Intelligent play fairway analysis and chance of success mapping

Abstract

A method for identifying a prospect in a subsurface formation includes receiving first data corresponding to one or more first subsurface formations. The method also includes training a machine learning (ML) model using the first data to produce a trained ML model. The method also includes receiving second data corresponding to one or more second subsurface formations. The method also includes identifying second prospects in the one or more second subsurface formations. The second prospects are identified using the trained ML model based upon the second data. The method also includes determining a chance of success in each of the second prospects using the trained ML model based upon the second data.

Description

INTELLIGENT PLAY FAIRWAY ANALYSIS AND CHANCE OF SUCCESS MAPPINGCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 595,096, filed on November 1, 2023, which is incorporated by reference in its entirety.Background

[0002] A subsurface exploration workflow may identify a suitable prospect in the subsurface. This may take into consideration risks associated with the elements of play. The workflow may also estimate resources in the prospect. However, the workflow may have some challenges associated therewith. For example, one challenge may be discouraging a success rate in play fairway analysis.

[0003] Play fairway analysis refers to a type of map used in exploration in which regional trends in geology that are relevant to exploring for a particular play are depicted as polygons on a map. The purpose of this map is to visually suggest the main “fairway(s)” or areas where the specific play is likely to be successful and additional exploration work at a smaller scale is warranted. A part of the map is often what portions of the map are “off fairway” and do not warrant additional exploration. The concept is used in various types of exploration, including hydrocarbon and geothermal exploration. Another challenge may be the impact of multi-domain factors on the chance of success. Yet another challenge may be the limited data availability at the exploration stage.Summary

[0004] A method for identifying a prospect in a subsurface formation is disclosed. The method receiving first data corresponding to one or more first subsurface formations. The method also includes training a machine learning (ML) model using the first data to produce a trained ML model. The method also includes receiving second data corresponding to one or more second subsurface formations. The method also includes identifying second prospects in the one or more second subsurface formations. The second prospects are identified using the trained ML model based upon the second data. The method also includes determining a chance of success in each of the second prospects using the trained ML model based upon the second data.

[0005] 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 first data corresponding to one or more first subsurface formations. The first data includes first input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, or data availability. The first data also includes first output data including one or more first prospects in the one or more first subsurface formations and a success or failure of the one or more first prospects. The success includes a location where an amount of hydrocarbons exceeds a hydrocarbon threshold. The failure indicates that the amount of hydrocarbons does not exceed the predetermined hydrocarbon threshold. The operations also include training a machine learning (ML) model using the first data to produce a trained ML model. The ML model is trained using supervised and unsupervised approaches. The ML model is trained to establish a relationship between the first input data and the first output data. The operations also include receiving second data corresponding to one or more second subsurface formations. The second data includes second input data including the reservoir quality, the charge quality, the geomechanical quality, the trapping mechanisms, the seal quality, or the data availability. The second data does not include second output data identifying second prospects in the one or more second subsurface formations and the success or the failure of the second prospects. The operations also include determining the second output data using the trained ML model. The second output data is determined based upon the second data. The second output data includes the second prospects in the one or more second subsurface formations and a chance of success in each of the second prospects. The operations also include determining risked resources to access the hydrocarbons in each of the second prospects. The risked resources are determined using the trained ML model. The risked resources are determined based upon the second data. The risked resources are determined based upon an amount of the resources and the chance of success. The operations also include selecting one of the second prospects based upon the chance of success and the risked resources.

[0006] 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 first datacorresponding to one or more first subsurface formations. The first data includes first input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, and data availability. The reservoir quality includes porosity, saturation, and volume. The charge quality includes source proximity, maturity, source quality, and formation pressure. The geomechanical quality includes fracture initiation, complexity, containment, and wellbore stability. The first data also includes first output data including one or more first prospects in the one or more first subsurface formations and a success or failure of the one or more first prospects. The success includes a location where an amount of hydrocarbons exceeds a predetermined hydrocarbon threshold. The failure indicates that the amount of hydrocarbons does not exceed the predetermined hydrocarbon threshold. The operations also include training a machine learning (ML) model using the first data to produce a trained ML model. The ML model is trained using supervised and unsupervised approaches. The ML model is trained to establish a relationship between the first input data and the first output data. The operations also include receiving second data corresponding to one or more second subsurface formations. The second data includes second input data including the reservoir quality, the charge quality, the geomechanical quality, the trapping mechanisms, the seal quality, and the data availability. The second data does not include second output data identifying second prospects in the one or more second subsurface formations and the success or the failure of the second prospects. The operations also include determining the second output data using the trained ML model. The second output data is determined based upon the second data. The second output data includes the second prospects in the one or more second subsurface formations and a chance of success in each of the second prospects. The operations also include determining risked resources to access the hydrocarbons in each of the second prospects. The risked resources are determined using the trained ML model. The risked resources are determined based upon the second data. The risked resources are determined based upon an amount of the resources and the chance of success. The operations also include selecting one of the second prospects based upon the chance of success and the risked resources. The operations also include displaying the second prospects, the chance of success in each of the second prospects, and the risked resources for each of the second prospects.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] Figure 2 illustrates a schematic view of a final decision based upon a plurality of factors, according to an embodiment.

[0010] Figures 3A and 3B illustrate an interface where different elements of a conventional play, unconventional play, and / or geothermal can be provided as an input, and one or more machine learning (ML) algorithms identify a prospect based upon these inputs, according to an embodiment.

[0011] Figure 4 illustrates a flowchart of a method for identifying a prospect in a subsurface formation, according to an embodiment.

[0012] Figure 5 illustrates a schematic view of a play fairway analysis and chance of success mapping including at least a portion of the first data, according to an embodiment.

[0013] Figure 6 illustrates a schematic view of a computing system for performing at least a portion of the method(s) herein, according to an embodiment.Detailed Description

[0014] 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.

[0015] 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 firstobject 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.

[0016] 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.

[0017] 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.

[0018] The present disclosure may include a system and method that are configured to perform a play fairway analysis in an automated manner using machine learning (ML). The present disclosure may also determine a location and a chance of success (COS) of a plurality of prospects in the automated manner using ML. The chance of success may refer to the likelihood (e.g., a percentage) that an amount of hydrocarbons (e.g., oil and / or gas) exceeds a predetermined hydrocarbon threshold in a prospect. The present disclosure may also provide an interface for risked resource estimation of the prospects. The present disclosure may also provide asset evaluation and input to acquisition and / or divestiture decisions. A final decision (e.g., rank) may be expandable and customizable (e.g., based upon data availability).

[0019] The present disclosure uses a ML-based approach, where reservoir quality, charge quality, trapping mechanisms, seal quality, and / or data availability may be used to train a machinelearning model using supervised and / or unsupervised approaches. The present disclosure may also provide different realizations and / or scenarios of prospects, which may be used as part of a riskedresource assessment. The present disclosure may be applicable to multiple types of plays (e.g., conventional, unconventional, geothermal, etc.).System Overview

[0020] 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).

[0021] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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 ).

[0026] 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 aframework 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.).

[0027] 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.).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.).

[0034] 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 anassessment 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.

[0035] 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 a workflow 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.).Exemplary Schematic View of a Einal Decision

[0036] Figure 2 illustrates a schematic view of a final decision based upon a plurality of factors, according to an embodiment. In some cases, the final decision (RANK) can be expandable and / or customizable, based on data availability.Exemplary Table

[0037] The present disclosure may provide an interface where different elements of a conventional play, unconventional play, and / or geothermal can be provided as an input. This is shown in Figures 3A and 3B. These inputs may include or be in the form of structural maps, fault polygons, facies proportion maps, seismic attribute maps, maturity maps, petroleum system model, geomechanical model, or a combination thereof. The present disclosure may then use (e.g., supervised) ML algorithms to identify one or more (e.g., the best) prospects based upon the provided inputs. It may also provide an additional option to estimate the risked resources for the identified prospects. The application may be useful in exploration studies for different plays and may provide a fast track estimate of resources in an automated manner. The present disclosuremay also or instead determine a sweet spot area in the subsurface as well as prioritize assets with reduced risk and their appraisal. In already developed areas, the present disclosure may provide an understanding of variable well performance. The present disclosure may also remove subjectivity and provide an additional level of confidence (e.g., by comparing with known successful plays).Exemplary Flowchart and Schematic View for Identifying a Prospect

[0038] Figure 4 illustrates a flowchart of a method 400 for identifying a prospect in a subsurface formation, according to an embodiment. An illustrative order of the method 400 is provided below; however, one or more portions of the method 400 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 400 may be performed using a computing system (described below).The method 400 may include receiving first data, as at 405. The first data may correspond to one or more first subsurface formations. The first data may include first input data such as reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, data availability, or a combination thereof. The reservoir quality may include porosity, saturation, volume, or a combination thereof. The charge quality may include source proximity, maturity, source quality, formation pressure, or a combination thereof. The geomechanical quality may include fracture initiation, complexity, containment, wellbore stability, or a combination thereof.Play Fairway Analysis

[0039] The first data may also include first output data. The first output data may include one or more first prospects in the one or more first subsurface formations and / or a success or failure of the one or more first prospects. The success may include a location where an amount of hydrocarbons exceeds a predetermined hydrocarbon threshold. The failure indicates that the amount of hydrocarbons does not exceed the predetermined hydrocarbon threshold. Figure 5 illustrates a schematic view of a play fairway analysis and chance of success (COS) mapping including at least a portion of the first data, according to an embodiment. The overall chance of success is determined by multiplication of the chance of successes related to each element in Figure 5, where each element’s chance of success is normalized between (0-1), with 0 being the worst, and 1 being the best.

[0040] Table 1 illustrates an example of determining the chance of success, according to an embodiment. More particularly, Table 1 shows the COS calculation of an identified prospect in an example area. The COS for the individual element may be calculated for the example area. The interpretation confidence may be assigned a COS value of 0.9, because the entire area is covered by good quality 3D seismic data. The reservoir quality COS value of 0.7 is governed by high fracture porosity inside the prospect polygon. Similarly, the COS values for seal capacity and source adequacy are assigned using the normalized hydrocarbon expulsion map and a normalized capillary entry pressure map, respectively, A low COS value of 0.6 is assigned for structural closure within the prospect polygon.

[0041] Referring back to Figure 4, the method 400 may also include training a machine learning (ML) model using the first data to produce a trained ML model, as at 410. The ML model may be trained using supervised and unsupervised approaches. The ML model may be trained to establish a relationship between the first input data and the first output data.

[0042] The method 400 may also include receiving second data, as at 415. The second data may correspond to one or more second subsurface formations. The second data may include second input data such as the reservoir quality, the charge quality, the geomechanical quality, the trapping mechanisms, the seal quality, the data availability, or a combination thereof. The second data may not include second output data. In other words, the second data may not identify second prospects in the one or more second subsurface formations. The second data also may not identify or determine the success or the failure of the second prospects.

[0043] The method 400 may also include determining the second output data using the trained ML model, as at 420. The second output data may be determined based upon the second data. The second output data may identify the second prospects in the one or more second subsurface formations, as at 421. The second output data may also include or identify a chance of success in each of the second prospects, as at 422.

[0044] The method 400 may also include determining risked resources to access the hydrocarbons in each of the second prospects, as at 425. The risked resources may be determined using the trained ML model. The risked resources may be determined based upon the second data (e.g., the second output data). The risked resources may be determined based upon an amount of the resources and the chance of success. For example, risked resources = an amount of the resources (in place) * the overall chance of success.

[0045] The method 400 may also include selecting one of the second prospects, as at 430. The second prospect may be selected based upon the chance of success and / or the risked resources.

[0046] The method 400 may also include displaying the second prospects, the chance of success in each of the second prospects, and the risked resources for each of the second prospects, as at 435.

[0047] The method 400 may also include performing a wellsite action, as at 440. The wellsite action may be based upon the identified second prospects, the chances of success in each of the second prospects, the risked resources to access hydrocarbons in each of the second prospects, the selected second prospect, or a combination thereof. The wellsite action may be or include generating and / or transmitting a signal (e.g., using the computing system) that instructs or causes a physical action to occur at a wellsite. The wellsite action may also or instead include performing the physical action at the wellsite. The physical action may 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, or the like.Exemplary Computing System

[0048] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 6 illustrates an example of such a computing system 600, in accordance with some embodiments. The computing system 600 may include a computer or computer system601 A, which may be an individual computer system 601 A or an arrangement of distributed computer systems. The computer system 601A includes one or more analysis modules 602 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 602 executes independently, or in coordination with, one or more processors 606, which is (or are) connected to one or more storage media 606. The processor(s) 604 is (or are) also connected to a network interface 607 to allow the computer system 601 A to communicate over a data network 609 with one or more additional computer systems and / or computing systems, such as 601B, 601C, and / or 601D (note that computer systems 601B, 601C and / or 601D may or may not share the same architecture as computer system 601A, and may be located in different physical locations, e.g., computer systems 601 A and 601B may be located in a processing facility, while in communication with one or more computer systems such as 601C and / or 601D that are located in one or more data centers, and / or located in varying countries on different continents).

[0049] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0050] The storage media 606 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 6 storage media 606 is depicted as within computer system 601A, in some embodiments, storage media 606 may be distributed within and / or across multiple internal and / or external enclosures of computing system 601A and / or additional computing systems. Storage media 606 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 articleof 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.

[0051] In some embodiments, computing system 600 contains one or more play fairway analysis module(s) 608. It should be appreciated that computing system 600 is merely one example of a computing system, and that computing system 600 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 6, and / or computing system 600 may have a different configuration or arrangement of the components depicted in Figure 6. The various components shown in Figure 6 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.

[0052] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0053] 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 600, Figure 6), 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.

[0054] 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 theart 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 identifying a prospect in a subsurface formation, the method comprising: receiving first data corresponding to one or more first subsurface formations; training a machine learning (ML) model using the first data to produce a trained ML model; receiving second data corresponding to one or more second subsurface formations; identifying second prospects in the one or more second subsurface formations, wherein the second prospects are identified using the trained ML model based upon the second data; and determining a chance of success in each of the second prospects using the trained ML model based upon the second data.

2. The method of Claim 1, wherein the first data comprises input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, or data availability.

3. The method of Claim 2, wherein the first data also comprises output data including one or more first prospects in the one or more first subsurface formations and a success or failure of the one or more first prospects.

4. The method of Claim 3, wherein the success comprises a location where an amount of hydrocarbons exceeds a hydrocarbon threshold, and wherein the failure indicates that the amount of hydrocarbons does not exceed the hydrocarbon threshold.

5. The method of Claim 3, wherein the ML model is trained using supervised and unsupervised approaches, and wherein the ML model is trained to establish a relationship between the input data and the output data.

6. The method of Claim 1, wherein the second data comprises input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, or the data availability.

7. The method of Claim 6, wherein the second data does not comprise output data identifying the second prospects in the one or more second subsurface formations and a success or failure of the second prospects.

8. The method of Claim 1, further comprising determining risked resources to access hydrocarbons in each of the second prospects, wherein the risked resources are determined using the trained ML model, wherein the risked resources are determined based upon the second data, and wherein the risked resources are determined based upon an amount of the resources and the chance of success.

9. The method of Claim 8, further comprising selecting one of the second prospects based upon the chance of success and the risked resources.

10. The method of Claim 9, further comprising performing a wellsite action to access the selected one of the second prospects.

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 first data corresponding to one or more first subsurface formations, wherein the first data comprises first input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, or data availability, wherein the first data also comprises first output data including one or more first prospects in the one or more first subsurface formations and a success or failure of the one or more first prospects, wherein the success comprises a location where an amount of hydrocarbons exceeds a hydrocarbon threshold, and wherein the failure indicates that the amount of hydrocarbons does not exceed the predetermined hydrocarbon threshold;training a machine learning (ML) model using the first data to produce a trained ML model, wherein the ML model is trained using supervised and unsupervised approaches, and wherein the ML model is trained to establish a relationship between the first input data and the first output data; receiving second data corresponding to one or more second subsurface formations, wherein the second data comprises second input data including the reservoir quality, the charge quality, the geomechanical quality, the trapping mechanisms, the seal quality, or the data availability, and wherein the second data does not comprise second output data identifying second prospects in the one or more second subsurface formations and the success or the failure of the second prospects; determining the second output data using the trained ML model, wherein the second output data is determined based upon the second data, and wherein the second output data comprises: the second prospects in the one or more second subsurface formations; and a chance of success in each of the second prospects; determining risked resources to access the hydrocarbons in each of the second prospects, wherein the risked resources are determined using the trained ML model, wherein the risked resources are determined based upon the second data, and wherein the risked resources are determined based upon an amount of the resources and the chance of success; and selecting one of the second prospects based upon the chance of success and the risked resources.

12. The computing system of Claim 11, wherein the reservoir quality comprises porosity, saturation, and volume.

13. The computing system of Claim 11, wherein the charge quality comprises source proximity, maturity, source quality, and formation pressure.

14. The computing system of Claim 11, wherein the geomechanical quality comprises fracture initiation, complexity, containment, and wellbore stability.

15. The computing system of Claim 11, wherein the operations further comprise displaying the second prospects, the chance of success in each of the second prospects, and the risked resources for each of the second prospects16. 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 first data corresponding to one or more first subsurface formations, wherein the first data comprises first input data including reservoir quality, charge quality, geomechanical quality, trapping mechanisms, seal quality, and data availability, wherein the reservoir quality comprises porosity, saturation, and volume, wherein the charge quality comprises source proximity, maturity, source quality, and formation pressure, wherein the geomechanical quality comprises fracture initiation, complexity, containment, and wellbore stability, wherein the first data also comprises first output data including one or more first prospects in the one or more first subsurface formations and a success or failure of the one or more first prospects, wherein the success comprises a location where an amount of hydrocarbons exceeds a predetermined hydrocarbon threshold, and wherein the failure indicates that the amount of hydrocarbons does not exceed the predetermined hydrocarbon threshold; training a machine learning (ML) model using the first data to produce a trained ML model, wherein the ML model is trained using supervised and unsupervised approaches, and wherein the ML model is trained to establish a relationship between the first input data and the first output data; receiving second data corresponding to one or more second subsurface formations, wherein the second data comprises second input data including the reservoir quality, the charge quality, the geomechanical quality, the trapping mechanisms, the seal quality, and the data availability, and wherein the second data does not comprise second output data identifying second prospects in the one or more second subsurface formations and the success or the failure of the second prospects; determining the second output data using the trained ML model, wherein the second output data is determined based upon the second data, and wherein the second output data comprises: the second prospects in the one or more second subsurface formations; anda chance of success in each of the second prospects; determining risked resources to access the hydrocarbons in each of the second prospects, wherein the risked resources are determined using the trained ML model, wherein the risked resources are determined based upon the second data, and wherein the risked resources are determined based upon an amount of the resources and the chance of success; selecting one of the second prospects based upon the chance of success and the risked resources; and displaying the second prospects, the chance of success in each of the second prospects, and the risked resources for each of the second prospects.

17. The non-transitory computer-readable medium of Claim 16, wherein the risked resources are equal to the amount of resources multiplied by the chance of success.

18. The non-transitory computer-readable medium of Claim 16, wherein the operations further comprise performing a wellsite action to access the selected one of the second prospects.

19. The non-transitory computer-readable medium of Claim 18, wherein the wellsite action comprises generating or transmitting a signal that instructs or causes a physical action to occur at a wellsite that includes the second prospects.

20. The non-transitory computer-readable medium of Claim 19, 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, or varying a concentration and / or flow rate of a fluid pumped into the wellbore.

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