WO2017206159A1

WO2017206159A1 - Fracture network extraction by microseismic events clustering analysis

Info

Publication number: WO2017206159A1
Application number: PCT/CN2016/084636
Authority: WO
Inventors: Yun Ma; Shoichi NAKANISHI; Bing NIU; Xiaolin Zhang; Qingrui Li; Shaoyong SU; Bolei TAN; Guoping Zhang
Original assignee: Schlumberger Technology Corporation; Schlumberger Canada Limited; Services Petroliers Schlumberger; Geoquest Systems B.V.
Priority date: 2016-06-03
Filing date: 2016-06-03
Publication date: 2017-12-07

Abstract

Methods, computing systems, and computer-readable media for fracture network extraction are provided. Operations include acquiring a microseismic signal. The operations also include determining signal attributes by processing the microseismic signal. The operations also include selecting input attributes as inputs. The operations also include performing a clustering analysis based on the signal attributes and the input attributes. The operations also include extracting fracture planes from event clusters determined by the clustering analysis. The operations also include forming a fracture network by merging the fracture planes.

Description

FRACTURE NETWORK EXTRACTION BY MICROSEISMIC EVENTS CLUSTERING ANALYSIS

Background

Microseismic technology is the key way for hydraulic fracturing monitoring. Currently, microseismic process provides the event location rather than the fracture network. However, fracture geometry is an input for stimulation treatment design optimization, reservoir simulation and production prediction. Although there are already some tools to extract the fracture network from events, there are limitation in current tools, for example, limited event attributes used for fracture network extraction (e.g., x, y and z location are used in many fracture extraction methods) ； simple fracture plane (e.g., the method in current Mistral software only takes a rectangular plane to approximate fracture geometry； and high requirement on data (e.g., for example, the Moment Tensor Visualization (MTV) software needs moment tensor information for fracture extraction. In current commercial product, it requires at least two monitor wells to calculate microseismic moment tensor, which is very limited in the real monitoring jobs. )

Summary

Embodiments of the disclosure may provide a method, a computing system, and a non-transitory computer-readable medium for fracture network extraction. The method, the computing system, and the non-transitory computer-readable medium perform operations including acquiring a microseismic signal. The operations also include determining signal attributes by processing the microseismic signal. The operations also include selecting input attributes as inputs. The operations also include performing a clustering analysis based on the signal attributes and the input attributes. The operations also include extracting fracture planes from event clusters determined by the clustering analysis. The operations also include forming a fracture network by merging the fracture planes.

In an embodiment, the signal attributes may include one or more of an event time, an event location, and a microseismic attribute.

In an embodiment, selecting input attributes may include providing an attribute option list for fracture extraction.

In an embodiment, selecting input attributes may include using statistical analysis to select the input attributes.

In an embodiment, the operations may further include preprocessing the input attributes.

In an embodiment, preprocessing the input attributes may include scaling and weighting the input attributes.

In an embodiment, the operations may further include performing quality control on results of the clustering analysis.

In an embodiment extracting fracture planes may include extracting the fracture planes from each event cluster.

It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.

Brief Description of the Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

Figure 2 illustrates a flow chart diagram of a process for fracture network extraction, according to an embodiment.

Figure 3 illustrates a schematic view of a computing system, according to an embodiment.

Detailed Description

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a, ” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes, ” “including, ” “comprises” and/or “comprising, ” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting, ” depending on the context.

Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.

Systems and methods for clustering analysis on microseismic data in accordance with the present disclosure utilize the attributes of events, such as their location, time, azimuth, dip, D-value etc., to extract the complex fracture network through the clustering analysis. Using data mining based clustering method, complex fracture network can be extracted for every single microseismic monitoring job. With the MTI data input, the result can be even better. Also, this clustering analysis can be applied in both real-time and post-job cases with the similar technology workflow.

Figure 1 illustrates an example of a system 100 that includes various management components 110 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 153-1, one or more geobodies 153-2, etc. ) . For example, the management components 110 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 110) .

In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well/logging data) , a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the

components

112 and 114 may be input to the simulation component 120.

In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114) . 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) . Such properties may represent one or more measurements (e.g., acquired data) , calculations, etc.

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

framework (Redmond, Washington) , which provides a set of extensible object classes. In the

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 in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example of Figure 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. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116) . 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 relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial) . In the example of Figure 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.

As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSETM reservoir simulator (Schlumberger Limited, Houston Texas) , the INTERSECTTM reservoir simulator (Schlumberger Limited, Houston Texas) , etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc. ) . 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 SAGD, etc. ) .

In an example embodiment, the management components 110 may include features of a commercially available framework such as the

seismic to simulation software framework (Schlumberger Limited, Houston, Texas) . The

framework provides components that allow for optimization of exploration and development operations. The

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. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc. ) .

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

environment (Schlumberger Limited, Houston, Texas) allows for integration of add-ons (or plug-ins) into a

framework workflow. The

framework environment leverages

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. ) .

Figure 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 a modules layer 175. The framework 170 may include the commercially available

framework where the model simulation layer 180 is the commercially available

model-centric software package that hosts

framework applications. In an example embodiment, the

software may be considered a data-driven application. The

software can include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example of Figure 1, 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, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, 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) .

In the example of Figure 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. 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. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. 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, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc. ) .

Figure 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 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.

As mentioned, the system 100 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

software, for example, that operates on seismic data, seismic attribute (s) , etc. As an example, a workflow may be a process implementable in the

framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc. ) .

Figure 2 illustrates a flow chart diagram for a process 200 for fracture network extraction, according to an embodiment. At 205 the process 200 acquires asignal. For example, the signal can be a microseismicsignal acquired via VSI tool, MS RECON, or other tools. At 210 the process 200 determines signal attributes by processing the signalacquired at 205. The signal attributes can include an event time, a location, and a microseismic attribute. At 215 the process 200 selectsinput attributes as inputs for a clustering analysis. For example, based on previous studies and publications, aninput attribute option list can be provided for fracture extraction. Additionally, statistical analysis can be used to select the input attributes. Further, implementations can provide tools to optimize the selection of the input attributes.

At 220, the process 200 preprocessestheinput attributes selected at 215. For example, implementations can provide tools to scale and weight each attribute. Additionally, implementations can provide tools to view and quality control the data after the preprocessing.

At 225, the process 200 performs a clustering analysis based on the signal attributes determined at 210 and the input attributes selected at 215. In implementations, the clustering analysis can use legacy clustering methods (such as, K-means, Fuzzy C-means, model-based cluster etc. ) . Additionally, the clustering analysis can use customized clustering methods for specific situations.

At 230 the process 200 may perform quality control ( “QC” ) on the cluster results from 225. In implementations, tools are provided to view and QC cluster results. If the QC cluster results do not satisfy predetermined QC requirements (i.e., 230, “N” ) , then the process 100 returns to 215. If QC cluster results satisfy the predetermined QC requirements (i.e., 230, “Y” ) , then at 235 the process 200 extracts fracture planes from each event cluster determined clustering analysis at 225. In implementations, the fracture planes can be extracted from each event cluster. At 240, the process 200 forms a fracture network by mergingthe fracture planes determined at 235. In implementations, techniques for merging fracture planes into one fracture network are applied.

In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 3 illustrates an example of such a computing system 300, in accordance with some embodiments. The computing system 300 may include a computer or computer system 301A, which may be an individual computer system 301A or an arrangement of distributed computer systems. The computer system 301A includes one or more analysis modules 302 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 602 executes independently, or in coordination with, one or more processors 304, which is (or are) connected to one or more storage media 306. The processor (s) 304 is (or are) also connected to a network interface 307 to allow the computer system 301A to communicate over a data network 309 with one or more additional computer systems and/or computing systems, such as 301B, 301C, and/or 301D (note that

computer systems

301B, 301C and/or 301D may or may not share the same architecture as computer system 301A, and may be located in different physical locations, e.g.,

computer systems

301A and 301B may be located in a processing facility, while in communication with one or more computer systems such as 301C and/or 301D that are located in one or more data centers, and/or located in varying countries on different continents) .

A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

The storage media 306may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 5 storage media 306 is depicted as within computer system 301A, in some embodiments, storage media 306 may be distributed within and/or across multiple internal and/or external enclosures of computing system 301A and/or additional computing systems. Storage media 306 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) ,

disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture) . An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, computing system 300 contains one or more clustering module (s) 308. In the example of computing system 300, computer system 301A includes the clusteringmodule 308. In some embodiments, a single clustering module 308 may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of clustering modules 308 may be used to perform some aspects of methods herein.

It should be appreciated that computing system 300 is merely one example of a computing system, and that computing system 300 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 5, and/or computing system 300 may have a different configuration or arrangement of the components depicted in Figure 5. The various components shown in Figure 5 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Geologic interpretations, models, and/or other interpretation aids may be refined in an iterative fashion； this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 300, Figure 5) , and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrate and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

A method for fracture network extraction, comprising:

acquiring a microseismic signal；

determining signal attributes by processing the microseismic signal；

selectinginput attributes；

performing a clustering analysis based on the signal attributes and the input attributes；

extractingone or more fracture planes from one or more event clusters determined by the clustering analysis； and

forming a fracture network by mergingthe one or more fracture planes.
The method of claim 1, wherein the signal attributes comprise one or more of an event time, an event location, and a microseismic attribute.
The method of claims 1 or 2, wherein selecting input attributes comprises providing an attribute option list for fracture extraction.
The method of any of claims 1-3, wherein selecting input attributes comprises using statistical analysis to select the input attributes.
The method of any of claims 1-4, further comprising preprocessingtheinput attributes.
The method of claim 5, wherein preprocessingtheinput attributes comprises scaling and weighting the input attributes.
The method of any of claims 1-6, further comprising performing quality control on results of the clustering analysis.
The method of any of claims1-7, wherein extracting one or more fracture planescomprises extracting the fracture planes from each event cluster.
A computing system, comprising:

one or more processors； and

a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:

acquiring a microseismic signal；

determining signal attributes by processing the microseismic signal；

selectinginput attributes as inputs；

performing a clustering analysis based on the signal attributes and the input attributes；

extractingone or more fracture planes from one or more event clusters determined by the clustering analysis； and

forming a fracture network by mergingthe one or more fracture planes.
The computing system of claim 9, wherein the signal attributes comprise one or more of an event time, an event location, and a microseismic attribute.
The computing system of claims 9 or 10, wherein selecting input comprises providing an attribute option list for fracture extraction.
The computing system of any of claims 9-11, wherein selecting input comprises using statistical analysis to select the input attributes.
The computing system of any of claims 9-12, wherein the operations further comprise preprocessingtheinput attributes.
The computing system of claim 13, wherein preprocessingtheinput attributes comprises scaling and weighting the input attributes.
The computing system of any of claims 9-14, wherein the operations further comprise performing quality control on results of the clustering analysis.
The computing system of any of claims 9-15, wherein extracting one or more fracture planescomprises extracting the fracture planes from each event cluster.
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

acquiring a microseismic signal；

determining signal attributes by processing the microseismic signal；

selectinginput attributes as inputs；

performing a clustering analysis based on the signal attributes and the input attributes；

extractingone or more fracture planes from one or more event clusters determined by the clustering analysis； and

forming a fracture network by mergingthe one or more fracture planes.