated with hydraulic fracturing operations; select at least one of a plurality of trained machine learning algorithms associated with hydraulic fracturing operations;

execute the at least one of the plurality of trained machine learning algorithms;

predict at least one outcome for each of the at least one of the plurality of trained machine learning algorithms; and

output the at least one outcome for each of the at least one of the plurality of trained machine learning algorithms.