Generative ai agents for production engineering in oil and gas

Abstract

A method for performing production engineering tasks includes receiving a question or instruction related to an oil and / or gas industry. The question or instruction is received by a large language model (LLM) agent. The method also includes selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include a data tool, a retrieval augmented generation (RAG) tool, a simulation tool, and an analytical tool. The method also includes generating output data related to the question or instruction using the one or more identified tools. The method also includes generating a response to the question or instruction using the LLM agent based upon output data.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 694,322, filed on Sep. 13, 2024, which is incorporated by reference herein.BACKGROUND

[0002] Conventional systems that perform production engineering tasks in the oil and gas industry have difficulty in efficiently processing and analyzing large volumes of production data. Conventional systems also have challenges in optimizing complex production systems. Conventional systems also have a limited ability to leverage domain expertise at scale. Therefore, what is needed is an improved system and method that provide faster, more accurate decision-making in production operations.SUMMARY

[0003] A method for performing production engineering tasks is disclosed. The method includes receiving a question or instruction related to an oil and / or gas industry. The question or instruction is received by a large language model (LLM) agent. The method also includes selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include a data tool, a retrieval augmented generation (RAG) tool, a simulation tool, and an analytical tool. The method also includes generating output data related to the question or instruction using the one or more identified tools. The method also includes generating a response to the question or instruction using the LLM agent based upon output data.

[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 a question or instruction related to an oil and / or gas industry. The question or instruction is received by a large language model (LLM) agent. The operations also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include (1) a data tool that is configured to be selected in response to the question or instruction including structured data; (2) a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction including unstructured data; (3) a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; and (4) an analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. The operations also include generating output data related to the question or instruction using the one or more identified tools. The operations also include generating a response to the question or instruction using the LLM agent based upon output data.

[0005] A non-transitory computer-readable medium is also disclosed. The medium includes instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving a question or instruction related to an oil and / or gas industry. The question is related to identifying pumps with a cavitation risk and suggesting immediate corrective actions. The question or instruction is received by a large language model (LLM) agent. The operations also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The one or more tools are also identified based upon a context on a screen of a user. The context includes a region and / or asset that the user is working on and an ongoing conversation history of the user. The tools include (1) a data tool that is configured to be selected in response to the question or instruction including structured data; (2) a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction also including unstructured data; (3) a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; and (4) an analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. The operations also include generating output data related to the question or instruction using the one or more identified tools. The output data requests additional information from the user. The output data includes a list of the pumps with the cavitation risk, generated by the data tool. The list also includes a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk. The output data also includes the immediate corrective actions, generated by the RAG tool. The immediate corrective actions include reducing a speed of the pumps and / or increasing a suction head of the pumps. The operations also include generating a response to the question or instruction using the LLM agent based upon output data. The response shows one or more sources that were used to generate the output data. The operations also include displaying the response, wherein displaying the response comprises generating a graph.

[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] FIG. 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] FIGS. 2A and 2B illustrate a schematic view of a system for performing production engineering tasks in the oil and gas industry, according to an embodiment.

[0010] FIG. 3 illustrates a schematic view of the system showing multi-agent capabilities, according to an embodiment.

[0011] FIG. 4 illustrates a flowchart of a method for performing production engineering tasks in the oil and gas industry, according to an embodiment.

[0012] FIGS. 5 and 6 illustrate schematic view of the system generating responses for different questions or instructions, according to an embodiment.

[0013] FIG. 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

[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 present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure 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 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.

[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.System Overview

[0018] FIG. 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).

[0019] In the example of FIG. 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.

[0020] 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.

[0021] 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.

[0022] In the example of FIG. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. 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 FIG. 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0023] 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.).

[0024] 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.).

[0025] 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.).

[0026] FIG. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and 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.

[0027] 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.

[0028] In the example of FIG. 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.

[0029] 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).

[0030] In the example of FIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. 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.

[0031] In the example of FIG. 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, FIG. 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.).

[0032] FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0033] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the 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.).Generative AI Agents for Production Engineering in Oil and Gas

[0034] The present disclosure relates to a system of generative AI agents designed to assist with production engineering tasks in the oil and gas industry. The system includes multiple specialized AI agents, including a subject matter expert (SME) agent, a data agent, and / or a simulator agent that work together to analyze data, run simulations, and provide insights to optimize production operations.

[0035] The system may include an AI orchestration hub that manages and coordinates the different AI agents. The system may also include the SME agent that provides domain expertise and (e.g., best) practices. The system may also include the data agent which analyzes real-time and historical production data. The system may also include the simulator agent, which runs production simulations and optimizations.

[0036] The system may provide integration of multiple specialized AI agents to tackle complex production engineering tasks. The system may also provide a natural language interface for interacting with the system. The system may also include an ability to combine real-time data, simulations, and domain expertise. Accordingly, the system may provide faster and more accurate production optimization, improved decision making through AI-assisted insights, reduced downtime, and increased efficiency of operations.

[0037] These benefits may be provided because the AI agents can rapidly process and analyze large datasets. In addition, the simulator agent enables quick optimization of production scenarios. The SME agent provides scalable access to domain knowledge. The system also provides AI-assisted insights to support faster, data-driven decisions.

[0038] The system may have the following applications: production optimization, well performance analysis, equipment maintenance scheduling, reservoir management, autonomous production management and integration with IoT sensors, or a combination thereof. Other applications and / or uses may be or include integration into existing production management workflows, development of custom AI agents for specific production challenges, creation of digital twin systems powered by AI agents, use in training and knowledge transfer for new engineers, or a combination thereof.

[0039] The system may provide improvements over conventional systems such as an integration of multiple AI capabilities (e.g., data analysis, simulation, domain expertise), a natural language interface for easier interaction, an ability to combine real-time data with simulations and expert knowledge, or a combination thereof. Other improvements may be or include faster analysis and decision-making, more comprehensive insights by combining multiple data sources and AI capabilities, easier access to domain expertise through natural language queries, scalability to handle large, complex production systems, or a combination thereof.Exemplary System

[0040] FIGS. 2A and 2B illustrate a schematic view of a system 200 for performing production engineering tasks in the oil and gas industry, according to an embodiment. As shown, the system 200 may include a generative artificial intelligence (GenAI) agent 210. The agent 210 may include and / or be configured to access one or more tools. The tools may include one or more data tools 220 that are configured to determine or provide asset performance, production assurance, maintenance records, or a combination thereof. The tools may also or instead include one or more simulation tools 230 that are configured to simulate steady-state multiphase flow and / or thermodynamic fluid modelling for the oil and gas industry. IN an example, the simulation tools 230 may be or include PIPESIM® and / or SYMMETRY®. The tools may also or instead include one or more analytical tools 240 that are configured to perform data-driven machine-learning (ML) and / or physics-based ML. The tools may also or instead include one or more retrieval augmented generation (RAG) tools 250 that are configured to retrieve operational manuals and / or design documents. In an embodiment, once a user is authenticated to SLB's DELFI® platform, the user can access multiple applications and services based on their feature subscriptions and data access permissions, without having to log in repeatedly.

[0041] FIG. 3 illustrates a schematic view of the system 200 showing multi-agent capabilities, according to an embodiment. The data tools 220 may be configured to answer questions from structured data. The analytical tools 240 may be configured to run on-demand data analysis involving ML models and physics-based calculations. The simulation tools 230 may be configured to run on-demand simulations. The RAG tools 250 may be configured to answer questions from unstructured data.Exemplary Method

[0042] FIG. 4 illustrates a flowchart of a method 400 for performing production engineering tasks in the oil and gas industry, 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.

[0043] The method 400 may include receiving a question or instruction related to an oil and / or gas industry, as at 405. The question or instruction is received by a large language model (LLM) agent.

[0044] The method 400 may also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction, as at 410. The one or more tools may also be identified based upon a context on a screen of a user including a region and / or asset that the user is working on and an ongoing conversation history of the user. The tools may include the data tool 220 that is identified for use in response to the question or instruction including structured data. In an example, the structured data may be related to:

[0045] a production summary for a predetermined time period (see FIG. 3);

[0046] a corrective maintenance (CM) work order for a compressor (see FIG. 3); and / or

[0047] identifying pumps with a cavitation risk and suggesting immediate corrective actions (see FIG. 5);

[0048] The tools may also or instead include the RAG tool 250 that is identified for use in response to the question or instruction including unstructured data. In an example, the unstructured data may be related to:

[0049] an effect of a production separation temperature on basic sediment and water (BSW) specifications for crude treatment as well as liquid in a gas processing train (see FIG. 3); and / or

[0050] identifying the pumps with the cavitation risk and suggesting the immediate corrective actions (see FIG. 5).

[0051] The tools may also or instead include the simulation tool 230 that is identified for use in response to the question or instruction relating to simulations. In an example, the simulations may be related to:

[0052] an impact on total production in response to decreasing a speed of an electrical submersible pump (ESP) by a particular amount in a well (see FIG. 3); and / or

[0053] emissions from the compressor based upon a simulation using different energy sources (see FIGS. 3 and 6);

[0054] The tools may also or instead include the analytical tool 240 that is identified for use in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. In an example, the data analysis may be related to:

[0055] carbon dioxide (CO2) emissions for the well in response to switching from electricity to natural gas (see FIG. 3).

[0056] The method 400 may also include generating output data related to the question or instruction using the one or more identified tools, as at 415. In an embodiment, the output data may request additional information from the user. In an example, the output data may include:

[0057] a list of the pumps with the cavitation risk, generated by the data tool, wherein the list also comprises a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk; and / or

[0058] the immediate corrective actions, generated by the RAG tool, wherein the immediate corrective actions comprise reducing a speed of the pump and / or increasing a suction head of the pump;

[0059] The method 400 may also include generating a response to the question or instruction using the LLM agent based upon output data, as at 420. The response may also show one or more sources that were used to generate the output data.

[0060] The method 400 may also include displaying the response, as at 425. In an example, displaying the response may include generating a graph.The method 400 may also include performing an action based upon and / or in response to the response, as at 430. The action may be performed on or in production equipment, a production system, a production facility, or a wellsite. The action may be or include generating and / or transmitting a signal (e.g., using a computing system) that recommends, instructs, or causes a physical action to occur at a wellsite. The action may also or instead include performing the physical action at the wellsite. The physical action may include implementing the immediate corrective actions (see examples above), 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

[0061] In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 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 701B, 701C, and / or 701D (note that computer systems 701B, 701C and / or 701D may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and / or 701D that are located in one or more data centers, and / or located in varying countries on different continents).

[0062] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0063] 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 FIG. 7 storage media 706 is depicted as within computer system 701A, in some embodiments, storage media 706 may 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.

[0064] In some embodiments, computing system 700 contains one or more method execution module(s) 708. In the example of computing system 700, computer system 701A includes the method execution module 708. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

[0065] 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 FIG. 7, and / or computing system 700 may have a different configuration or arrangement of the components depicted in FIG. 7. The various components shown in FIG. 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.

[0066] 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.

[0067] 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, FIG. 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.

[0068] 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.

Examples

Embodiment Construction

[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 present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure 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 firs...

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 694,322, filed on Sep. 13, 2024, which is incorporated by reference herein.BACKGROUND

[0002] Conventional systems that perform production engineering tasks in the oil and gas industry have difficulty in efficiently processing and analyzing large volumes of production data. Conventional systems also have challenges in optimizing complex production systems. Conventional systems also have a limited ability to leverage domain expertise at scale. Therefore, what is needed is an improved system and method that provide faster, more accurate decision-making in production operations.SUMMARY

[0003] A method for performing production engineering tasks is disclosed. The method includes receiving a question or instruction related to an oil and / or gas industry. The question or instruction is received by a large language model (LLM) agent. The method also includes selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include a data tool, a retrieval augmented generation (RAG) tool, a simulation tool, and an analytical tool. The method also includes generating output data related to the question or instruction using the one or more identified tools. The method also includes generating a response to the question or instruction using the LLM agent based upon output data.

[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 a question or instruction related to an oil and / or gas industry. The question or instruction is received by a large language model (LLM) agent. The operations also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include (1) a data tool that is configured to be selected in response to the question or instruction including structured data; (2) a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction including unstructured data; (3) a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; and (4) an analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. The operations also include generating output data related to the question or instruction using the one or more identified tools. The operations also include generating a response to the question or instruction using the LLM agent based upon output data.

[0005] A non-transitory computer-readable medium is also disclosed. The medium includes instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving a question or instruction related to an oil and / or gas industry. The question is related to identifying pumps with a cavitation risk and suggesting immediate corrective actions. The question or instruction is received by a large language model (LLM) agent. The operations also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The one or more tools are also identified based upon a context on a screen of a user. The context includes a region and / or asset that the user is working on and an ongoing conversation history of the user. The tools include (1) a data tool that is configured to be selected in response to the question or instruction including structured data; (2) a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction also including unstructured data; (3) a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; and (4) an analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. The operations also include generating output data related to the question or instruction using the one or more identified tools. The output data requests additional information from the user. The output data includes a list of the pumps with the cavitation risk, generated by the data tool. The list also includes a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk. The output data also includes the immediate corrective actions, generated by the RAG tool. The immediate corrective actions include reducing a speed of the pumps and / or increasing a suction head of the pumps. The operations also include generating a response to the question or instruction using the LLM agent based upon output data. The response shows one or more sources that were used to generate the output data. The operations also include displaying the response, wherein displaying the response comprises generating a graph.

[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] FIG. 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] FIGS. 2A and 2B illustrate a schematic view of a system for performing production engineering tasks in the oil and gas industry, according to an embodiment.

[0010] FIG. 3 illustrates a schematic view of the system showing multi-agent capabilities, according to an embodiment.

[0011] FIG. 4 illustrates a flowchart of a method for performing production engineering tasks in the oil and gas industry, according to an embodiment.

[0012] FIGS. 5 and 6 illustrate schematic view of the system generating responses for different questions or instructions, according to an embodiment.

[0013] FIG. 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

[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 present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure 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 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.

[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.System Overview

[0018] FIG. 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).

[0019] In the example of FIG. 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.

[0020] 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.

[0021] 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.

[0022] In the example of FIG. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. 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 FIG. 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0023] 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.).

[0024] 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.).

[0025] 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.).

[0026] FIG. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and 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.

[0027] 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.

[0028] In the example of FIG. 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.

[0029] 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).

[0030] In the example of FIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. 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.

[0031] In the example of FIG. 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, FIG. 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.).

[0032] FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0033] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the 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.).Generative AI Agents for Production Engineering in Oil and Gas

[0034] The present disclosure relates to a system of generative AI agents designed to assist with production engineering tasks in the oil and gas industry. The system includes multiple specialized AI agents, including a subject matter expert (SME) agent, a data agent, and / or a simulator agent that work together to analyze data, run simulations, and provide insights to optimize production operations.

[0035] The system may include an AI orchestration hub that manages and coordinates the different AI agents. The system may also include the SME agent that provides domain expertise and (e.g., best) practices. The system may also include the data agent which analyzes real-time and historical production data. The system may also include the simulator agent, which runs production simulations and optimizations.

[0036] The system may provide integration of multiple specialized AI agents to tackle complex production engineering tasks. The system may also provide a natural language interface for interacting with the system. The system may also include an ability to combine real-time data, simulations, and domain expertise. Accordingly, the system may provide faster and more accurate production optimization, improved decision making through AI-assisted insights, reduced downtime, and increased efficiency of operations.

[0037] These benefits may be provided because the AI agents can rapidly process and analyze large datasets. In addition, the simulator agent enables quick optimization of production scenarios. The SME agent provides scalable access to domain knowledge. The system also provides AI-assisted insights to support faster, data-driven decisions.

[0038] The system may have the following applications: production optimization, well performance analysis, equipment maintenance scheduling, reservoir management, autonomous production management and integration with IoT sensors, or a combination thereof. Other applications and / or uses may be or include integration into existing production management workflows, development of custom AI agents for specific production challenges, creation of digital twin systems powered by AI agents, use in training and knowledge transfer for new engineers, or a combination thereof.

[0039] The system may provide improvements over conventional systems such as an integration of multiple AI capabilities (e.g., data analysis, simulation, domain expertise), a natural language interface for easier interaction, an ability to combine real-time data with simulations and expert knowledge, or a combination thereof. Other improvements may be or include faster analysis and decision-making, more comprehensive insights by combining multiple data sources and AI capabilities, easier access to domain expertise through natural language queries, scalability to handle large, complex production systems, or a combination thereof.Exemplary System

[0040] FIGS. 2A and 2B illustrate a schematic view of a system 200 for performing production engineering tasks in the oil and gas industry, according to an embodiment. As shown, the system 200 may include a generative artificial intelligence (GenAI) agent 210. The agent 210 may include and / or be configured to access one or more tools. The tools may include one or more data tools 220 that are configured to determine or provide asset performance, production assurance, maintenance records, or a combination thereof. The tools may also or instead include one or more simulation tools 230 that are configured to simulate steady-state multiphase flow and / or thermodynamic fluid modelling for the oil and gas industry. IN an example, the simulation tools 230 may be or include PIPESIM® and / or SYMMETRY®. The tools may also or instead include one or more analytical tools 240 that are configured to perform data-driven machine-learning (ML) and / or physics-based ML. The tools may also or instead include one or more retrieval augmented generation (RAG) tools 250 that are configured to retrieve operational manuals and / or design documents. In an embodiment, once a user is authenticated to SLB's DELFI® platform, the user can access multiple applications and services based on their feature subscriptions and data access permissions, without having to log in repeatedly.

[0041] FIG. 3 illustrates a schematic view of the system 200 showing multi-agent capabilities, according to an embodiment. The data tools 220 may be configured to answer questions from structured data. The analytical tools 240 may be configured to run on-demand data analysis involving ML models and physics-based calculations. The simulation tools 230 may be configured to run on-demand simulations. The RAG tools 250 may be configured to answer questions from unstructured data.Exemplary Method

[0042] FIG. 4 illustrates a flowchart of a method 400 for performing production engineering tasks in the oil and gas industry, 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.

[0043] The method 400 may include receiving a question or instruction related to an oil and / or gas industry, as at 405. The question or instruction is received by a large language model (LLM) agent.

[0044] The method 400 may also include selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction, as at 410. The one or more tools may also be identified based upon a context on a screen of a user including a region and / or asset that the user is working on and an ongoing conversation history of the user. The tools may include the data tool 220 that is identified for use in response to the question or instruction including structured data. In an example, the structured data may be related to:

[0045] a production summary for a predetermined time period (see FIG. 3);

[0046] a corrective maintenance (CM) work order for a compressor (see FIG. 3); and / or

[0047] identifying pumps with a cavitation risk and suggesting immediate corrective actions (see FIG. 5);

[0048] The tools may also or instead include the RAG tool 250 that is identified for use in response to the question or instruction including unstructured data. In an example, the unstructured data may be related to:

[0049] an effect of a production separation temperature on basic sediment and water (BSW) specifications for crude treatment as well as liquid in a gas processing train (see FIG. 3); and / or

[0050] identifying the pumps with the cavitation risk and suggesting the immediate corrective actions (see FIG. 5).

[0051] The tools may also or instead include the simulation tool 230 that is identified for use in response to the question or instruction relating to simulations. In an example, the simulations may be related to:

[0052] an impact on total production in response to decreasing a speed of an electrical submersible pump (ESP) by a particular amount in a well (see FIG. 3); and / or

[0053] emissions from the compressor based upon a simulation using different energy sources (see FIGS. 3 and 6);

[0054] The tools may also or instead include the analytical tool 240 that is identified for use in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations. In an example, the data analysis may be related to:

[0055] carbon dioxide (CO2) emissions for the well in response to switching from electricity to natural gas (see FIG. 3).

[0056] The method 400 may also include generating output data related to the question or instruction using the one or more identified tools, as at 415. In an embodiment, the output data may request additional information from the user. In an example, the output data may include:

[0057] a list of the pumps with the cavitation risk, generated by the data tool, wherein the list also comprises a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk; and / or

[0058] the immediate corrective actions, generated by the RAG tool, wherein the immediate corrective actions comprise reducing a speed of the pump and / or increasing a suction head of the pump;

[0059] The method 400 may also include generating a response to the question or instruction using the LLM agent based upon output data, as at 420. The response may also show one or more sources that were used to generate the output data.

[0060] The method 400 may also include displaying the response, as at 425. In an example, displaying the response may include generating a graph.The method 400 may also include performing an action based upon and / or in response to the response, as at 430. The action may be performed on or in production equipment, a production system, a production facility, or a wellsite. The action may be or include generating and / or transmitting a signal (e.g., using a computing system) that recommends, instructs, or causes a physical action to occur at a wellsite. The action may also or instead include performing the physical action at the wellsite. The physical action may include implementing the immediate corrective actions (see examples above), 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

[0061] In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 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 701B, 701C, and / or 701D (note that computer systems 701B, 701C and / or 701D may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and / or 701D that are located in one or more data centers, and / or located in varying countries on different continents).

[0062] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0063] 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 FIG. 7 storage media 706 is depicted as within computer system 701A, in some embodiments, storage media 706 may 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.

[0064] In some embodiments, computing system 700 contains one or more method execution module(s) 708. In the example of computing system 700, computer system 701A includes the method execution module 708. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

[0065] 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 FIG. 7, and / or computing system 700 may have a different configuration or arrangement of the components depicted in FIG. 7. The various components shown in FIG. 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.

[0066] 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.

[0067] 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, FIG. 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.

[0068] 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.

Examples

Embodiment Construction

[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 present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure 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 firs...

Claims

1. A method for performing production engineering tasks, the method comprising:receiving a question or instruction related to an oil and / or gas industry, wherein the question or instruction is received by a large language model (LLM) agent;selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction, wherein the tools comprise a data tool, a retrieval augmented generation (RAG) tool, a simulation tool, and an analytical tool;generating output data related to the question or instruction using the one or more identified tools; andgenerating a response to the question or instruction using the LLM agent based upon output data.

2. The method of claim 1, wherein the data tool is selected in response to the question or instruction including structured data.

3. The method of claim 2, wherein the question or instruction is related to:a production summary for a predetermined time period; and / ora corrective maintenance (CM) work order for a compressor.

4. The method of claim 1, wherein the RAG tool is selected in response to the question or instruction including unstructured data.

5. The method of claim 4, wherein the question or instruction is related to an effect of a production separation temperature on basic sediment and water (BSW) specifications for crude treatment as well as liquid in a gas processing train.

6. The method of claim 1, wherein the simulation tool is selected in response to the question or instruction relating to simulations.

7. The method of claim 6, wherein the question or instruction is related to:an impact on total production in response to decreasing a speed of an electrical submersible pump (ESP) by a particular amount in a well; and / oremissions from the compressor.

8. The method of claim 1, wherein the analytical tool is selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations.

9. The method of claim 8, wherein the question or instruction is related to carbon dioxide (CO2) emissions for the well in response to switching from electricity to natural gas.

10. The method of claim 1, further comprising performing a physical action based upon the response.

11. A computing system, comprising:one or more processors; anda 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 a question or instruction related to an oil and / or gas industry, wherein the question or instruction is received by a large language model (LLM) agent;selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction, wherein the tools comprise:a data tool that is configured to be selected in response to the question or instruction including structured data;a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction including unstructured data;a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; andan analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations;generating output data related to the question or instruction using the one or more identified tools; andgenerating a response to the question or instruction using the LLM agent based upon output data.

12. The computing system of claim 11, wherein the one or more tools are also identified based upon a context on a screen of a user.

13. The computing system of claim 12, wherein the context comprises a region and / or asset that the user is working on and an ongoing conversation history of the user.

14. The computing system of claim 11, wherein the question is related to identifying pumps with a cavitation risk and suggesting immediate corrective actions, and wherein the output data comprises:a list of the pumps with the cavitation risk, generated by the data tool, wherein the list also comprises a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk; andthe immediate corrective actions, generated by the RAG tool, wherein the immediate corrective actions comprise reducing a speed of the pumps and / or increasing a suction head of the pumps.

15. The computing system of claim 11, wherein the output data requests additional information from the user, and wherein the response shows one or more sources that were used to generate the output data.

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 a question or instruction related to an oil and / or gas industry, wherein the question is related to identifying pumps with a cavitation risk and suggesting immediate corrective actions, and wherein the question or instruction is received by a large language model (LLM) agent;selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction, wherein the one or more tools are also identified based upon a context on a screen of a user, wherein the context comprises a region and / or asset that the user is working on and an ongoing conversation history of the user, and wherein the tools comprise:a data tool that is configured to be selected in response to the question or instruction including structured data;a retrieval augmented generation (RAG) tool that is configured to be selected in response to the question or instruction also including unstructured data;a simulation tool that is configured to be selected in response to the question or instruction relating to simulations; andan analytical tool that is configured to be selected in response to the question or instruction including a data analysis involving machine learning (ML) models and physics-based calculations;generating output data related to the question or instruction using the one or more identified tools, wherein the output data requests additional information from the user, and wherein the output data comprises:a list of the pumps with the cavitation risk, generated by the data tool, wherein the list also comprises a type of the pumps, start and end dates for running the pumps, and a severity level of the cavitation risk; andthe immediate corrective actions, generated by the RAG tool, wherein the immediate corrective actions comprise reducing a speed of the pumps and / or increasing a suction head of the pumps;generating a response to the question or instruction using the LLM agent based upon output data, wherein the response shows one or more sources that were used to generate the output data; anddisplaying the response, wherein displaying the response comprises generating a graph.

17. The non-transitory computer-readable medium of claim 16, wherein the simulation tool and the analytical tool are not selected due to the question or instruction not being related to simulation, ML models, and / or physics-based calculations.

18. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise performing an action in response to and / or based upon the response.

19. The non-transitory computer-readable medium of claim 18, wherein the action comprises generating and / or transmitting a signal that recommends, instructs, or causes a physical action to occur.

20. The non-transitory computer-readable medium of claim 19, wherein the physical action implements the immediate corrective actions.

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