Rapid 3D reservoir contextualization

Abstract

A method for contextualizing a 3D reservoir includes receiving input data corresponding to the 3D reservoir. The method also includes processing and interpreting the input data to produce stratigraphic and / or structural insights. The method also includes processing and interpreting the input data to produce petrophysical insights. The method also includes determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights. The method also includes determining depositional facies of the 3D reservoir based upon the structure. The method also includes determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies.

Description

RAPID 3D RESERVOIR CONTEXTUALIZATIONCross-Reference to Related Applications

[0001] This patent application claims priority to U.S. Provisional Patent Application No. 63 / 620,379, filed on January 12, 2024, which is incorporated by reference herein in its entirety.Background

[0002] Currently, the generation of geological insights is too time-consuming to be helpful for real time drilling decisions such as geosteering, fluid and rock sampling locations and sampling frequency, completion strategy, and / or reservoir compartments understanding. Therefore, what is needed is an improved system and method for generating geological insights. More particularly, what is needed is an improved system and method with 3D contextualization of reservoir data.Summary

[0003] A method for contextualizing a 3D reservoir is disclosed. The method includes receiving input data corresponding to the 3D reservoir. The method also includes processing and interpreting the input data to produce stratigraphic and / or structural insights. The method also includes processing and interpreting the input data to produce petrophysical insights. The method also includes determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights. The method also includes determining depositional facies of the 3D reservoir based upon the structure. The method also includes determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving input data corresponding to the 3D reservoir. The input data includes well log data and / or seismic data. The well log data includes borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, sonic data, and / or deep resistivity data. The operations also include processing and interpreting the input data to producestratigraphic and / or structural insights. The stratigraphic and / or structural insights include a sedimentary structure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir. The operations also include processing and interpreting the input data to produce petrophysical insights. Processing and interpreting the input data to produce the petrophysical insights includes selecting and determining values for one or more parameters based upon the input data. The operations also include determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights. The operations also include determining depositional facies of the 3D reservoir based upon the structure. The operations also include determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies.

[0005] A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving input data corresponding to the 3D reservoir. The input data includes well log data and / or seismic data. The well log data includes borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, sonic data, and / or deep resistivity data. The operations also include processing and interpreting the input data to produce stratigraphic and / or structural insights. The stratigraphic and / or structural insights include a sedimentary structure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir. The input data is processed and interpreted to produce the stratigraphic and / or structural insights by detecting and labeling the grain size trends using a first machine learning method. The first machine learning method includes a transfer learning U-Net architecture. The input data is also processed and interpreted to produce the stratigraphic and / or structural insights by extracting a zonation related to bedding features from the borehole images using a second machine learning method. The second machine learning method includes a convolutional neural network and ResNet architecture. The bedding features includes tabular sets, zigzags, scallops, trough sets, and / or rippleform. The operations also include processing and interpreting the input data to produce petrophysical insights. Processing and interpreting the input data to produce the petrophysical insights includes selecting and determining values for one or more parameters based upon the input data. The operations also include determining a structure of the 3D reservoir basedupon the input data, the stratigraphic and / or structural insights, and the petrophysical insights. The operations also include determining depositional facies of the 3D reservoir based upon the structure. The operations also include determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies. The operations also include displaying an output of the petrophysical property distributions. The operations also include performing a drilling operation in response to the petrophysical property distributions.

[0006] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0007] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0008] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0009] Figures 2A-2C illustrates a digital geology workflow, according to an embodiment.

[0010] Figure 3 illustrates a flowchart of a method for contextualizing a 3D reservoir, according to an embodiment.

[0011] Figures 4A-4C illustrate automatic stratigraphic insights, according to an embodiment.

[0012] Figures 5A-5D illustrate examples of 3D stratigraphic models, according to an embodiment.

[0013] Figures 6A-6C illustrate input data used for 3D modeling, according to an embodiment.

[0014] Figure 7 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.Detailed Description

[0015] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention.However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0016] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0017] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0018] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.

[0019] The method described herein is a combination of techniques accelerating both interpretation and 3D modeling to help with the generation of geological and petrophysical insights for operation decision making. The method accelerates the whole pipeline: from borehole images and logs to 3D modeling. It also includes data processing and interpretation. It is made to integrate the acquired data efficiently in 3D.

[0020] The method is a digital geology data flow made of cloud-based applications delivering fast geological and petrophysical insights that can directly be integrated in 3D near wellbore modeling. It involves the automated orchestration and coordination of interpretation products: (1) automatic processing and dip picking from borehole images, deep resistivity, and sonic, (2) automatic interpretations of grain size trend and sedimentary geometry from logs and borehole images to obtain quick insights on stratigraphy and depositional environments, (3) automatic method to retrieve from a datastore, the ensemble of 3D stratigraphic models that best explain the acquired well log data, (4) automatic computation of petrophysical properties, and (5) 3D meshless smart structure modelling technique to contextualize rapidly these wellbore insights and 3D analogues within 3D space.

[0021] As a result, the method may provide a transformed operation’ s experience for subsurface 3D contextualization. More particularly, the method may provide data valorization, thus enhancing acquisition tools as the data undergoes processing, interpretation, and integration into 3D models within a seamless workflow. The method may also provide acceleration and de-risking operational decisions through rapid data interpretation following its acquisition. The method may also provide reduction of the need for human supervision, thereby minimizing the mobilization of experts and expediting interpretation.System Overview

[0022] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0023] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic dataand other information provided per the components 112 and 114 may be input to the simulation component 120.

[0024] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0025] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object -based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0026] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0027] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc ). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc ).

[0028] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.). The step with dynamic modeling and simulation is one application of the 3D reservoir contextualization workflow. Other applications may be performed using 3D geological and petrophysical models, stimulation, completion design, etc. The dynamic reservoir evaluation may be performed in wellbore insights (WBI), INTERSECT™, ECLIPSE™, and / or PETREL®.

[0029] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0030] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model -centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

[0031] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0032] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0033] As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0034] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0035] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0036] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0037] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be aworkflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).Digital Geology Workflow

[0038] Figures 2A-2C illustrate a digital geology workflow, according to an embodiment. More particularly, Figures 2A-2C illustrate a dynamic reservoir evaluation with pressure data contextualization. The present disclosure provides an innovative cloud-based method that focuses on interpretation solutions and the seamless link between borehole data and their 3D contextualization. The method may enable more accurate and efficient interpretation of subsurface reservoir data (e g., including seismic data), which may help with operational decision making. An example of an application of the method is dynamic reservoir evaluation where an ensemble of 3D geological and petrophysical scenarios may be useful for pressure measurements contextualization (as shown in the example in Figures 2A-2C). In this application, the method may accurately model the structural complexity and the facies complexity in the vicinity of the well. The method may also integrate the information coming from the acquired logs and seismic data.Exemplary Method

[0039] Figure 3 illustrates a flowchart of a method 300 for contextualizing a 3D reservoir for operations decision making, according to an embodiment. An illustrative order of the method 300 is provided below; however, one or more portions of the method 300 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 300 may be performed using a computing system (described below).

[0040] The method 300 may include receiving input data corresponding to the 3D reservoir, as at 305. The input data may be or include well log data and / or seismic data. The well log data may be or include borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, and / or sonic data.

[0041] The method 300 may also include processing and interpreting the input data to produce stratigraphic and / or structural insights, as at 310. The stratigraphic and / or structural insights maybe or include a sedimentary structure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir. The input data may be processed and interpreted to produce the stratigraphic and / or structural insights by detecting and labeling the grain size trends using a first machine learning method. The first machine learning method may be or include a transfer learning UNet architecture. The first machine learning method may perform a (e.g., first) run on the gamma ray data to identify zones of blocky sands and fining and / or coarsening upward sequences. The first machine learning method may also or instead perform a (e.g., second) run on the borehole images to refine an analysis of the grain size trends in the zones to identify small- scale variations in the grain size trends. In an embodiment, fluid and / or rock sampling locations and frequency may be determined based upon the analysis and / or the small-scale variations. The input data may also or instead be processed and interpreted to produce the stratigraphic and / or structural insights by extracting a zonation related to bedding features from the borehole images using a second machine learning method. The second machine learning method may be or include a convolutional neural network and ResNet architecture. The bedding features may be or include tabular sets, zigzags, scallops, trough sets, and / or rippleform.

[0042] The method 300 may also include processing and interpreting the input data to produce petrophysical insights, as at 315. Processing and interpreting the input data to produce the petrophysical insights may include selecting and / or determining values for one or more parameters based upon the input data. The one or more parameters may include a formation water salinity (FSAL) parameter in response to the input data comprising the spectroscopy data but not the Rt scanner data. The one or more parameters may also or instead include the FSAL parameter, a horizontal shale resistivity parameter, and a vertical shale resistivity parameter in response to the input data comprising the spectroscopy data and the Rt scanner data. The one or more parameters may also or instead include the FSAL parameter, a gamma ray (GR) clean parameter, and a GR shale parameter in response to the input data not comprising the spectroscopy data. The one or more parameters may also or instead include a T2 cut off parameter in response to the input data comprising the NMR data.

[0043] The method 300 may also include determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and / or the petrophysical insights, as at 320.

[0044] The method 300 may also include determining depositional facies of the 3D reservoir based upon the structure, as at 325.

[0045] The method 300 may also include determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies, as at 330.

[0046] The method 300 may also include performing a dynamic reservoir evaluation based upon the petrophysical property distributions to characterize the 3D reservoir, as at 335. The dynamic reservoir evaluation may include evaluating a plurality of 3D geological and petrophysical scenarios to contextualize pressure measurements in the 3D reservoir.

[0047] The method 300 may also include performing dynamic simulations using a plurality of models and calibrating the models, as at 340. The dynamic simulations and / or the calibration of the models may be based upon the dynamic reservoir evaluation. The models may integrate the stratigraphic and / or structural insights with well markers, property logs, and / or dip sets. The dip sets may be from borehole images, deep resistivity, sonic, and / or seismic data.

[0048] The method 300 may also include displaying an output of the petrophysical property distributions, the dynamic reservoir evaluation, the dynamic simulations, and / or the (e.g., calibrated) models, as at 345.

[0049] The method 300 may also include performing a wellsite action (e.g., drilling operation) in response to the petrophysical property distributions, the dynamic reservoir evaluation, the dynamic simulations, and / or the (e.g., calibrated) models, as at 350. The wellsite action may be or include and / or transmitting a signal that instructs or causes a physical action to occur at a wellsite. The physical action may be or include selecting where to drill a wellbore, drilling the wellbore, varying a weight and / or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and / or flow rate of a fluid pumped into the wellbore, and / or performing formation testing.Automatic processing and dip extraction

[0050] The processing and dip extraction may include processing the seismic data, borehole images, deep resistivity, and / or sonic data to extract dips therefrom. Borehole images provide the highest resolution for well logs enabling detailed reservoir characterization at the borehole wall of geological events that present enough electrical or acoustic contrast. Each image log, whetherlogging-while-drilling (LWD) or wireline, has its own complex processing chain due to the sonde configuration itself, with or without a pad, and due to the physics measurement that it is acquiring.

[0051] With a single click and no supervision, borehole imaging data may be processed into an interpretation-ready borehole image, which may be used to generate geological insights in a standardized format. The computation may be run immediately after acquisition. Fully automated workflows may process multi-physics borehole image logs and extract geological surfaces, fractures and textures (e.g., planned) with a single click and no supervision. Automated image processing and geological feature extraction enable near real time decisions (e.g., on operations and reservoir engineering).Automated Stratigraphic Insights

[0052] Figures 4A-4C illustrate automatic stratigraphic insights, according to an embodiment. In Figure 4A, track 3 shows the results of the grain size / stratigraphic trend extraction on gamma ray; track 5 shows the results of the grain size trend extraction on borehole images data; and track 8 (on the right) shows the results of the sedimentary geometry interpretation from borehole images. Figure 4B shows the conceptual models using sedimentary geometry and average grain size to understand depositional environments. Figure 4C shows the conceptual depositional environment for the interval shown in a well.

[0053] The recognition of sedimentary structures, grain size trends, and / or facies associations may be used to identify depositional environments. Making operational decisions, such as fluid and rock sampling locations and frequency, involves the understanding of the sedimentary variability at a small scale. The proposed methodology is a combination of machine learning- assisted approaches applied on well log data, including borehole images data, and aims to provide strong insights on stratigraphy. The first machine learning method is an innovative multi-physics technique, based on transfer learning UNet architecture, detecting and labelling automatically grain size trends. A first run on gamma-ray data provides the main zones of blocky sands and fining / coarsening upward sequences. A second run on specific borehole images may refine the trends analysis on targeted zones, allowing the understanding of small-scale grain size variations. The second machine learning method, based on CNN and ResNet architectures, extracts from the borehole images the zonation related to bedding features such as tabular sets, zigzags, scallops, trough sets, or rippleform (Figures 4A-4C).

[0054] The example below shows that the combination of the automatically generated stratigraphic insights allows the identification of a succession of longitudinal and transverse bars, typical from the transition between fluvial and deltaic environments. These stratigraphic insights can then be fed to the 3D analogue search.3D Stratigraphic analogue search

[0055] Figures 5A-5D illustrate examples of 3D stratigraphic models, according to an embodiment. More particularly, Figures 5B and 5C illustrate two examples of 3D stratigraphic models explaining the stratigraphic insights / depositional environments from a specific depth interval. Figure 5D shows that a geobody (e.g., point bar geobody) can then be extracted from these analogs to be integrated into 3D models.

[0056] The method may retrieve from a datastore, the ensemble of 3D stratigraphic models that explain the acquired well log data. These models, representing a range of depositional environments, may be stored at either local or cloud-based storage service center, and may be indexed using any kind of effective encoding, embedding, clustering or any other dimensionality reduction method, for example, vector quantization variational autoencoder (VQ-VAE) encoding. User-supplied well logs may also be encoded with VQ-VAE, enabling efficient data retrieval from the datastore based on clustering algorithms.Automatic petrophysical interpretation

[0057] The sections below list and briefly summarize the portions of the automatic petrophysical interpretation workflow. The method may use real-time LWD data (e.g., fully automatic). The method may also or instead use LWD memory data or wireline data (e.g., automatic by default, with user verification and optional fine tuning). For the wireline case, where the data is acquired in multiple passes and provided at the end of multiple logging runs in multiple files, the method may use automatic pass-to-pass depth matching. Automatic depth matching may support depth matching of curves other than gamma ray. Real time LWD data often has gaps such as the depths of missing values. If not handled properly, this makes the log views difficult to use. One solution is for RCI graphical gap filling capability to address this. Another solution may be to interpolate the input data so that the output data does not have gaps. In the adaptive embodiment, the log view layout (e.g., tracks, curves etc.) may be set up on-the-fly based upon available data and selectedcomputations. In the interactive embodiment, parameter selection may be performed by the user via dragging cut-offlines.User sets parameters

[0058] The number of parameters which the user may have to set depends on the input data. For example, when spectroscopy data is present, there may be one interpretation parameter: formation water salinity (FSAL) for computing water saturation. For LWD / EcoScope, the user may also use the sigma shale parameter. In another embodiment, there may be three net pay parameters. When spectroscopy and Rt scanner data are present, there may be one interpretation parameter to set: FSAL. There may be two interpretation parameters to validate: shale resistivity (e.g., horizontal) and shale resistivity (e.g., vertical). There may be three net pay parameters. When spectroscopy data is not present, there may be three interpretation parameters: FSAL, GR clean, GR shale. There may be three net pay parameters. When NMR data is present in addition to any of the above, there may be one additional interpretation parameterT2 cut off.Trigger Computation

[0059] In an embodiment, a user may run the computation (e.g., run button?) initially. The computation may not be rerun automatically when parameters are changed. In an embodiment, automatic quality control (QC) may not be applicable because this is an interpretation. Instead, QC may be performed by an interpretation expert.

[0060] Once a complete workflow is established between pre-job planning and RCI, interpretation experts may set up auto-QC tests during pre-job phase. The curves (e.g., Sw, porosity, permeability, etc.) may be available for download by those interested in retaining or reusing them.Fast 3D Structural and Property modeling

[0061] Figures 6A-6C illustrate input data used for 3D modeling, according to an embodiment. More particularly, Figure 6C shows seismic surfaces, well tops, dips from automatic processing and dip extraction, petrophysical properties from automatic petrophysical interpretation, and geobody from 3D stratigraphic analogue search. Figure 6B shows an interactive boundary. Figure 6A shows one scenario of 3D property modeling around the target well.

[0062] The method combines structural modeling using meshless smart structures with property modeling using EMBER to contextualize wellbore measurements within 3D space. This approach integrates stratigraphic insights with conventional well markers, dip sets, geobody, property from logs, 2D sections, and 3D trends for generating a robust ensemble of 3D property models (Figure 6A-6C) for various applications from subsurface characterization to formation testing. In other words, for the 3D models, the method may integrate geobodies (e.g., 3D volumes) with properties and 2D sections such as transversal or longitudinal sections from deep resistivity.Exemplary Computing System

[0063] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 7 illustrates an example of such a computing system 700, in accordance with some embodiments. The computing system 700 may include a computer or computer system 701A, which may be an individual computer system 701A or an arrangement of distributed computer systems. The computer system 701A includes one or more analysis modules 702 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 702 executes independently, or in coordination with, one or more processors 704, which is (or are) connected to one or more storage media 706. The processor(s) 704 is (or are) also connected to a network interface 707 to allow the computer system 701A to communicate over a data network 709 with one or more additional computer systems and / or computing systems, such as 70 IB, 701C, and / or 70 ID (note that computer systems 70 IB, 701C and / or 70 ID may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701 A and 70 IB may be located in a processing facility, while in communication with one or more computer systems such as 701 C and / or 70 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0064] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0065] The storage media 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 7 storage media 706 is depicted as within computer system 701 A, in some embodiments, storage media 706may be distributed within and / or across multiple internal and / or external enclosures of computing system 701A and / or additional computing systems. Storage media 706 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), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0066] In some embodiments, computing system 700 contains one or more reservoir contextualization module(s) 708. In the example of computing system 700, computer system 701 A includes the reservoir contextualization module 708. In some embodiments, a single reservoir contextualization module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of reservoir contextualization modules may be used to perform some aspects of methods herein.

[0067] It should be appreciated that computing system 700 is merely one example of a computing system, and that computing system 700 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 7, and / or computing system 700 may have a different configuration or arrangement of the components depicted in Figure 7. The various components shown in Figure 7 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0068] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as generalpurpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0069] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 700, Figure 7), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

[0070] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

CLAIMSWhat is claimed is:

1. A method for contextualizing a 3D reservoir, the method comprising: receiving input data corresponding to the 3D reservoir; processing and interpreting the input data to produce stratigraphic and / or structural insights; processing and interpreting the input data to produce petrophysical insights; determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights; determining depositional facies of the 3D reservoir based upon the structure; and determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies.

2. The method of Claim 1, wherein the input data comprises well log data and / or seismic data.

3. The method of Claim 1, wherein the input data comprises borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, sonic data, and / or deep resistivity data.

4. The method of Claim 1, wherein the stratigraphic and / or structural insights comprise a sedimentary structure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir.

5. The method of Claim 1, wherein processing and interpreting the input data to produce the petrophysical insights comprises selecting and determining values for one or more parameters based upon the input data.

6. The method of Claim 5, wherein the one or more parameters comprise a formation water salinity (FSAL) parameter, a horizontal shale resistivity parameter, a vertical shale resistivityparameter, a gamma ray (GR) clean parameter, a GR shale parameter, and / or a T2 cut off parameter.

7. The method of Claim 1, further comprising performing a dynamic reservoir evaluation based upon the petrophysical property distributions to characterize the 3D reservoir, wherein the dynamic reservoir evaluation comprises evaluating a plurality of 3D geological and petrophysical scenarios to contextualize pressure measurements in the 3D reservoir.

8. The method of Claim 7, further comprising performing dynamic simulations using a plurality of models based upon the dynamic reservoir evaluation, wherein the models integrate the stratigraphic and / or structural insights with well markers, dip sets, and property logs.

9. The method of Claim 1, further comprising displaying an output of the petrophysical property distributions.

10. The method of claim 1, further comprising performing a drilling operation in response to the stratigraphic and / or structural insights, the petrophysical insights, and / or the petrophysical property distributions.

11. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: receiving input data corresponding to the 3D reservoir, wherein the input data comprises well log data and / or seismic data, and wherein the well log data comprises borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, sonic data, and / or deep resistivity data; processing and interpreting the input data to produce stratigraphic and / or structural insights, wherein the stratigraphic and / or structural insights comprise a sedimentarystructure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir; processing and interpreting the input data to produce petrophysical insights, wherein processing and interpreting the input data to produce the petrophysical insights comprises selecting and determining values for one or more parameters based upon the input data; determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights; determining depositional facies of the 3D reservoir based upon the structure; and determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies.

12. The computing system of Claim 11, wherein the input data is processed and interpreted to produce the stratigraphic and / or structural insights by detecting and labeling the grain size trends using a first machine learning method, and wherein the first machine learning method comprises a transfer learning UNet architecture.

13. The computing system of Claim 12, wherein the input data is processed and interpreted to produce the stratigraphic and / or structural insights by extracting a zonation related to bedding features from the borehole images using a second machine learning method, wherein the second machine learning method comprises a convolutional neural network and ResNet architecture, and wherein the bedding features comprise tabular sets, zigzags, scallops, trough sets, and / or rippleform.

14. The computing system of Claim 11, wherein the operations further comprise performing a dynamic reservoir evaluation based upon the petrophysical property distributions to characterize the 3D reservoir, wherein the dynamic reservoir evaluation comprises evaluating a plurality of 3D geological and petrophysical scenarios to contextualize pressure measurements in the 3D reservoir.

15. The computing system of Claim 14, wherein the operations further comprise:performing dynamic simulations using a plurality of models based upon the dynamic reservoir evaluation, wherein the models integrate the stratigraphic and / or structural insights with well markers, dip sets, and property logs; and calibrating the models to produce calibrated models of the 3D reservoir.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: receiving input data corresponding to the 3D reservoir, wherein the input data comprises well log data and / or seismic data, and wherein the well log data comprises borehole images, gamma ray data, spectroscopy data, resistivity (Rt) scanner data, nuclear magnetic resonance (NMR) data, sonic data, and / or deep resistivity data; processing and interpreting the input data to produce stratigraphic and / or structural insights, wherein the stratigraphic and / or structural insights comprise a sedimentary structure, grain size trends, and / or facies associations for identifying depositional environments in the 3D reservoir, and wherein the input data is processed and interpreted to produce the stratigraphic and / or structural insights by: detecting and labeling the grain size trends using a first machine learning method, wherein the first machine learning method comprises a transfer learning UNet architecture; and extracting a zonation related to bedding features from the borehole images using a second machine learning method, wherein the second machine learning method comprises a convolutional neural network and ResNet architecture, and wherein the bedding features comprise tabular sets, zigzags, scallops, trough sets, and / or rippleform; processing and interpreting the input data to produce petrophysical insights, wherein processing and interpreting the input data to produce the petrophysical insights comprises selecting and determining values for one or more parameters based upon the input data; determining a structure of the 3D reservoir based upon the input data, the stratigraphic and / or structural insights, and the petrophysical insights; determining depositional facies of the 3D reservoir based upon the structure;determining petrophysical property distributions of the 3D reservoir based upon the structure only and / or based upon the structure and the depositional facies; displaying an output of the petrophysical property distributions; and performing a drilling operation in response to the stratigraphic and / or structural insights, the petrophysical insights, and / or the petrophysical property distributions.

17. The non-transitory computer-readable medium of Claim 16, wherein the first machine learning method: performs a first run on the gamma ray data to identify zones of blocky sands and fining and / or coarsening upward sequences; and performs a second run on the borehole images to refine an analysis of the grain size trends in the zones to identify small-scale variations in the grain size trends, wherein fluid and / or rock sampling locations and frequency are determined based upon the analysis and / or the small-scale variations.

18. The non-transitory computer-readable medium of Claim 16, wherein the one or more parameters comprise: a formation water salinity (FSAL) parameter in response to the input data comprising the spectroscopy data but not the Rt scanner data; the FSAL parameter, a horizontal shale resistivity parameter, and a vertical shale resistivity parameter in response to the input data comprising the spectroscopy data and the Rt scanner data; the FSAL parameter, a gamma ray (GR) clean parameter, and a GR shale parameter in response to the input data not comprising the spectroscopy data; and / or a T2 cut off parameter in response to the input data comprising the NMR data19. The non-transitory computer-readable medium of Claim 16, wherein the operations further comprise: performing a dynamic reservoir evaluation based upon the petrophysical property distributions to characterize the 3D reservoir, wherein the dynamic reservoir evaluation comprises evaluating a plurality of 3D geological and petrophysical scenarios to contextualize pressure measurements in the 3D reservoir;performing dynamic simulations using a plurality of models based upon the dynamic reservoir evaluation, wherein the models integrate the stratigraphic and / or structural insights with well markers, dip sets, and property logs; and calibrating the models to produce calibrated models of the 3D reservoir.

20. The non-transitory computer-readable medium of Claim 16, wherein the drilling operation comprises generating and / or transmitting a signal that instructs or causes a physical action to occur at a wellsite, and wherein the physical action comprises selecting where to drill a wellbore, drilling the wellbore, varying a weight and / or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and / or flow rate of a fluid pumped into the wellbore, or performing formation testing.

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