EP4634702A1

EP4634702A1 - Geologic analogue flow property framework

Info

Publication number: EP4634702A1
Application number: EP24742099.5A
Authority: EP
Inventors: Andreas Laake
Original assignee: Services Petroliers Schlumberger SA; Geoquest Systems BV
Current assignee: Services Petroliers Schlumberger SA; Geoquest Systems BV
Priority date: 2023-01-13
Filing date: 2024-01-12
Publication date: 2025-10-22
Also published as: WO2024151980A1; EP4634702A4

Abstract

A method can include selecting a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

Description

GEOLOGIC ANALOGUE FLOW PROPERTY FRAMEWORK

RELATED APPLICATION

[0001] This application claims priority to and the benefit of US Provisional Application having Serial No. 63/439,005, filed 13 January 2023, which is incorporated by reference herein in its entirety.

BACKGROUND

[0002] A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.).

[0003] An analogue can be an example of an exposed structure, whether at surface or at a water bottom, that can be used for comparison to a subsurface structure (e.g., not exposed at the surface of the Earth, whether a land surface or a water bottom surface). In oil and gas exploration, geoscientists and engineers may compare subterranean structures with surface exposures deemed analogues where they may be thought to be similar in depositional environment and reservoir character to the subterranean structures. Such comparisons may be part of an interpretation process and based on direct visual comparisons of surface imagery to a stratigraphic model (e.g., a layer cake type of model, etc.) of a subterranean region. For example, layers seen in a surface image of an outcrop rendered to a display may be visually compared to layers seen in a stratigraphic model rendered to a display. An outcrop can be a body of rock exposed at the surface of the Earth, which may be exposed naturally or due to one or more human actions (e.g., construction of a highway, construction of a railroad, open pit mining, etc.). While outcrops are mentioned, a water bottom surface may be imaged, for example, using satellites, water surface vessels, subsurface water vessels, etc.

[0004] In oil and gas exploration, interpretation is a process that involves analysis of data to identify and locate various subsurface structures (e.g., horizons, faults, geobodies, etc.) in a geologic environment. Various types of structures (e.g., stratigraphic formations) may be indicative of hydrocarbon traps or flow channels, as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation can allow for construction of a more accurate model of a subsurface region, which, in turn, may improve characterization of the subsurface region for purposes of resource extraction. While resource extraction is mentioned, one or more reservoirs may be suitable for storage. For example, consider storage of water, oil, gas (e.g., natural gas, hydrogen, etc.), etc. As an example, characterization processes may indicate that a reservoir may be suitable for storage of carbon, for example, as part of a carbon sequestration process. Characterization of one or more subsurface regions in a geologic environment can guide, for example, performance of one or more operations (e.g., field operations, etc.). As an example, a more accurate model of a subsurface region may make a drilling operation more accurate as to a borehole’s trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc.

SUMMARY

[0005] A method can include selecting a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location. A system can include one or more processors; a memory accessible to at least one of the one or more processors; and processor-executable instructions stored in the memory and executable to instruct the system to: select a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location. One or more non-transitory computer-readable storage media can include processorexecutable instructions to instruct a computing system to: select a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location. Various other apparatuses, systems, methods, etc., are also disclosed.

[0006] 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

[0007] 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.

[0008] FIG. 1 illustrates an example system that includes various framework components associated with one or more geologic environments;

[0009] FIG. 2 illustrates examples of a basin, a convention and a system;

[0010] FIG. 3 illustrates an example of a system;

[0011] FIG. 4 illustrates examples of systems;

[0012] FIG. 5 illustrates an example of a graphic of depositional environments renderable to a graphical user interface;

[0013] FIG. 6 illustrates an example of a graphic of systems tracts renderable to a graphical user interface;

[0014] FIG. 7 illustrates an example of a plot;

[0015] FIG. 8 illustrates examples of components of a framework;

[0016] FIG. 9 illustrates an example of a workflow;

[0017] FIG. 10 illustrates an example of a seismic image that includes identified structures; [0018] FIG. 11 illustrates examples of images;

[0019] FIG. 12 illustrates examples of images;

[0020] FIG. 13 illustrates examples of images;

[0021] FIG. 14 illustrates examples of images;

[0022] FIG. 15 illustrates an example of a graphical user interface;

[0023] FIG. 16 illustrates an example of a graphical user interface;

[0024] FIG. 17 illustrates an example of a seismic image that includes identified structures suitable for training one or more machine learning models;

[0025] FIG. 18 illustrates an example of a method; and

[0026] FIG. 19 illustrates examples of computer and network equipment.

DETAILED DESCRIPTION

[0027] 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.

[0028] As explained, analogues can exist for subsurface structures where such analogues may be visible as outcrops and/or at a water bottom (e.g., an ocean bottom, etc.). In various instances, subsurface structures may have been formed by movement of water such as, for example, water currents. As an example, a framework can provide for associating subsurface ancient water bottom locations and modern water bottom locations. In such an example, based on one or more associations, measurements as to one or more modern water bottom flows (e.g., currents, etc.), may be assigned to one or more subsurface ancient water bottom locations; noting that a framework may provide for modern to ancient, ancient to modern, ancient to ancient and/or modern to modern types of associations and/or water bottom flow assignments. As an example, associations may be made between flow, material characteristics, and structural characteristics. For example, flow can influence distribution of material, which may be characterized by properties such as grain size. In various instances, flow influenced distribution of particles may provide indications as to possible traps (e.g., seals), which may act to form a reservoir. As such, a material property may be considered to be a flow property. In geoscience, sediment transport phenomena may help to explain what type of grains become entrained in a flow stream based on one or more material properties, such as, for example, size, density, and shape; noting that gravity and direction of flow and/or structural features with respect to gravity may also play a role in sediment transport.

[0029] Below, FIG. 1 , FIG. 2, FIG. 3, and FIG. 4 describe various types of equipment, environments and workflows while FIG. 5 and FIG. 6 describe various types of environments. FIG. 7 describes associations between flow velocity, grain size, and structures or no structures. FIGs. 8 to 18 describe various examples of frameworks, workflows, methods, etc., where associations and assignments can be performed as to analogues and flows while FIG. 19 describes some examples of computing equipment, networking and associated techniques. As an example, a geologic analogue flow property framework can operate as a paleo flow meter using geologic analogues for purposes of ocean current interpretation. Such a framework may be utilized in one or more workflows, which can include workflows for assessment of subsurface regions (e.g., as to reservoirs, presence of hydrocarbons, etc.) and/or workflows for water current research, which may involve climate change research. While various examples refer to water flow, as an example, a framework may be applied to one or more other types of flows such as, for example, lava flow where, one or more modern analogues exists for measurements of flow of lava (e.g., flow velocity, flow direction, etc.). As an example, a geologic analogue flow property framework may provide for identifying features within seismic data, which may be related to flow. For example, consider identifying features within seismic data according to sediment transport phenomena such that seismic data can provide indications of material properties. In such an example, a region may be characterized on a relatively fine scale even though the seismic data may be on a relatively coarse scale.

[0030] FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that can provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of FIG. 1 , the GU1 120 can include graphical controls for computational frameworks (e.g., applications) 121 , projects 122, visualization features 123, one or more other features 124, data access 125, and data storage 126.

[0031] In the example of FIG. 1 , the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, 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 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 wellsite 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 170 in communication with the network 155 that may be configured for communications, noting that the satellite 170 may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.). As an example, the satellite 170 may be configured for measuring flow water flows (e.g., ocean currents), measuring water temperatures and/or energy, imaging one or more water bottom surfaces, etc. As an example, the satellite 170 and/or one or more other devices may be utilized to capture imagery and/or acquire other measurements of an environment, which may be a modern environment; noting that a modern environment may include one or more outcroppings of an ancient environment.

[0032] 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 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. [0033] In the example of FIG. 1 , the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, INTERSECT, PIPESIM and OMEGA frameworks (SLB, Houston, Texas). As to another type of framework, consider, for example, an emissions framework (EF), which may be operable in combination with one or more other frameworks to make determinations as to emissions (e.g., of one or more field operations, etc.). In such an example, an EF may provide feedback such that another framework can operate on output of the EF, for example, to revise a plan, revise a control scheme, etc., which may be in a manner that aims to reduce one or more types of emissions and/or other impact from an activity, etc. As an example, an EF may be utilized in one or more workflows involving carbon capture and storage (e.g., carbon sequestration, etc.).

[0034] The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.

[0035] The PETREL framework can be part of the DELFI cognitive E&P environment (SLB, Houston, Texas) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

[0036] The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc.

[0037] The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

[0038] The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

[0039] The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed chemical-enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI cognitive E&P environment, for example, for rapid simulation of multiple concurrent cases. For example, a workflow may utilize one or more of the DELFI on demand reservoir simulation features.

[0040] 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 (SLB, 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.). As an example, the PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena.

[0041] The OMEGA framework includes finite difference modelling (FDMOD) features for two-way wavefield extrapolation modelling, generating synthetic shot gathers with and without multiples. The FDMOD features can generate synthetic shot gathers by using full 3D, two-way wavefield extrapolation modelling, which can utilize wavefield extrapolation logic matches that are used by reverse-time migration (RTM). A model may be specified on a dense 3D grid as velocity and optionally as anisotropy, dip, and variable density. The OMEGA framework also includes features for RTM, FDMOD, adaptive beam migration (ABM), Gaussian packet migration (Gaussian PM), depth processing (e.g., Kirchhoff prestack depth migration (KPSDM), tomography (Tomo)), time processing (e.g., Kirchhoff prestack time migration (KPSTM), general surface multiple prediction (GSMP), extended interbed multiple prediction (XI MP)), framework foundation features, desktop features (e.g., GUIs, etc.), and development tools. Various features can be included for processing various types of data such as, for example, one or more of: land, marine, and transition zone data; time and depth data; 2D, 3D, and 4D surveys; isotropic and anisotropic (TTI and VTI) velocity fields; and multicomponent data.

[0042] The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in FIG. 1 , outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

[0043] As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory, which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework. As an example, a system or systems may utilize a framework such as the DELFI framework (SLB, Houston, Texas). Such a framework may operatively couple various other frameworks to provide for a multiframework workspace. As an example, the GUI 120 of FIG. 1 may be a GUI of the DELFI framework.

[0044] In the example of FIG. 1 , the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

[0045] As an example, a visualization process can implement one or more of various features that can be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter.

[0046] As an example, visualization features can provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features can provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

[0047] As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of 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. Such interpretation results can be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.) and/or storage of fluid to a reservoir (e.g., reservoir rock, etc.).

[0048] 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 1 D, 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).

[0049] 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. A simulator, such as a reservoir simulator, can simulate fluid flow in a geologic environment based at least in part on a model that can be generated via a framework that receives seismic data. A simulator can be a computerized system (e.g., a computing system) that can execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that that includes layers of rock, geobodies, etc., that have corresponding positions that can be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model can represent a physical area or volume in a geologic environment where the cell can be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model can be a spatial model that may be cell-based.

[0050] A simulator can be utilized to simulate the exploitation of a real reservoir, for example, to examine different productions scenarios to find an optimal one before production or further production occurs. A reservoir simulator does not provide an exact replica of flow in and production from a reservoir at least in part because the description of the reservoir and the boundary conditions for the equations for flow in a porous rock are generally known with an amount of uncertainty. Certain types of physical phenomena occur at a spatial scale that can be relatively small compared to size of a field. A balance can be struck between model scale and computational resources that results in model cell sizes being of the order of meters; rather than a lesser size (e.g., a level of detail of pores). A modeling and simulation workflow for multiphase flow in porous media (e.g., reservoir rock, etc.) can include generalizing real micro-scale data from macro scale observations (e.g., seismic data and well data) and upscaling to a manageable scale and problem size. Uncertainties can exist in input data and solution procedure such that simulation results too are to some extent uncertain. A process known as history matching can involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, can provide for adjustments to a model, data, etc., which can help to increase accuracy of simulation.

[0051] As an example, a simulator may utilize various types of constructs, which may be referred to as entities. Entities may include earth entities or geological objects such as wells, surfaces, reservoirs, etc. Entities can include virtual representations of actual physical entities that may be reconstructed for purposes of simulation. Entities may include entities based on data acquired via sensing, observation, etc. (e.g., consider entities based at least in part on seismic data and/or other information). 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.

[0052] As an example, a simulator may utilize an object-based software framework, which may include entities based on pre-defined classes to facilitate modeling and simulation. As an example, an object class can encapsulate 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.).

[0053] While several simulators are illustrated in the example of FIG. 1 , one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (SLB, Houston Texas), 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, caprock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO2 disposal, etc. The MANGROVE simulator (SLB, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery.

[0054] As mentioned, a framework may be implemented within or in a manner operatively coupled to the DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas), which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. As an example, the DELFI framework can include various other frameworks, which can include, for example, one or more types of models (e.g., simulation models, etc.).

[0055] As mentioned, reflection seismology 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 or optionally less than 1 Hz and/or optionally more than 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks.

[0056] Digital images of a subsurface region of the Earth can be generated using digital seismic data acquired using reflection seismology as part of a seismic survey. A digital image can show subterranean structure, for example, as related to one or more of exploration for petroleum, natural gas, and mineral deposits; or, for example, as to a reservoir or reservoirs that may be suitable for fluid storage. As an example, reflection seismology can include determining time intervals that elapse between initiation of a seismic wave at a selected shot point (e.g., the location where an explosion generates seismic waves) and the arrival of reflected or refracted impulses at one or more seismic detectors (e.g., sensing of seismic energy at one or more seismic receivers). As an example, a seismic air gun can be used to initiate seismic waves. As an example, one or more electric vibrators or falling weights (e.g., thumpers) may be employed at one or more sites. Upon arrival at the detectors, the amplitude and timing of seismic energy waves can be recorded, for example, as a seismogram (e.g., a record of ground vibrations). [0057] In various regions of the Earth, the material density (e.g., rock density) increases with depth. Seismic energy waves can be initiated at a shot point (or points) at or near the surface where a portion of the seismic energy, as waves, may reach one or more receiving points. Material properties and structural organization of materials (e.g., as objects, layers, etc.) can affect seismic energy waves in one or more manners. Received seismic energy waves can be utilized to determine one or more types of material properties and/or structural organization of one or more types of materials. As with sound traveling through air or water, seismic energy waves can be attenuated as they pass through subsurface materials, which may include air, water, hydrocarbons, rock, etc. Such attenuation can occur in a manner that is dependent on material properties of such materials.

[0058] Interpretation of the depths and media reached by seismic energy waves can depend on geometry of a seismic survey, for example, on the distance between shot points and receiving points, as well as densities of media. Results of a seismic survey may be in digital form (e.g., digital data) as stored in memory of a computing device where display circuitry (e.g., a graphics processor, a video processor, etc.) can render the digital data to a display in the form of a cross-sectional image of subsurface structures as if cut by a plane through the shot point, the detector, and a reference point such as the Earth’s center. As an example, digital image processing can involve receiving seismic data as digital data, processing the seismic data via one or more techniques, and rendering processed seismic data to a display as an image of a region of the Earth that can show structural features of the Earth that otherwise are not visible from an observer standing on the surface of the Earth.

[0059] A seismic survey can be defined with respect to a region of the Earth and, for example, a manner of acquisition of seismic data. As an example, a survey may be two-dimensional, three-dimensional, four-dimensional, etc. Dimensions include one or more spatial dimensions and optionally one or more temporal dimensions (e.g., repeating a survey for a region at different points in time). As to a 2D survey, a grid may be considered dense if the line spacing (e.g., of receivers) is less than about 400 m. As to a 3D spatial survey, in comparison to a 2D spatial survey, it may help to elucidate true structural dip (e.g., a 2D survey may give apparent dip), it may provide more and better stratigraphic information, it may provide a map view of reservoir properties, it may provide a better areal mapping of fault patterns and connections and delineation of reservoir blocks, it may provide better lateral resolution (e.g., 2D may suffer from a cross-line smearing, or Fresnel zone, problem).

[0060] As to data sets, a 3D spatial seismic data set can be a cube or volume of data. As an example, a 2D spatial seismic data set can be a panel of data. To interpret 3D seismic data, a method can process the “interior” of the cube (e.g., seismic cube) using one or more processors of computing equipment. As an example, a 3D seismic data set can range in size from a few tens of megabytes to several gigabytes or more.

[0061] As to a 3D seismic cube, a point can have an (x, y, z) coordinate and a data value. A coordinate can be a distance from a particular corner of the cube. A 3D seismic data volume is like a room-temperature example (e.g., where temperature differs in a cube shaped room), however, rather than a height of a room, a height or vertical axis can be in terms of a two-way traveltime, which may be a proxy for depth. In such an example, the 3D seismic cube is still a spatial cube because the data therein correspond to the same survey where, rather than depth, two-way traveltime (TWT) is utilized, which, can be, in general, a proxy for depth. And, in contrast to roomtemperature, data values can be seismic amplitudes (e.g., amplitudes of seismic energy waves). A 3D seismic data set can be, for example, a box full of electronically determined numbers where each number represents a measurement (e.g., amplitude of a seismic energy wave, etc.). In a 3D seismic data set, amplitudes may be rendered as data values in the form of one or more images for slices through the 3D seismic data set where, for example, in grayscale, dark and light image bands in the sections are related to rock boundaries.

[0062] Reflection seismology can be implemented as a technique that detects “edges” of materials in the Earth. An image generated utilizing reflection seismology can show such edges of materials, which can be equated to positions in the Earth such that one may know where an edge of a material is in the Earth. For example, where the edge corresponds to a hydrocarbon reservoir, a method can include drilling to the reservoir in a manner guided by the position of the edge. As an example, a drilling process can be manual, semi-automated or automated where positional information as to an edge of a material in the Earth can be utilized to guide drilling equipment that forms a bore in the Earth where the bore may be directed to the edge or to a region that is defined at least in part by the edge. Where reflection seismology is improved, such an “edge” may be detected more readily and/or with greater accuracy (e.g., resolution), which, in turn, can improve one or more field processes such as a drilling process.

[0063] 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.

[0064] 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.

[0065] 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).

[0066] 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.).

[0067] 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.). As explained, sediment transport phenomena may play a role where material can be transported by a fluid (e.g., water, gas, etc.).

[0068] 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.

[0069] 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.

[0070] As an example, one or more probes may be deployed in a bore via a wireline or wirelines. 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 bore. 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.).

[0071] As an example, lithology scanning technology may be employed to acquire and analyze data. For example, consider the LITHO SCANNER technology marketed by SLB (Houston, Texas). As an example, a LITHO SCANNER tool may be a gamma ray spectroscopy tool.

[0072] 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 (SLB, Houston, Texas).

[0073] 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, ECLIPSE, OMEGA, etc ).

[0074] 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).

[0075] 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., Dip? 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 a₉₀) and also the maximum possible value of dip magnitude. Another term is “apparent dip” (see, e.g., DipA 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., as DipA for angle a); however, it is possible that the apparent dip is equal to the true dip (see, e.g., </> as DipA = Dip? for angle a₉₀ 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. [0076] 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 DipA = Dip? for angle ago 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).

[0077] 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.

[0078] 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. As an example, one or more dips may be utilized for understanding phenomena, whether ancient or modern. As explained, sediment transport phenomena can depend on orientation of a structural feature with respect to gravity. For example, consider material that may roll down a surface of a slope to then encounter a flow stream, which may act to segregate the material according to properties thereof. In such an example, a portion of the material may be deposited below the flow stream while another portion of the material may be transported and deposited elsewhere.

[0079] A convention such as the convention 240 may be used with respect to an analysis, an interpretation, an attribute, etc. 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. [0080] 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.

[0081] 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.

[0082] 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 a 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. As an example, the system 250 may be local, remote or in part local and in part remote. As to remote resources, consider one or more cloud-based resources (e.g., as part of a cloud platform, etc.).

[0083] 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 one or more aspects of the workspace framework 110 of FIG. 1 . 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.

[0084] 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 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.

[0085] FIG. 3 shows an example of a system 300 that includes a geological/geophysical data block 310, a surface models block 320 (e.g., for one or more structural models), a volume modules block 330, an applications block 340, a numerical processing block 350 and an operational decision block 360. As shown in the example of FIG. 3, the geological/geophysical data block 310 can include data from well tops or drill holes 312, data from seismic interpretation 314, data from outcrop interpretation and optionally data from geological knowledge. As to the surface models block 320, it may provide for creation, editing, etc. of one or more surface models based on, for example, one or more of fault surfaces 322, horizon surfaces 324 and optionally topological relationships 326. As to the volume models block 330, it may provide for creation, editing, etc. of one or more volume models based on, for example, one or more of boundary representations 332 (e.g., to form a watertight model), structured grids 334 and unstructured meshes 336.

[0086] As shown in the example of FIG. 3, the system 300 may allow for implementing one or more workflows, for example, where data of the data block 310 are used to create, edit, etc. one or more surface models of the surface models block 320, which may be used to create, edit, etc. one or more volume models of the volume models block 330. As indicated in the example of FIG. 3, the surface models block 320 may provide one or more structural models, which may be input to the applications block 340. For example, such a structural model may be provided to one or more applications, optionally without performing one or more processes of the volume models block 330 (e.g., for purposes of numerical processing by the numerical processing block 350). Accordingly, the system 300 may be suitable for one or more workflows for structural modeling (e.g., optionally without performing numerical processing per the numerical processing block 350).

[0087] As to the applications block 340, it may include applications such as a well prognosis application 342, a reserve calculation application 344 and a well stability assessment application 346. As to the numerical processing block 350, it may include a process for seismic velocity modeling 351 followed by seismic processing 352, a process for facies and petrophysical property interpolation 353 followed by flow simulation 354, and a process for geomechanical simulation 355 followed by geochemical simulation 356. As indicated, as an example, a workflow may proceed from the volume models block 330 to the numerical processing block 350 and then to the applications block 340 and/or to the operational decision block 360. As another example, a workflow may proceed from the surface models block 320 to the applications block 340 and then to the operational decisions block 360 (e.g., consider an application that operates using a structural model).

[0088] In the example of FIG. 3, the operational decisions block 360 may include a seismic survey design process 361 , a well rate adjustment process 352, a well trajectory planning process 363, a well completion planning process 364, and a process for one or more prospects 365, for example, to decide whether to explore, develop, abandon, etc. a prospect.

[0089] Referring again to the data block 310, the well tops or drill hole data 312 may include spatial localization, and optionally surface dip, of an interface between two geological formations or of a subsurface discontinuity such as a geological fault; the seismic interpretation data 314 may include a set of points, lines or surface patches interpreted from seismic reflection data, and representing interfaces between media (e.g., geological formations in which seismic wave velocity differs) or subsurface discontinuities; the outcrop interpretation data 316 may include a set of lines or points, optionally associated with measured dip, representing boundaries between geological formations or geological faults, as interpreted on the earth surface; and the geological knowledge data 318 may include, for example knowledge of the paleo-tectonic and sedimentary evolution of a region.

[0090] As to a structural model, it may be, for example, a set of gridded or meshed surfaces representing one or more interfaces between geological formations (e.g., horizon surfaces) or mechanical discontinuities (fault surfaces) in the subsurface. As an example, a structural model may include some information about one or more topological relationships between surfaces (e.g. fault A truncates fault B, fault B intersects fault C, etc.).

[0091] As to the one or more boundary representations 332, they may include a numerical representation in which a subsurface model is partitioned into various closed units representing geological layers and fault blocks where an individual unit may be defined by its boundary and, optionally, by a set of internal boundaries such as fault surfaces.

[0092] As to the one or more structured grids 334, it may include a grid that partitions a volume of interest into different elementary volumes (cells), for example, that may be indexed according to a pre-defined, repeating pattern. As to the one or more unstructured meshes 336, it may include a mesh that partitions a volume of interest into different elementary volumes, for example, that may not be readily indexed following a pre-defined, repeating pattern (e.g., consider a Cartesian cube with indexes I, J, and K, along x, y, and z axes).

[0093] As to the seismic velocity modeling 351 , it may include calculation of velocity of propagation of seismic waves (e.g., where seismic velocity depends on type of seismic wave and on direction of propagation of the wave). As to the seismic processing 352, it may include a set of processes allowing identification of localization of seismic reflectors in space, physical characteristics of the rocks in between these reflectors, etc.

[0094] As to the facies and petrophysical property interpolation 353, it may include an assessment of type of rocks and of their petrophysical properties (e.g. porosity, permeability), for example, optionally in areas not sampled by well logs or coring. As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge.

[0095] As to the flow simulation 354, as an example, it may include simulation of flow of hydro-carbons in the subsurface, for example, through geological times (e.g., in the context of petroleum systems modeling, when trying to predict the presence and quality of oil in an un-drilled formation) or during the exploitation of a hydrocarbon reservoir (e.g., when some fluids are pumped from or into the reservoir).

[0096] As to geomechanical simulation 355, it may include simulation of the deformation of rocks under boundary conditions. Such a simulation may be used, for example, to assess compaction of a reservoir (e.g., associated with its depletion, when hydrocarbons are pumped from the porous and deformable rock that composes the reservoir). As an example, a geomechanical simulation may be used for a variety of purposes such as, for example, prediction of fracturing, reconstruction of the paleogeometries of the reservoir as they were prior to tectonic deformations, etc.

[0097] As to geochemical simulation 356, such a simulation may simulate evolution of hydrocarbon formation and composition through geological history (e.g., to assess the likelihood of oil accumulation in a particular subterranean formation while exploring new prospects).

[0098] As to the various applications of the applications block 340, the well prognosis application 342 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 344 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 346 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress.

[0099] As to the operational decision block 360, the seismic survey design process 361 may include deciding where to place seismic sources and receivers to optimize the coverage and quality of the collected seismic information while minimizing cost of acquisition; the well rate adjustment process 362 may include controlling injection and production well schedules and rates (e.g., to maximize recovery and production); the well trajectory planning process 363 may include designing a well trajectory to maximize potential recovery and production while minimizing drilling risks and costs; the well trajectory planning process 364 may include selecting proper well tubing, casing and completion (e.g., to meet expected production or injection targets in specified reservoir formations); and the prospect process 365 may include decision making, in an exploration context, to continue exploring, start producing or abandon prospects (e.g., based on an integrated assessment of technical and financial risks against expected benefits). While various processes, data, etc., in the system 300 refer to fluid production, the system 300 may be suitably utilized for one or more other purposes, such as, for example, fluid storage (e.g., hydrogen storage, natural gas storage, carbon storage, etc.). [00100] FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility. In the example of FIG. 4, individual wellsites 402 can include equipment that can form individual wellbores 436. Such wellbores can extend through subterranean formations including one or more reservoirs 404. Such reservoirs 404 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 444. As an example, a surface network can include tubing and control mechanisms for controlling flow of fluids from a wellsite to a processing facility. In the example of FIG. 4, a rig 454 is shown, which may be an offshore rig or an onshore rig. As an example, a rig can be utilized to drill a borehole that can be completed to be a wellbore where the wellbore can be in fluid communication with a reservoir such that fluid may be produced from the reservoir and/or injected into the reservoir.

[00101] FIG. 5 shows a diagram of examples of various depositional environments 500. A depositional environment or sedimentary environment describes the combination of physical, chemical and biological processes associated with the deposition of a particular type of sediment and, thus, rock types that can be formed after lithification, if the sediment is preserved in the rock record.

[00102] As an example, a method or methods may aim to match environments associated with particular rock types or associations of rock types to one or more existing analogues. However, the further back in geological time sediments were deposited, the more likely it may become that one or more direct modern analogues are not available.

[00103] Depositional environments may be classified using various descriptors such as, for example, continental, transitional, marine and other.

[00104] As to continental, it can include alluvial, a type of fluvial deposit, that is caused by moving water in a fan shape (e.g., alluvial fan) and containing impermeable and nonporous sediments that tend to be well sorted. Another continental type is Aeolian, which is due to wind activity (e.g., air currents). For example, wind activity can be seen in deserts and coastal regions and well sorted, large scale cross-beds. Yet another continental type is fluvial, as mentioned, due to processes associated with moving water, mainly streams, where common sediments can include gravel, sand, and silt. Another continental type is lacustrine, which is also due to processes associated with moving water, mainly lakes, where common sediments can include sand, silt, and clay.

[00105] As to transitional, it can include deltaic, which is characterized by a silt deposition landform at the mouth of a river (e.g., possible cross beds, ripple marks) where common sediments can include sand, silt, and clay. Another type of transitional is tidal, due to processes associated with tidal currents, which can create tidal flats (e.g., fine-grained, ripple marks, cross-beds) where common sediments can include silt and clay. Yet another type of transitional is lagoonal, which can be associated with a shallow body of water separated from a larger body of water, for example, by barrier islands or reefs. In various instances, little transportation creates a lagoon bottom environment. Common sediments can include carbonates (e.g., in tropical climates). Another type of transitional is beach, which can be an area of loose particles at the edge of the sea or other body of water. A beach can be caused by waves and longshore currents. Processes can create beaches, spits, and sandbars with common sediments of gravel and sand. Yet another type of transitional is lake, which is a large body of relatively still water.

[00106] As to marine, a shallow water marine environment can be due to processes associated with waves and tidal currents, which can create shelves and slopes, and/or lagoons. Common sediments can include carbonates (e.g., in tropical climates) or sand, silt, and clay (e.g., non-tropical). A marine type can be an upper shoreface, which is a portion of the seafloor that is shallow enough to be agitated by everyday wave action, and another marine type can be a lower shoreface, which is a portion of the seafloor, and the sedimentary depositional environment, that lies below the everyday wave base. Yet another marine type can be a deep water marine environment, which can be a flat area on a deep ocean floor (e.g., abyssal plains) caused by ocean currents where common sediments can include clay, carbonate mud, and silica mud. Another marine type is reef, which can be a shoal of rock, sand, coral or similar material, lying beneath the surface of water caused by waves and tidal currents, which may also create one or more adjacent basins. Common reef sediments include carbonates.

[00107] As to others, consider evaporite, which is a water-soluble mineral sediment formed by evaporation from an aqueous solution and consider glacial, which can include till, which is angular to rounded grains, poorly sorted, unstratified (e.g., massive) and outwash, which can include ripple marks and/or cross-beds, similar to stream channels. Yet others include volcanic and tsunami, which is a sedimentary unit deposited by a tsunami.

[00108] Depositional environments in ancient sediments may be recognized by using a combination of one or more of sedimentary facies, facies associations, sedimentary structures and fossils, particularly trace fossil assemblages, as they can indicate the environment in which organisms lived. As to the term ancient, it may be defined in a paleo-sense, such as, for example, involving or dealing with ancient forms and/or conditions. In various instances, an ancient location may not be exposed as it may be covered by sediment deposited over some period of time that may be, for example, more than 1 ,000 years into the past from a present, modern time. As such, an ancient location may not be observable to the human eye or by a conventional visible light sensing camera, except, for example, where one or more outcroppings may exist. As explained, reflection seismology may be employed to acquire seismic data indicative of one or more structural features at an ancient location, which may be, for example, an ancient water bottom location (e.g., a location that was at some point or points in time a water bottom).

[00109] As an example, a geologic environment may be characterized with respect to one or more system tracts. For example, consider a systems tract as a sequence subdivision that includes one or more depositional units that may differ in geometry from another systems tract.

[00110] As an example, different systems tracts may represent different phases of eustatic changes. Eustasy pertains to sea level and its variations. Thus, eustatic changes may pertain to sea level changes, which may result, for example, from movement of tectonic plates that alter volume of an ocean basin, from climate effects on volume of water stored in glaciers/icecaps, etc. Eustasy can affect positions of shorelines and processes of sedimentation, which can make interpretation of eustasy a useful aspect of sequence stratigraphy.

[00111] As an example, a lowstand systems tract (LST) may develop during times of relatively low sea level; a highstand systems tract (HST) may develop at times of high sea level; and a transgressive systems tract (TST) may develop at times of changing sea level.

[00112] A lowstand systems tract (LST) may be a systems tract overlying a sequence boundary (SB) and overlain by a transgressive surface (TS). A lowstand systems tract (LST) may be characterized by a progradational to aggradational parasequence set. As an example, a lowstand systems tract (LST) may be a basinfloorfan, a slope fan, a lowstand wedge, etc.

[00113] A highstand systems tract (HST) may be a systems tract bounded below by a downlap surface (DS) and above by a sequence boundary (SB). A highstand systems tract (HST) may be characterized by an aggradational to progradational parasequence set.

[00114] As an example, retrogradation may be characterized by accumulation of sequences by deposition in which beds are deposited successively landward, for example, where sediment supply may be limited and unable to fill available accommodation. For example, the position of a shoreline may migrate backward onto land, a process called transgression, during episodes of retrogradation.

[00115] As an example, aggradation may be characterized by accumulation of stratigraphic sequences by deposition that stack beds atop one another, for example, building upwards during periods of balance between sediment supply and accommodation.

[00116] As an example, progradation may be characterized by accumulation of sequences by deposition in which beds are deposited successively basinward, for example, where sediment supply exceeds accommodation. For example, the position of a shoreline may migrate into a basin during episodes of progradation (e.g., regression).

[00117] As shown in the example of FIG. 5, various features may be influenced by one or more water currents, such as, for example, an ocean bottom current (see arrow near the bottom of a sloped structural feature). Where a water current exists proximate to a slope, material falling down the slope (e.g., due to gravity, etc.) may enter a body of water and interact with the water current. For example, consider a water current that moves at an angle with respect to a slope such as at an orthogonal angle. In such an example, one or more sediment waves and a moat may be formed. In various instances, a water current may be a vortex that acts to separate materials, for example, based on properties that may include density, size and shape. In such instances, larger particles may be deposited along the water current while smaller particles are distributed away from a moat. [00118] As shown in FIG. 5, away from the sediment waves, one or more basin floor fans may exist; noting that an abyssal plain may be associated with or otherwise include a region that has effectively no water currents.

[00119] As shown in FIG. 5, at some distance away from the sediment waves, polygonal faults may exist. For example, consider polygonal faults as described in an article by Goulty, N. (2008), entitled “Geomechanics of polygonal fault systems: A review”, Petroleum Geoscience, 14, pp. 389-397 (10.1144/1354-079308-781 ), which is incorporated by reference herein in its entirety.

[00120] As to polygonal faults, layer-bound systems of polygonal faults may be found in sequences of relatively fine-grained sediments that may have undergone passive subsidence and burial. In an absence of tectonic extension, heave of faults can be complemented by horizontal compaction of sediments. Density inversion, syneresis and low coefficients of friction on fault planes have been proposed as causal mechanisms for the development of polygonal fault systems, but various sequences that include polygonal faults may not be underlain by sediments of lower density, noting that there tends to be a lack of evidence to support the idea that syneresis is responsible. The article by Goulty describes low coefficients of residual friction in finegrained sediments as leading to growth of faults that may eventually develop into polygonal systems; noting that coefficients of residual friction apply to faults only after initial slip has taken place, such that one or more other mechanisms may be responsible for an initial nucleation of faults. As explained, polygonal faults may be associated with relatively fine-grained sediments; hence, where polygonal faults are observed, it may be possible to infer one or more grain characteristics.

[00121] FIG. 6 shows an example of a graphic 600 with respect to sequence stratigraphy identification. The graphic 600 shows examples of some types of sequence boundaries, one or more HST s, a transgressive systems tract (TST), a shelf margin wedge, a lowstand fan, lowstand wedge, etc.

[00122] As shown in FIG. 5 and FIG. 6, various types of depositional environments may be associated various physical processes that generated gross sediment geometric end members, which include sequences, systems tracts, and parasequences.

[00123] FIG. 7 shows an example plot 700 of flow velocity in meters per second versus grain size in millimeters. As shown, material may be characterized as being clay and silt, sand, or gravel, with respect to increasing grain size; noting that the plot 700 utilized log scales. In the plot 700, five different regions are labeled, including: (1) smooth surface, no structures; (2) reservoir structures related to velocity, reservoir quality related to grain size; (3) no deposition; (4) internal seal structures related to velocity; and (5) final seal, polygonal faults. As shown, low velocity and small grain size can be characteristic of polygonal faults, which may be indicative of a final seal as in region (5). Further, as shown, relatively high flow velocities can reduce deposition or, for example, provide no deposition as in region (3). The plot 700 of FIG. 7 demonstrates that relationships can be established between flow velocity, grain size and structures or no structures. As explained, one or more types of sediment transport phenomena may provide for distribution of material, which may be in a manner that depends on flow velocity and one or more material properties, such as, for example, one or more of density, size, and shape.

[00124] An article by Stow et al. (2002), entitled “Bottom currents, contourites and deep-sea sediment drifts: current state-of-the-art”, as appearing in Stow et al. (eds.), entitled “Deep-Water Contourite Systems: Modern Drifts and Ancient Series, Seismic and Sedimentary Characteristics”, Geological Society, London, Memoirs, 22, 7-20 (DOI: 10.1144/GSL.MEM.2002.022.01.02) is incorporated by reference herein in its entirety. Additionally, an article by Stow et al. (2009), entitled “Bedform-velocity matrix: The estimation of bottom current velocity from bedform observations”, Geology, 37(4), 327-330 (DOI: 10.1130/G25259A.1) is incorporated by reference herein in its entirety.

[00125] As explained, a sequence can be a group of relatively conformable strata that represent a cycle of deposition that is bounded by unconformities or correlative conformities and can form a unit of interpretation in sequence stratigraphy where a sequence can include a systems tract. A parasequence can be a relatively conformable depositional unit bounded by surfaces of marine flooding, surfaces that separate older strata from younger and show an increase in water depth in successively younger strata. A parasequence tends to be thin, which may make detection using seismography more difficult; however, when present as a set of parasequences, detection can be easier.

[00126] As explained, a shoreline may define a landward side and a seaward side. A shoreline may exist at present time or a shoreline may have existed prior to present time. As an example, a number of abyssal basins on both sides of the MidAtlantic Ridge and bounded by east-west fractures that hold substantial thickness of sedimentation and hydrocarbon-rich black shales. Various abyssal basin plains are at water depths of approximately 4 km to 5 km. For example, three extensive hydrocarbon deposits have been found in 4 km deep abyssal plains off the northwestern coast of Africa. Investigation by seismic acquisition vessels determined that sequences found during coring conducted in the Gambia Basin extended throughout the basin and were found in similar depths in other basins located along the equatorial Atlantic. Hydrocarbon deposits lie in an area of complex terrain surrounding the Mid-Atlantic Ridge, which is displaced eastward 3700 km with the aid of numerous lateral east-west transform faults. These transform faults or fractures divide up the abyssal areas into basins, with Gambia and Sierra Leone in the east and Ceara and Demerara in the west. During the Early Cretaceous (110 million years ago), water transfer between the two adjacent continents was restricted by shallow fracture zones where, under a tropical environment, conditions were favorable for accumulation of organic matter.

[00127] Relatively deep reserves also exist below the pre-salt layer, which is a diachronous series of geological formations on the continental shelves of extensional basins formed after the break-up of Gondwana, characterized by the deposition of thick layers of evaporites, mostly salt. Some of the petroleum that was generated from sediments in the pre-salt layer has not migrated upward (e.g., salt dome formation) to the post-salt layers above. Such features can be found off the coast of Africa and Brazil. Oil and natural gas reserves lie below an approximately 2 km thick layer of salt, itself below in places more than 2 km of post-salt sediments, in water depths between approximately 2 km and 3 km in the South Atlantic. Drilling through the rock and salt, especially in such deep water environments, can be technologically complex and resource intensive.

[00128] Where a geologic environment is at a considerable depth, whether onshore and/or offshore, an analogue or analogues may be sought to understand better the geologic environment. For example, consider a study by Freire et al. (Searching for Potential Analogues for the Pre-Salt Santos Basin, Brazil: High- Resolution Stratigraphic Studies of Microbialite-Bearing Successions from Salta Basin, Argentina, AAPG International Conference & Exhibition, January 2011 ), which examined outcrops of the Salta Basin as an analogue to pre-salt reservoirs offshore Brazil. In particular, microbialite successions were utilized as input for production zoning, predictability of reservoir facies and geocellular modeling of carbonate reservoirs of microbial origin from the Pre-Salt Santos Basin.

[00129] The Salta Basin is a sedimentary basin located in the Argentine Northwest that started to accumulate sediments in the Early Cretaceous (Neocomian) and at present has sedimentary deposits reaching thicknesses of 5 km. The basin developed under conditions of extensional tectonics and rift-associated volcanism. An outcrop is a body of rock exposed at the surface of the Earth. Thus, some analogues of a region of interest can be relatively easy to examine when compared to the region of interest. While the foregoing example of the Pre-Salt Santos Basin and the Salta Basin may be quite extreme in terms of accessibility, locating an analogue that already has core and/or other data and/or that is more accessible and/or otherwise less complex to explore can be beneficial.

[00130] Referring to the system 300 of FIG. 3, the geological data 310 can include the outcrop interpretation data 316, water bottom data, etc. Various types of data may be available or become available for one or more analogues. In the system 300, an analogue framework may be utilized to find and/or assess one or more analogues.

[00131] As explained, geoscientists often seek comparisons for predicting and/or propagating information to areas where a lesser amount of information exists. As an example, a framework may help in discovering analogues based on properties or meta properties.

[00132] As an example, a framework may be a prospect analysis framework, where a prospect can be a region of interest, which may be an area of exploration in which hydrocarbons have been predicted to exist with some certainty as to recoverability. As an example, a prospect may be an anomaly, such as a geologic structure or a seismic amplitude anomaly, that may be recommended by one or more explorationists for drilling a well. Justification for drilling a prospect may be made by assembling evidence for an active petroleum system, or reasonable probability of encountering reservoir-quality rock, a trap of sufficient size, adequate sealing rock, and appropriate conditions for generation and migration of hydrocarbons to fill the trap. As an example, a group of prospects of a similar nature can form a play.

[00133] As explained, seismic data can be utilized for reconstruction of depositional environments, including paleodepositional environments (e.g., consider a paleoenvironment being an environment that has been preserved in the rock record at some time in the past). The study of turbidites, as localized deposits of high porosity resulting from seafloor instabilities, has facilitated exploration of deepwater deposits for hydrocarbon reservoirs. To further facilitate such exploration, one may study high- porosity ocean bottom deposits from deep currents, which can be of particular interest due to their often greater extent. Drift and contourite deposits have been recognized for their economic importance as well as for their relevance for climate change research.

[00134] Contourites are oceanic sedimentary deposits originated by the circulation of thermohaline oceanic currents of contour, named this way because they follow bathymetric curves. These currents may flow upwards, downwards, around or above obstacles or irregular topographies. In addition of taking part in the regulation of global temperatures and the distribution of the marine fauna, contour currents generate across-slope processes, that together with along-slope processes, dominate large part of the sedimentary dynamics in deep marine environments, either within the continental slope or the continental rise. Along-slope processes may generate drift or contouritic features. The latter are less known than the former, which are considered erosional terraces, contourite channels, abraded surfaces, moats and furrows.

[00135] In various instances, contourites, formed by oceanic currents which circulate along the continental slope, may be altered by “turbiditic” sedimentary events. Such turbiditic deposits may be formed by gravitational processes at the seabed sharpest slopes, transverse to the continental slope. Sediment transport phenomena and the existence of fossils in contournitic and turbidic sediments are traits that have implications in paleoclimatic, paleoceanographic aspects, as well as in the morphology of sediment within the oceanic margin, both kind of deposits are relevant as reservoirs of oil and other mineral resources.

[00136] In various environments, bottom currents are responsible for contourites formation. Such currents can be affected by variations in temperatures, salinity, tides, waves, wind, topography and physical barriers that have to pass through. Thermohaline currents are a common type of bottom current, they conform a global conveyor belt, which has relevance in distributing heat over the world, from the tropics to polar regions. Changes in thermohaline currents can cause climate changes. Thermohaline currents tend to begin on the Antarctic Ocean, most specifically in Weddell Sea. Considered as the coolest water, they separate from the salt phase and sink in the water mass, forming bottom currents. Such currents can flow eastward, affecting Pacific, Atlantic and Indian Oceans. In such regions, temperature starts to rise as latitude decreases and bottom currents turn into shallow-water bottom currents. As this happens, they can be enriched with salts and progressively increase in temperature. Subsequently, high evaporation cooling, where concentrated salt water temperature drops, they can again become denser. In the seas of Greenland and Norway, they can return to form deep undercurrents that flow south, to the west of the Atlantic Ocean.

[00137] A contourite deposits surface can range from small sizes (e.g., approximately 100 km²) to giant sizes (e.g., greater than 100,000 km²). Consider, as an example, the basin of Argentina that covers an area of approximately 1 ,000,000 km². On average, its width, ranges from tens to hundreds of kilometers and its thickness from tens to 2,000 meters. Such deposits can vary depending on factors such as sediment, stream velocity, availability of oxygen and others. The lithology of such deposits can be akin to pelagic sediments and hemipelagic and may be of a mixed nature between terrigenous, biogenic, chemogenic and volcanigenic composition. Grain sizes tend to be of a fine size from silt and sand but also can be of a gravel size.

[00138] While various approaches may utilize seismic data for detection of contourites, vertical resolution and low signal-to-noise ratio of seismic data often do not provide sufficient vertical resolution to allow for the mapping of depositional environments. In various approaches, indications for regional depositional environments can be obtained from seismic attributes; however, because of the resolution of wavelet-based signal processing, the impact of vertical averaging is a relevant consideration during interpretation. Various color-processing techniques for seismic data can provided sufficient vertical resolution as well as for representation of seismic data. For example, a color-processing technique can be applied to retrieve dynamic geologic processes from 3D seismic data where a workflow may be refined to create geologic analogues from seismic data. In various instances, an approach may involve combining findings from seismic data with oceanographic measurements for extraction of paleodepositional environments from the seismic data.

[00139] As an example, a framework can include features that can provide for integration of depositional environments obtained from color-processed seismic data and oceanographic measurements using the concept of geologic analogues. For example, consider a framework that can provide a paleo velocity meter from geologic analogues for ocean current interpretation. As mentioned, deep currents can lead to high-porosity ocean bottom deposits. As an example, a framework can provide for linking of ocean currents and depositional environments. Such a framework can facilitate one or more of various workflows, which can include, for example, model building, seismic interpretation, simulation, etc.

[00140] As an example, a framework can include or be operatively coupled to components for seismic interpretation for structural features (e.g., horizons, etc.). As explained, the PETREL framework provides various components for seismic interpretation.

[00141] As an example, a framework can include or be operatively coupled to components for color-processing of seismic data. For example, consider one or more of the features described in an article by Laake, (2015), “Structural interpretation in color — A new RGB processing application for seismic data,” Interpretation 3: SC1- SC8, which is incorporated by reference herein in its entirety. Also, consider US Patent No. 9,964,654, which is incorporated by reference herein in its entirety. As an example, a framework can implement structure-sharpened continuous red-green-blue (SRGB) color processing, for example, to allow for interpretation of seismic data for geologic features, to facilitate extraction of structures and/or geobodies along horizons, and to provide a visualization environment for the interpretation of geologic objects in their spatial context.

[00142] As an example, a framework can include or be operatively coupled to components to perform geologic process mapping from seismic data. For example, consider an article by Laake and Francis, “Geological Process Mapping from Seismic Data”, 77th EAGE Conference and Exhibition 2015, Jun 2015, Volume 2015, pp.1-5, which is incorporated by reference herein in its entirety. The article by Laake and Francis describes aspects of high-vertical-resolution processing of seismic data in a color domain that can provide relatively dense horizon images that can facility mapping of continuous geological processes and detection of discontinuous processes from seismic data. The article by Laake and Francis applied such an approach to phases of a mass flow cycle from a deepwater region of the Gulf of Mexico where long periods of smooth and quasi-continuous changes in the sediment deposition have been separated by two events of very sudden changes in the flow and deposition pattern where such sudden changes were resolved within a few 10s of meters. [00143] As an example, a framework can include or be operatively coupled to components for performing rapid reservoir detection. For example, consider an article by Laake et al., “Efficient Reservoir Detection — A Rapid Mapping Approach for Nearfield Exploration and Carbon Capture and Storage”, 83rd EAGE Annual Conference & Exhibition, Jun 2022, Volume 2022, pp.1-5, which is incorporated by reference herein in its entirety. The article by Laake et al., describes an approach using a regional subsurface model based on an integrated sequence stratigraphic interpretation of well logs and seismic data to provide regional horizons and gross depositional environment maps where the latter can be refined through geobody identification and extraction from color processed 3D seismic data, which can be calibrated with lithology logs. The article by Laake et al., provides a portfolio of age dated lithology-calibrated sand reservoir bodies at vertical and lateral resolution of the seismic data that can be entered into a risking process for ranking where various features can be used to identify potential sites for carbon capture and storage.

[00144] As an example, a framework can include or be operatively coupled to components for using geologic analogues. For example, consider a book chapter by Laake, 2022, “Geologic Analogs. In: Remote Sensing for Hydrocarbon Exploration. Springer Remote Sensing/Photogrammetry”, Springer, Cham. (DOI: 10.1007/978-3- 030-73319-3_12), which is incorporated by reference herein in its entirety.

[00145] As an example, a framework can provide for inferring direction and/or velocity of paleoocean bottom currents from seismic data where such a framework can utilize geologic analogues and, for example, integration with modern oceanographic measurements. For example, consider a workflow where color- processed seismic data provide erosional features at the ocean bottom that can be correlated with oceanographic measurements to validate the modern direction and velocity of the bottom current. Such an example can generate a calibrated model as a modern analogue to infer paleoocean bottom characteristics of erosional contourite features from subsurface seismic data. As an example, a framework may operate with or without wavelet-based seismic attributes. As explained, such wavelet-based seismic attributes may be limited in their vertical resolution and vertical spatial averaging, which can in various instances hide geologic features indicative of paleocurrents.

[00146] As an example, consider a scenario where a comparison can be made between a modern analogue from the northern Gulf of Mexico with an ancient analogue from the lower Paleogene of Flemish Pass offshore east Canada. In such an example, the comparison shows such level of similarity that a framework can consider the sedimentary processes largely similar, thus enabling inference of the direction of flow along with velocity of flow. As an example, a framework can provide for workflows that can consider contourite depositional features under lower energy conditions and the combination of depositional and erosional features, which can facilitate understanding of modern day and ancient formation of contourite depositional systems (CDS). As an example, a framework can operate using measurements of paleoocean bottom currents, which can impact the understanding of deepwater contourite reservoirs for hydrocarbon exploration and/or carbon sequestration and, for example, assist in the reconstruction of the paleoclimate as an input to one or more climate change models.

[00147] FIG. 8 shows an example of framework features 800 that can provide for integration of seismic interpretation and oceanographic techniques. As shown, seismic interpretation features can provide for determining modern seafloor texture and ancient seafloor texture while oceanographic features can provide for receipt of in-situ measurements of flow direction and velocity of a modern seafloor such that a framework can output inferred flow direction and/or velocity for an ancient seafloor. Further, as mentioned, associations may exist between velocity, grain size and structures or no structures, such that, for example, the framework features 800 can include oceanographic features that can provide for modern correlation of flow velocity, grain size and bedform at a seafloor such that a framework can output inferred ancient grain size. As an example, the framework features 800 can be implemented to perform a method that can provide for inferring one or more paleo flow properties from seismic data.

[00148] As to flow velocity, grain size, and bedform at a seafloor, consider an article by Rebesco et al. (2014), entitled “Contourites and associated sediments controlled by deep-water circulation processes: State-of-the-art and future considerations”, Marine Geology, 352, 1 11-154 (DOI:

10.1016/j.margeo.2014.03.011 ), which is incorporated by reference herein in its entirety.

[00149] As an example, a workflow for interpretation of seismic data for geologic features representative of deep ocean currents can commence with horizon interpretation where, for example, the amplitude texture of which is interpreted for depositional environments. In such an example, if the quality of the seismic data is sufficient, geologic features can be mapped within these depositional environments, one or more of which can be indicative of currents. At the seafloor one or more of such features can then be correlated with oceanographic measurements of the bottom current to correlate the direction and/or the velocity of the bottom flow with their representation in the seismic data.

[00150] FIG. 9 shows an example of a workflow 900 that includes a seismic data process and an oceanographic process, which can be combined to provide output. As shown, the workflow 900 can include a reception block 912 for receiving three- dimensional seismic data (e.g., volumetric data such as a seismic cube), a color processing block 914 for performing color processing on the seismic data, a horizon interpretation block 916 for interpreting one or more horizons in the seismic data, a depositional environment block 918 for determining a depositional environment using the color processing and the horizon interpretation, a feature detection block 920 for performing feature detection for the depositional environment, a decision block 922 for deciding whether features are present, a no reservoir structures block 924 (see, e.g., regions (1 ) and (3) of the plot 700) that follows from a decision that no features are present, a polygonal faults block 926 that follows from a decision that features are present, and a seal block 928 that follows from a decision that polygonal faults are present (see, e.g., region (5) of the plot 700). Additionally, the workflow 900 can include an oceanographic measurement block 932 for receiving oceanographic measurements (e.g., from one or more sources), a bottom current data determination block 934, a correlation block 940 for correlation of various features and one or more flow properties (e.g., velocity, etc.) where the correlation block 940 may follow from a decision that no polygonal faults are present (see, e.g., the “no” branch of the decision block 926), a clay and silt bedforms block 942, a sand and gravel bedforms block 944, a seal block 946 (see, e.g., region (4) of the plot 700), and a reservoir block 948 (see, e.g., region (2) of the plot 700). Accordingly, the workflow 900 may provide for making determinations as to types of regions that may exist in one or more environments. In the example of FIG. 9, blocks are present as to the five regions (1 ), (2), (3), (4), and (5) of the plot 700 of FIG. 7. As explained, flow velocity, grain size and structures or no structure may be associated.

[00151] FIG. 10 shows an example image of seismic data 1000 (e.g., a seismic slice in a seismic cube) that includes interpretations as may be generated via the workflow 900 of FIG. 9. In particular, the image of seismic data 1000 includes an interpreted seismic sequence generated using the workflow 900 to identify regions, including region (5) as a final seal, region (4) as internal seals, region (3) as no structure, region (2) as a reservoir, and region (1) as no structure. In the example of FIG. 10, the reservoir of region (2) may be of interest for development, for example, to produce hydrocarbons; noting that various processes may have evolved over time to form one or more seals to thereby form a trap (e.g., a seal) to accumulate hydrocarbons.

[00152] FIG. 11 shows various images 1100, labeled (a), (b), (c), (d), (e), and (f), which are grayscale versions of color images. Specifically, FIG. 11 shows mapping of current features from seismic data where the image (a) is a regional overview in color- processed data; image (b) is a detail of contourites in color-processed data; image (c) is the same detail as in image (b) in RMS amplitude attribute; image (d) is a vertical section in post-stack Kirchhoff Time Migration data, where arrows indicate horizon shown in images (b) and (c); image (e) is a section in a green component of color- processed data; and image (f) is a section in RMS amplitude attribute data. The images 1100 in FIG. 11 are based on example data from the Flemish Pass, Canada, which is off the coast of Canada’s Newfoundland and Labrador province, in the North Atlantic Ocean.

[00153] As explained, seismic data can be of varied resolution. In the workflow 900 of FIG. 9, seismic data may be assessed and/or processed with respect to resolution, for example, to help assure sufficient resolution of the seismic data. In various approaches, a root mean square (RMS) amplitude attribute can be used to map and characterize depositional features in seismic data. Such an approach may work well when studying a map representation (see, e.g., image (c) of FIG. 11). However, a vertical section as shown in image (f) of FIG. 11 shows that the RMS attribute blurs the data resulting from vertical averaging when compared to the input data of image (d) of FIG. 11. This effect limits the vertical of RMS amplitude attribute data to approximately 50 m, which can be too coarse for the mapping of depositional features.

[00154] As shown in FIG. 11 , a color-processing technique can provide for higher vertical resolution, for example, consider at one to two samples or 50 m to 10 m (e.g., less than 50 m) as shown in image (e) of FIG. 11. The mapping capabilities of image (b) of FIG. 11 are substantially enhanced because of the increased dynamic range. Furthermore, color-processing can correlate adjacent data slices, which can increase sensitivity to subtle lithologic changes. Therefore, a framework can implement colorprocessing (e.g., color-processed data) in a workflow for inferring various flow properties.

[00155] FIG. 12 shows example images 1210 and 1230 where the image 1210 shows a map of ocean currents in the Gulf of Mexico where a circle indicates a particular location and where the image 1230 is a seafloor texture obtained from rendering color-processed seismic data in 3D.

[00156] As explained, a framework can utilize modern and ancient geologic analogues for indications of bottom currents. As explained, a workflow can include mapping of features at the seafloor at a location such as, for example, the location indicated in FIG. 12, which is near Green Knoll in the USA Gulf of Mexico. At this location, strong bottom currents have been observed in oceanographic measurements. In the image 1210, patterns of surface and bottom currents in the Gulf of Mexico are shown. At this location, a strong bottom current is guided by the edge of the salt canopy of the Sigsbee Escarpment. The Green Knoll diapir protrudes from the seafloor, thus presenting an obstacle to the bottom current. As indicated in the image 1230, the current forms a horseshoe cortex and creates mega furrows on the side of the salt diapir. The shape and orientation of these furrows can be correlated with the oceanographic measurements, which provide a flow velocity of up to 1 m/s.

[00157] FIG. 13 shows example images 1310 and 1330 where the image 1310 is a regional overview of a shelf setting at the Flemish Pass and where the image 1330 is a detail of the sandy contourites indicated by the box in the regional overview image 1310.

[00158] The images 1310 and 1330 are from a seismic data set from the Flemish Pass offshore Newfoundland, Canada, where deepwater deposits from the early Paleogene exist. The images 1310 and 1330 are grayscale versions of color images. In the regional overview of the image 1310, hard rocks on the shelf slope (e.g., intense colors in color version), sandy sediments in the proximal part of the Orphan Basin (e.g., light colors in color version) and muddy sediments (e.g., brownish colors in color version) at the Orphan Basin floor are shown. The interpretation for a high shale content is supported by intense polygonal faulting indicative of obstruction of the dewatering through a tight rock. [00159] FIG. 14 shows example images 1410 and 1430, where the image 1410 is an image of a modern structural feature and where the image 1430 is an image of an ancient structural feature, which provide for a comparison of modern and ancient analogues. In FIG. 14, the image 1430 of the Flemish Pass resembles the image 1410 of the modern depositional environment observed in the present-day Gulf of Mexico. Even when considering the substantially lower vertical resolution of the subsurface data and compaction that affects the vertical dimension of the contourites, the modern analogue can be utilized to infer the velocity of the ancient bottom current, for example, as being of the same order of magnitude. As an example, a framework, via images such as the example images 1410 and 1430, can utilize geologic features observed in the subsurface to infer one or more flow properties.

[00160] As an example, a framework that can generate information as to one or more ocean current properties (e.g., flow properties such as direction and velocity and/or flow properties as to material) can help in assessing the chance of success for high-porosity deposits in the subsurface. For example, consider application to hydrocarbon exploration, specifically near-field exploration. As an example, a framework can generate paleo-ocean bottom current patterns to assist in modeling porosity of potential reservoirs, for example, inferring the sand portion in the reservoir rock. As an example, a framework can help to focus on geologic structures and/or local geobodies such as mass transport systems. As an example, a framework can assist in one or more carbon sequestration workflows, for example, where an interest can be in large horizontal areas (so-called regional saline aquifers). As an example, a framework can provide information germane to contourites, which can be of interest in hydrocarbon applications due to their quite large regional extent.

[00161] For nearfield exploration often 3D seismic data are available but not presently used due to absence of high-resolution extraction, which in turn confounds interpretation of ocean bottom current patterns. Often insufficient data are available or data have not (or not yet) been interpreted at sufficient detail. Petroleum system modeling, which may provide a chance of success mapping, can benefit from determinations as to one or more flow properties, rather than relying solely on assumptions about ocean flow (e.g., currents) where such assumptions may lead to severe errors.

[00162] As an example, a framework can provide for paleo current mapping, which can assist petroleum system modeling with the input of depositional environment maps, which allows for creation of more realistic chance of success maps. Modern and ancient analogues created by a framework can be collected, for example, in an analogue library (e.g., database), which may be utilized by one or more frameworks, etc. For example, consider an automated approach to determining one or more flow properties using such a library. As an example, a framework may utilize one or more machine learning (ML) techniques. In such an example, data can be assessed with respect to patterns, which may aim to match ancient and modern features. In such an example, one or more flow properties can be assigned to ancient features and/or one or more instructions may be issued for acquiring data as to modern flow properties in a region such that one or more flow properties can be assigned to one or more ancient features. As an example, a framework may provide for physicsbased and/or data-based modeling of one or more flow properties. As to a data-based approach to modeling, consider training one or more machine learning models using particular data where gaps in the data can be filled using one or more trained machine learning models. In such an approach, the ML may include various inputs such as, for example, temperature, salinity, etc., such that output may be more accurate. As explained, a physics-based approach may be utilized, alone or in combination with a data-based approach. For example, consider utilizing temperature in a physics-based model that can provide input or guidance for an ML model.

[00163] As an example, a framework may operate in an automated manner and/or a semi-automated manner. As an example, a framework may provide for screening, ranking, etc., various regions through access to data that includes ancient and modern data. As explained, a framework may implement one or more rapid assessment techniques.

[00164] As explained, vertical resolution of seismic interpretation can in various instances be insufficient for accurate sedimentological analysis of seismic data. For example, attributes can involve vertical averaging during attribute generation which results in averaging over vertical intervals much thicker than that demanded for sedimentological work. Further, the use of seismic 2D data (e.g., vertical or angled slices) does not readily allow for lateral depositional mapping. As a result, depositional environments that can depend on ocean bottom currents can be misinterpreted or completely missed. As explained, a framework can include components for mapping depositional environments in 3D, which provides for lateral depositional mapping. [00165] As explained, a framework may include features for machine learning and/or be operatively coupled to a platform that includes such features. As an example, a framework can create geologic analogues for depositional environments, where these can be captured as maps, geobodies and/or sections. As an example, classes of analogues can be generated to train one or more ML models for individual classes to enable automatic execution of a workflow, given a sufficiently large library of analogues. As explained, given a library, one or more ML techniques can be employed to facilitate finding one or more suitable (e.g., top ranked) analogues from the library for a scenario at hand. In such an example, the one or more suitable analogues can be presented to a geoscientist or other individual working on a project to expedite project execution. As an example, an ML approach can help to expedite interpretation and increase objectivity of the interpretation, for example, by using an analogue library.

[00166] As explained, a workflow can include using a framework such as the PETREL framework, which can provide for access to high-resolution lithology mapping, depositional environment mapping and geobody extraction available at single seismic sample resolution. As an example, a framework for flow determinations based on analogues can be integrated into and/or operatively coupled to the PETREL framework, which may, for example, be implemented in the DELFI environment.

[00167] As an example, the PETREL framework can include or be operatively coupled to an eXchroma chromatic geology extraction component (e.g, plug-in) (SLB, Houston, Texas). In such an example, the eXchroma component can enhances amplitude heterogeneities present in 3D seismic data, for example, to a resolution of the order of approximately 5 meters. Such a component can correlate individual amplitude sample values as intensity of red, green, and blue layers and combine them into a single layer where they are repeated for each sample interval until a full 3D seismic cube is represented. In such an approach, the component can facility delineating and isolating geologic features that may have been previously indiscernible from seismic noise. As an example, an ML approach may be applied to seismic data that have been processed using the eXchroma component. In such an approach, the seismic data can be of a suitable resolution for purposes of flow property determinations.

[00168] As explained, an ML or other artificial intelligence (Al) technique can be implemented in PETREL or as a separate shared component. Such a component may provide for one or more workflows, processes, etc. For example, consider generating, administering and consuming geologic analogues generated using a framework where a combination of information from the analogues in a database allows for creation of a large area, possibly global map (or maps), of paleo-currents.

[00169] As explained, today’s ocean currents can be measured. As to bottom currents, these may be more challenging to measure. As an example, one or more ML techniques may be applied to various measurements, which may be at various depths at various locations. In such an example, an ML model may provide output for a particular ocean bottom location where, for example, actual measurements are not available. Such a model may be a predictive model that can utilize measurements to discern current patterns, which may be multidimensional current patterns. As explained, ML can also be utilized as to patterns between modern and ancient features as may be present in seismic data and/or other data where, for example, three- dimensional seismic data may be processed for increased resolution to discern ancient features that, today, may be at some depth below a surface, whether that surface is presently a land surface or an ocean bottom surface. Information as to present and ancient ocean currents is increasing in relevance in the context of global climate modeling. As explained, currents can depend on temperature, salinity, location, rotation of the Earth, etc., which can be factors relevant to climate modeling. [00170] As an example, an ML approach can provide for analogue determinations, which may facilitate an understanding of climate change processes based at least in part on ancient currents.

[00171] As an example, an analogue library can include various types of analogues, which can extend beyond ancient ocean bottom analogues. As an example, assessments for analogues may be based on factors such as, for example, grain size, sortedness, energy, etc. As to some examples, consider glacial as being arenites with pebbles from till, moraines, drumlins; glaciofluvial as being arenites with pebbles from outwash plain, Esker, outwash delta/fan; glaciolacustrine as being poorly sorted like till, varves; lacustrine as being low energy particles size, well sorted (silt and clays highly laminated), occasionally carbonates, oil gas, uranium, strontium; lacustrine delta as being similar to river dominated deltas (fine grained to coarse deposits, gravelly fan); estuarine as being fine grained (clay silt) from brackish water (mix of marine and fluvial particles), high organic matter (o.m.), finer than deltaic deposits; fluvial as being sandstones channel (point bar), levee, muddy backswamp; delta plain as being alluvial dep; alluvial as being floodplains, deltas, banks overflow; desert as being dunes, dry lake (halite, gypsum, borax), flash floods; Eolian as being dunes, loess; evaporite basin as being calcite, gypsum, anhydrite, halite, potassium and magnesium salt; marine delta as being fluvial channels (incised valley), coarse topsets, sandy foresets, silty bottom sets, sandy mouth bars; marine delta front as being sandstone, siltstone, part of a delta that faces a mix of fluvial and marine processes; shoreline as being sand, blocks, Hummocky cross-stratification (HCS); supratidal as being sabkha, desiccation cracks, silty clay with roots, wood debris and burrows; subaerial as being sabkha, desiccation cracks, silty clay with roots, wood debris and burrows; intertidal as being cross laminated sandstones, flaser, mudstones; subtidal as being sandy mud and silt interlaminated, few burrows, ripple bedding, cross bedding; shallow marine as being carbonate or siliciclastic, coarse grains, sometimes evaporites (gypsum and anhydrite), cross stratifications, desiccation cracks, stromatolites; shallow marine shelf as being sandy bar shoal, coarse organic carbonates (skeletons), CaCOs precipitate; restricted marine as being washover (sandy) in the muddy rich lagoon, tidal channel, cross bedding, marsh, lagoon, tidal flat (sand lenses), tidal delta (sand); marine shelf as being HCS for storm dominated, burrowed shelf mud, packestone/wackestone carbonate, tempestite; deep marine shelf as being clay, shale, chalk mudstone carbonate, slump, tempestite; deep marine slope as being slump, turbidites of detrical elements; deep marine trench as being accretion prism; deep marine as being turbidites, contourites, pelagic sediments, diatoms and radiolarian opal, carbonate from foram and cocolithophorids (chalk); open marine as being pelagic (ooze above calcite compensation depth (CCD)), biogenous (pellets, diatoms and radiolaria tests), silty contourite; and abyssal plain as being flat area with clastic from tubitites or contourites, carbonated mudstone with foram accumulation.

[00172] As an example, a system can utilize petrophysical information such as, for example, porosity, permeability, mineralogy mix, pore pressure, net-to-gross (NTG), etc. As an example, analogues can be stored in a database with entries for such data.

[00173] FIG. 15 shows an example of a graphical user interface (GUI) 1500 that can include one or more graphical controls such as, for example, one or more of the graphical controls 1510, 1530 and 1550. In the example of FIG. 15, a user may select one or more of the graphical controls 1510, 1530 and 1550 to form a query or queries for a system, which may be issued via one or more API calls, etc.

[00174] As shown, a user can utilize a human input device (HID) such as a touch screen, a mouse, a touchpad, a stylus, a microphone, hand motion sensors, etc., to select a feature in the graphical control 1510 that represents various deposition environments. As to the graphical control 1530, a user can utilize a HID to select an age or an age range using one or more sliders, text input fields, etc. As to the graphical control 1550, it can represent a flow property such as, for example, flow velocity (e.g. , current velocity); noting that a grain size and/or other material property may be provided additionally or alternatively to flow velocity. For example, consider the rendering of the environment as corresponding to a particular ancient age per the graphical control 1530 where a location on the shelf is selected via the graphical control 1510, which can cause the graphical control 1550 to indicate a corresponding current velocity (e.g., and/or current direction).

[00175] As an example, the GUI 1500 may be utilized to formulate a query that may be a dynamic query that is issued to a search engine that generates search results that can be listed and/or visualized. Such search result may provide analogues where, for example, the analogues can be ranked with respect to relevance to a query. [00176] In the example of FIG. 15, the GUI 1500 may be utilized to find one or more analogues for a subsurface geological region where at least some data (e.g., log data, imagery, etc.) exist. In such an example, a user can generate a query using at least a portion of the data such that one or more analogues are found using a search engine. As an example, search results may be provided in one or more types of formats. For example, consider a tabular format, a graphical format, etc.

[00177] While various examples consider matching a subsurface ancient water bottom location to a modern water bottom location, a framework may provide for matching a subsurface ancient water bottom location to another subsurface ancient water bottom location. In such an example, where one of the subsurface ancient water bottom locations is associated with one or more flow properties, the framework may link the one or more flow properties. In such an approach, a chain can be created, for example, from a modern water bottom location to one or more subsurface ancient water bottom locations.

[00178] As an example, a method may include associating a subsurface ancient water bottom location to a modern water bottom location using one or more techniques. For example, consider comparing one or more structural features represented in seismic data for a subsurface ancient water bottom location to one or more structural features in a modern water bottom location. Such comparing may utilize one or more techniques that may aim to align ancient features with modern features. For example, consider a method that may include pattern matching of an ancient seismic horizon to modern digital imagery. In such an example, the modern digital imagery may be in a database where a search engine may provide for pattern matching (e.g., using a search index, etc ). As an example, one or more ML-based approaches may be utilized to associate one or more structural features represented in seismic data of an ancient environment to one or more structural features represented in imagery (e.g., digital imagery) of a modern environment. As an example, one or more ML models suitable for processing two-dimensional images may be implemented, where such two-dimensional images may be represented using one or more channels (e.g., black and white, grayscale, color, etc.). As an example, a channel associated with an image may encode information, which may correspond to one or more characteristics of one or more structural features (e.g., consider heightmaps, shadows cast by a lighting technique, flow properties, etc.).

[00179] FIG. 16 shows an example of a GUI 1600 that can include various sub- GUIs such as, for example, one or more of GUIs 1610 and 1630. As an example, search results may be represented in the GUI 1610 using a world map where circles, heat map colors, etc., indicate search results. In such an example, a user may utilize a HID to navigate the search results where, for example, upon selection of a search result, data associated with the search result can be rendered using one or more other GUIs such as, for example, the GUI 1630. For example, the GUI 1630 may automatically adjust to the search result and indicate one or more flow properties (e.g., current velocity, current direction, etc.).

[00180] FIG. 17 shows an example of a seismic image 1700 where various features may be interpreted features that may be extracted for training of one or more ML models. For example, consider training an ML model that can operate to create a feature inventory. As shown, features may include a moat, polygonal faults, sediment waves, and mud waves; noting that one or more additional or alternative features may be identified and utilized for training one or more ML models.

[00181] As an example, one or more ML techniques may be utilized with respect to grain size, for example, consider training one or more ML models to detect features where such features may be related to grain size. In such an example, a seismic image may be processed to generate grain size information. As explained, seismic imaging by itself tends to be too coarse to directly resolve grain size information. As an example, one or more ML models may be utilized to identify features within seismic data and associate such features with likely grain sizes. In such an example, knowledge of physical processes may be taken into account, for example, consider the physical processes described with respect to the graphic 500 of FIG. 5.

[00182] As an example, an ML model may be trained to detect a mote in seismic data where, for example, a mote may be substantially parallel to a water current and, for example, substantially perpendicular to a slope. As explained, physical processes can involve material falling along a slope to encounter a water current, which may be a vortex with a direction that depends on the Coriolis force (e.g., clockwise or counterclockwise). As an example, an ML model may provide for inferring grain size from seismic data in a geospatial manner, which may be part of a workflow that occurs prior to inversion of seismic data (e.g., consider acoustic velocity model-based inversion, etc.) and/or prior to drilling.

[00183] FIG. 18 shows an example of a method 1800 that includes a selection block 1810 for selecting a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; an association block 1820 for associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modem water bottom location; and a determination block 1830 for determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

[00184] As an example, grain size may be a flow property as flow and grain size may be associated, for example, as explained with respect to the plot 700 of FIG. 7, the framework features 800 of FIG. 8, the workflow 900 of FIG. 9, etc.

[00185] The method 1800 is shown in FIG. 18 in association with various computer-readable media (CRM) blocks 1811 , 1821 and 1831. Such blocks generally include instructions suitable for execution by one or more processors (or processor 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 1800. As an example, a computer-readable medium (CRM) may be a computer-readable storage medium that is non-transitory and that is not a carrier wave. As an example, one or more of the blocks 1811 , 1821 and 1813 may be in the form processor-executable instructions, for example, consider the one or more sets of instructions 270 of the system 250 of FIG. 2, etc.

[00186] Machine learning (ML) can be considered an artificial intelligence (Al) technology where a computational framework can train an ML model using training data to generate a trained ML model. A trained ML model may be utilized for one or more purposes. For example, consider a predictive trained ML model, a decision making trained ML model, etc.

[00187] As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naive Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naive Bayes, multinomial naive Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.

[00188] As an example, a machine model, which may be an ML model, may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k- medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.

[00189] As an example, a trained ML model (e.g., a trained ML tool that includes hardware, etc.) can be utilized for one or more tasks. As an example, various types of data may be acquired and optionally stored, which may provide for training one or more ML models, for retraining one or more ML models, for further training of one or more ML models, and/or for offline analysis, etc.

[00190] As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open-source software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley Al Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-leam), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO Al framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook Al Research Lab (FAIR), Facebook, Inc., Menlo Park, California).

[00191] As an example, a method can include selecting a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location. In such an example, the method can include processing the three- dimensional seismic data using a color scheme to discern at least one of the one or more structural features. In such an example, the processing can effectively refine vertical resolution of the three-dimensional seismic data. For example, consider the processing effectively refining the vertical resolution of the three-dimension seismic data to less than 50 meters. In such an approach, the three-dimensional seismic data can be of a resolution sufficient to discern one or more structural features as may be associated with a water bottom current.

[00192] As an example, a method can include associating locations by accessing an analogue database. In such an example, the associating can include implementing a machine learning model. For example, consider a machine learning model that associates one or more structural features of a subsurface ancient water bottom location to one or more structural features of a modern water bottom location.

[00193] As an example, a method can include associating a subsurface ancient water bottom location to more than one modern water bottom locations. For example, consider an approach that can provide for ranking locations where a selection may be made amongst ranked candidate locations.

[00194] As an example, a method can include associating a subsurface ancient water bottom location to one or more other subsurface ancient water bottom locations. In such an example, the one or more other subsurface ancient water bottom locations may be associated with one or more flow properties.

[00195] As an example, a method can include associating a subsurface ancient water bottom location to at least one modern water bottom location and to at least one other subsurface ancient water bottom location. [00196] As an example, one or more structural features can correspond to one or more structural features of a horizon. In such an example, a horizon may be discerned from three-dimensional seismic data where the horizon has a lateral extent and vertically extending features, noting that a horizon may be horizontal or may be dipping (e.g., at an angle to horizontal). In various instances, a surface that was at one time substantially horizontal may have become overtime non-horizontal (e.g., due to geological processes, etc.).

[00197] As an example, one or more structural features can include contourites. As explained, contourites can be a result of water movements such as water currents. [00198] As an example, a subsurface ancient water bottom location and a modern water bottom location can be different locations. For example, the different locations can differ as to depth. For example, the different locations can differ as to depth by at least 50 meters. As explained with respect to the images 1410 and 1430 of FIG. 14, an ancient water bottom can be 500 meters below the ocean bottom while a modern water bottom is at the ocean bottom.

[00199] As an example, one or more ancient flow properties can include one or more of velocity, velocity direction, and grain size. For example, a water current can be defined as having a velocity and a direction. Given a velocity and a direction, over time, such a water current can form one or more of various structural features, which may be discerned through one or more imaging techniques. For example, seismic imaging can discern subsurface features while visual imaging and/or other imaging may discern water bottom features of a present day water bottom. As an example, seismic imaging (e.g., acoustic imaging) may be utilized to discern features of a present-day water bottom. Further, as explained, velocity can be associated with grain size and/or one or more other material properties that may be relevant to transport of material (e.g., grains, etc.).

[00200] As an example, a method can include, based at least in part on one or more ancient flow properties of a subsurface ancient water bottom location, assessing the subsurface ancient water bottom location for presence of hydrocarbons.

[00201] As an example, a method can include predicting one or more modern flow properties of a modern water bottom location based on one or more modern flow properties of one or more other modern water bottom locations. As explained, a physics-based model and/or a data-based model may be utilized to predict one or more flow properties, for example, given one or more flow properties for one or more other locations, which may be, for example, neighboring locations.

[00202] As an example, a system can include one or more processors; a memory accessible to at least one of the one or more processors; and processor-executable instructions stored in the memory and executable to instruct the system to: select a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

[00203] As an example, one or more non-transitory computer-readable storage media can include processor-executable instructions to instruct a computing system to: select a subsurface ancient water bottom location that has corresponding three- dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three- dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

[00204] As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.

[00205] In some embodiments, a method or methods may be executed by a computing system. FIG. 19 shows an example of a system 1900 that can include one or more computing systems 1901-1 , 1901-2, 1901-3 and 1901-4, which may be operatively coupled via one or more networks 1909, which may include wired and/or wireless networks. As shown, the system 1900 can include one or more other components 1908. [00206] As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of FIG. 19, the computer system 1901-1 can include one or more modules 1902, 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.).

[00207] As an example, a module may be executed independently, or in coordination with, one or more processors 1904, which is (or are) operatively coupled to one or more storage media 1906 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1904 can be operatively coupled to at least one of one or more network interface 1907. In such an example, the computer system 1901-1 can transmit and/or receive information, for example, via the one or more networks 1909 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

[00208] As an example, the computer system 1901-1 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 1901-2, etc. A device may be located in a physical location that differs from that of the computer system 1901 -1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., serverfarm, etc.), a rig location, a wellsite location, a downhole location, etc.

[00209] 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.

[00210] As an example, the storage media 1906 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.

[00211] 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.

[00212] 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.

[00213] 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.

[00214] 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.

[00215] 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.

[00216] 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).

[00217] 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.).

[00218] 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.

Claims

CLAIMS What is claimed is:

1. A method comprising: selecting a subsurface ancient water bottom location that comprises corresponding three-dimensional seismic data; associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

2. The method of claim 1 , comprising processing the three-dimensional seismic data using a color scheme to discern at least one of the one or more structural features.

3. The method of claim 2, wherein the processing effectively refines vertical resolution of the three-dimensional seismic data.

4. The method of claim 3, wherein the processing effectively refines the vertical resolution of the three-dimension seismic data to less than 50 meters.

5. The method of claim 1 , wherein the associating comprises accessing an analogue database.

6. The method of claim 5, wherein the associating comprises implementing a machine learning model.

7. The method of claim 6, wherein the machine learning model associates the one or more structural features of the subsurface ancient water bottom location to one or more structural features of the modern water bottom location.

8. The method of claim 1 , wherein the associating comprises associating the subsurface ancient water bottom location to more than one modern water bottom location.

9. The method of claim 1 , further comprising associating the subsurface ancient water bottom location to one or more other subsurface ancient water bottom locations.

10. The method of claim 1 , wherein the associating comprises associating the subsurface ancient water bottom location to at least one modern water bottom location and to at least one other subsurface ancient water bottom location.

11 . The method of claim 1 , wherein the one or more structural features correspond to one or more structural features of a horizon.

12. The method of claim 1 , wherein the one or more structural features comprise contourites.

13. The method of claim 1 , wherein the subsurface ancient water bottom location and the modern water bottom location are different locations.

14. The method of claim 13, wherein the different locations differ as to depth.

15. The method of claim 14, wherein the different locations differ as to depth by at least 50 meters.

16. The method of claim 1 , wherein the one or more ancient flow properties comprise one or more of velocity, velocity direction, and grain size.

17. The method of claim 1 , comprising, based at least in part on the one or more ancient flow properties of the subsurface ancient water bottom location, assessing the subsurface ancient water bottom location for presence of hydrocarbons.

18. The method of claim 1 , comprising predicting the one or more modern flow properties of the modern water bottom location based on one or more modern flow properties of one or more other modern water bottom locations.

19. A system comprising: one or more processors; a memory accessible to at least one of the one or more processors; and processor-executable instructions stored in the memory and executable to instruct the system to: select a subsurface ancient water bottom location that comprises corresponding three-dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.

20. One or more computer-readable storage media comprising processorexecutable instructions to instruct a computing system to: select a subsurface ancient water bottom location that comprises corresponding three-dimensional seismic data; associate the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determine one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modem water bottom location.