Computing systems and methods for energy data management

Abstract

An Energy Data Platform (2400-1) may include one or more computing systems including a plurality of data type platforms comprising a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (GenAI) platform. The computing systems may also include an application management layer (1320) configured to receive a plurality of datasets from the plurality of data type platforms and a unification services layer (1310) configured to convert at least a portion the plurality of datasets acquired from a first data type platform of the plurality of data type platforms into a plurality of converted datasets. The plurality of converted datasets is accessible to a second data type platform of the plurality of data type platforms. The computing systems may also include a first network (1305) for accessing the Energy Data Platform.

Description

Computing Systems and Methods for Energy Data ManagementCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 579,878. entitled, "‘Computing Systems and Methods for Energy Management,” filed on August, 31, 2023, which is incorporated herein in its entirety for all purposes.BACKGROUND

[0002] Oilfield, energy' creation and transmission, and support operations are complex and involve systems, as well as operators, collaborating to accomplish a desired outcome. In many cases, the oilfield operations and other energy generation, capture, and transmission environments are conducted in remote environments impacting the ability to be connected to a stable, reliable, or deterministic network. With this in mind, the ability to collaborate and coordinate may assist in improving these operations. That is, the ability' to provide a specific dataset to a particular system or individual may be useful in improving operations, such as maximizing production, minimizing energy losses, and the like.

[0003] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.SUMMARY

[0004] A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may' encompass a variety of aspects that may not be set forth below.

[0005] In one embodiment, an Energy Data Platform (2400-1) may include one or more computing systems including a plurality of data type platforms comprising a Subsurface Platform (130 IB), an Operations Platform ( 1301 C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (GenAI) platform. The computing systems may also include an application management layer (1320) configured to receive a plurality of datasets from the plurality of data type platforms and a unification services layer (1310) configured to convert at least a portion the plurality of datasets acquired from a first data type platform of the plurality of data type platforms into a plurality of converted datasets. The plurality of converted datasets is accessible to a second data type platform of the plurality of data type platforms. The computing systems may also include a first network (1305) for accessing the Energy Data Platform.

[0006] In another embodiment, a method for an Energy Worker to use an Energy' Data Platform to utilize a plurality of energy data objects, comprising at a user computer system: starting a first energy data object working application from an application suite disposed in an application and management services layer, using the first energy data object working application to retrieve a first energy data object from a first energy' data management and storage platform, and performing one or more adjustments on the first energy' data object using the first energy data object working application. The computing system may use a cross-domain application from the application suite to retrieve a second energy data object disposed in a second energy data management and storage platform. The cross-domain application uses a data curation and translation application program interface disposed in a unification sendees layer. The computing system may also update the first energy data object based at least in part on the second energy data object and store the updated first energy data object in the first energy data management and storage platform.

[0007] In another embodiment, a method performed by an Energy' Data Platform to provide access to a plurality of energy data objects, may’ include receiving an instruction from a first user computer system to initiate a first energy’ data object working application from an application suite disposed in an application and management services layer in the Energy- Data Platform, executing the first energy' data object working application to identify and retrieve a first energy’ data object from a first energy data management and storage platform, sending a copy of the first energy data object to the first user computer system, and receiving a second instruction from the first user computer system to retrieve an unidentified secondenergy data object disposed in a second energy data management and storage platform. The second instruction may initiate a cross-domain application from the application suite that uses a data curation and translation application program interface disposed in a unification services layer in the Energy Data Platform, and automatically identifies the second energy data object. The method may also include sending a copy of the second energy data object to the first user computer system, receiving from the first user computer system an updated first energy data object that includes changes based at least in part on the second energy data object, and storing the updated first energy data object in the first energy data management and storage platform.

[0008] This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

[0009] Various refinements of the features noted above may be made in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may be made individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any' combination. The brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matterBRIEF DESCRIPTION OF THE DRAWINGS

[0010] Various features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying figures in which like characters represent like parts throughout the figures, wherein.

[0011] Fig. 1 illustrates examples of equipment in a geologic environment, in accordance with embodiments described herein;

[0012] Fig. 2 illustrates examples of equipment and examples of hole types in a wellsite system, in accordance with embodiments described herein;

[0013] Fig. 3 illustrates an example of a planning and operations system, in accordance with embodiments described herein;

[0014] Fig. 4 illustrates an example of a wellsite system and an example of a computing system, in accordance with embodiments described herein;

[0015] Fig. 5 illustrates an example of equipment in a geologic environment, in accordance with embodiments described herein;

[0016] Fig. 6 illustrates a side elevational view of a wind turbine, in accordance with embodiments described herein;

[0017] Fig. 7 illustrates a wind turbine farm, in accordance with embodiments described herein;

[0018] Fig. 8 illustrates a solar panel, in accordance with embodiments described herein, in accordance with embodiments described herein;

[0019] Fig. 9 illustrates a solar panel farm, in accordance with embodiments described herein;

[0020] Fig. 10 illustrates an ocean power generation farm, in accordance with embodiments described herein;

[0021] Fig. 11 illustrates an example of a well construction ecosy stem that includes one or more slips status engines, in accordance with embodiments described herein;

[0022] Fig. 12 illustrates an example of a computing system, in accordance with embodiments described herein;

[0023] Figs. 13A, 13B, 13C, and 13D illustrate various examples of Enterprise Data Platforms, in accordance with embodiments described herein;

[0024] Figs. 14A, 14B, 14C. 14D, and 14E illustrate various examples of Enterprise Data Platforms, in accordance with embodiments described herein;

[0025] Figs. 15 A and 15B illustrate various examples of Enterprise Data Platforms, in accordance with embodiments described herein;

[0026] FIG. 16 illustrates an example Enterprise Data Platform method and use case, in accordance with embodiments described herein; and

[0027] Fig. 17 illustrates an example sustainability platform system, in accordance with embodiments described herein.DETAILED DESCRIPTION

[0028] 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.

[0029] 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 invention. 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.

[0030] The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention 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 and all 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.

[0031] As used herein, the term "if1may be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" may be constmed to mean "upon determining" or "in response to determining" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]," depending on the context.

[0032] The present disclosure is directed to improved computing systems, processing systems, apparatuses, and methods for energy -related data platforms for storage, access, and curation of oilfield and other energy generation and management data. The apparatus and method described cannot be performed manually in any useful sense. Simplified datasets may be used for illustrative purposes but it will be appreciated that the disclosure extends to datasets with many thousands of points thereby necessitating the new hardware-based processing system described herein.

[0033] Examples of oil & gas applications in which the described infrastructure may be deployed include wireline operations, drilling and well construction operations, and production facility and testing operations. Examples of other energy' generation, capture, and transmission environments in which the described infrastructure may be deployed include solar power installations, nuclear power plants, electrical transmission lines and grids, hydroelectric power plants and infrastructure, tidal, current, and wave energy' installations, geothermal power sites, wind energy' sites, and other power generation facilities along with their grids, instrumentation, transmission lines, and sensors that have data emitting capabilities where the data emitted may be collected and managed with management system technologies as described herein.

[0034] Various datasets are emitted, collected, and retained from the systems, tools, processes, and operations in shown in Figs. 1 - 11; these datasets may be stored, managed, and curated in an Energy Data Platform, such as the example of Fig. 13A, 1300-1 , including within the Energy Data Platform, Subsurface Platform 1301B, Operations Platform 1301C, Sustainability' Platform 1301D, and Enterprise Platform 1301E (see also, Figs. 13B - 13D). By way of operation, the datasets collected, analyzed, and generated by these distinct platforms may be related to certain operations associated with the platform itself. That is, each platform may' be an application platform that includes a software environment (e.g., Java) that provides runtime support, libraries, and analysis tools for performing certainanalysis tasks with respect to a particular domain. For example, the subsurface platform 130 IB may include software tools and datasets associated with analyzing a subsurface region of the earth, such as identifying hydrocarbon deposit locations within the subsurface region using seismic datasets. In any case, the datasets accessible to each platform may be acquired and updated by a unification services component 1310 to enable different platform systems, virtual machines, user devices, and the like to analyze the various datasets in a common format. In this way, the present embodiments described herein allow various types of datasets for different domains or cross domains (e.g., energy, sustainability, operations) to be used in conjunction with each other.

[0035] In some embodiments, the unification services component 1310 may enable a generative artificial intelligence (GenAI) platform to access datasets from various other platforms. In this way, the GenAI platform may provide services to create experiences for users based on the domain-specific knowledge provided by the various disparate platforms that make up the management system.

[0036] Keeping this in mind, Fig. 1 shows an example of a geologic environment 120 in which the present embodiments described herein may be implemented therewith. In Fig.1, the geologic environment 120 may be a sedimentary basin that includes layers (e.g., stratification) that include a reservoir 121 and that may be, for example, intersected by a fault 123 (e.g., or faults). As an example, the geologic environment 120 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 122 may include communication circuitry to receive and to transmit information with respect to one or more networks 125. Such information may include information associated with downhole equipment 124, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 126 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 pieces of equipment may provide for measurement, collection, communication, storage, analysis, etc. of data (e.g., for one or more produced resources, 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 125 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0037] Fig. 1 also shows the geologic environment 120 as optionally including equipment 127 and 128 associated with a well that includes a substantially horizontal portion (e.g., a lateral portion) that may intersect with one or more fractures 129. 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 reserv oir 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 the reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 127 and / or 128 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, injection, production, etc. As an example, the equipment 127 and / or 128 may provide for measurement, collection, communication, storage, analysis, etc. of data such as, for example, production data (e.g., for one or more produced resources). As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc.

[0038] Fig. 1 also shows an example of equipment 170 and an example of equipment 180. Such equipment, which may be systems of components, may be suitable for use in the geologic environment 120. While the equipment 170 and 180 are illustrated as land-based, various components may be suitable for use in an offshore system (e.g., an offshore rig(s) in deep water, shallow water, transition zones, etc.).

[0039] The equipment 170 includes a platform 171, a derrick 172, a crown block 173, a line 174, a traveling block assembly 175, drawworks 176 and a landing 177 (e.g., a monkeyboard). As an example, the line 174 may be controlled at least in part via the drawworks 176 such that the traveling block assembly 175 travels in a vertical direction with respect to the platform 171. For example, by drawing the line 174 in. the drawworks 176 may cause the line 174 to run through the crown blockl73 and lift the traveling block assembly 175 skyward away from the platform 171; whereas, by allowing the line 174 out, the drawworks 176 may cause the line 174 to run through the crow n block 173 and lower the traveling block assembly 175 toward the platform 171. Where the traveling block assembly 175 carries pipe (e.g., casing, etc.), tracking of movement of the traveling block 175 may provide an indication as to how much pipe has been deployed.

[0040] A derrick can be a structure used to support a crown block and a traveling block operatively coupled to the crown block at least in part via line. A derrick may be pyramidal in shape and offer a suitable strength-to-weight ratio. A derrick may be movable as a unit or in a piece by piece manner (e.g., to be assembled and disassembled).

[0041] Fig. 2 shows another example of a wellsite system 200 (e.g., at a wellsite that may be onshore or offshore) in which the embodiments described herein may also be employed. As shown, the wellsite system 200 can include a mud tank 201 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 203 that serves as an inlet to a mud pump 204 for pumping mud from the mud tank 201 such that mud flo s to a vibrating hose 206. a drawworks 207 for winching drill line or drill lines 212, a standpipe 208 that receives mud from the vibrating hose 206, a kelly hose 209 that receives mud from the standpipe 208, a gooseneck or goosenecks 210, a traveling block 211, a crown block 213 for carrying the traveling block 211 via the drill line or drill lines 212 (see, e.g., the crown block 173 of Fig. 1), a derrick 214 (see, e.g., the derrick 172 of Fig. 1), a kelly 218 or a top drive 240, a kelly drive bushing 219, a rotary table 220, a drill floor 221, a bell nipple 222, one or more blowout preventers (BOPs) 223, a drillstring 225, a drill bit 226, a casing head 227 and a flow pipe 228 that carries mud and other material to, for example, the mud tank 201.

[0042] In the example system of Fig. 2, a borehole 232 is formed in subsurface formations 230 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc. As shown in the example of Fig. 2, the drillstring 225 is suspended within the borehole 232 and has a drillstring assembly 250 that includes the drill bit 226 at its lower end. As an example, the drillstring assembly 250 may be a bottom hole assembly (BHA).

[0043] The wellsite system 200 can provide for operation of the drillstring 225 and other operations. As shown, the wellsite system 200 includes the traveling block 211 and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200 can include the rotary table 220 where the drillstring 225 pass through an opening in the rotary table 220. In the example of Fig. 2, an uphole control and / or data acquisition system 262 may include circuitry to sense pressure pulses generated by telemetry equipment 252 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

[0044] The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254, a measurement-while-drilling (MWD) module 256, an optional module 258, a rotary-steerable system (RSS) and / or motor 260, and the drill bit 226. Such components or modules may be referred to as tools where a drillstring can include a lurality of tools. Fig. 2 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 272, an S-shaped hole 274, a deep inclined hole 276 and a horizontal hole 278.

[0045] The wellsite system 200 can include one or more sensors 264 that are operatively coupled to the control and / or data acquisition system 262. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 200. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 200 and the offset wellsite are in a common field (e.g., oil and / or gas field). As an example, one or more of the sensors 264 can be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.

[0046] Fig. 3 shows an example of a system 300 that includes various equipment for evaluation 310, planning 320, engineering 330 and operations 340 in accordance with embodiments herein. For example, a drilling workflow framework 301, a seismic-to- simulation framework 302, a technical data framework 303 and a drilling framework 304 may be implemented to perform one or more processes, such as a evaluating a formation 314, evaluating a process 318, generating atrajectory 324, validating a trajectory 328. formulating constraints 334, designing equipment and / or processes based at least in part on constraints 338, performing drilling 344, and evaluating drilling and / or formation 348.

[0047] In the example of Fig. 3, the seismic-to-simulation framework 302 can be, for example, the PETREL framework (SLB, Houston, Texas) and the technical data framework 303 can be, for example, the TECHLOG framework (SLB, Houston, Texas).

[0048] As an example, a framework can include entities that may include earth entities, geological objects or other objects such as wells, surfaces, reserv oirs, etc. Entities can include virtual representations of actual physical entities that are reconstructed for purposes of one or more of evaluation, planning, engineering, operations, etc. Entities mayinclude entities based on data acquired via sensing, observation, etc. (e.g., seismic data and / or other information). 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.

[0049] A framework may be an object-based framework. In such a framework, entities may include entities based on pre-defined classes, for example, to facilitate modeling, analysis, simulation, etc. An 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.

[0050] As an example, a framework may be implemented within or in a manner operatively coupled to the DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas), which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks.

[0051] As an example, a framework can include an analysis component that may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As to simulation, a framework may operatively link to or include a simulator, such as the ECLIPSE reservoir simulator (SLB, Houston Texas), the INTERSECT reservoir simulator (SLB, Houston Texas), etc.

[0052] The aforementioned PETREL framework provides components that allow" for optimization of exploration and development operations. The PETREL framew ork includes seismic to simulation softw are 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, well engineers, reservoir engineers, etc.) can develop collaborative w orkflow s and integrate operations to streamline processes. Such a framework may be considered an application and may beconsidered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0053] As mentioned with respect to the DELFI environment, one or more frameworks may be interoperative and / or run upon one or another. As an example, a framework environment marketed as the OCEAN framework environment (SLB, Houston, Texas) may be utilized, which allows for integration of add-ons (or plug-ins) into a PETREL framework workflow. 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.).

[0054] As an example, a framework can include a model simulation layer along with a framework services layer, a framework core layer and a modules layer. In a framework environment (e.g., OCEAN, DELFI, etc.), a model simulation layer can include or operatively link to a model-centric framework. In an example embodiment, a framework may be considered to be a data-driven application. For example, the PETREL framework can include features for model building and visualization. As an example, a model may include one or more grids where a grid can be a spatial grid that conforms to spatial locations per acquired data (e.g., satellite data, logging data, seismic data, etc.).

[0055] As an example, a model simulation layer may provide domain objects, act as a data source, provide for rendering and provide for various user interfaces. Rendering capabilities may provide a graphical environment in which applications can display their data while user interfaces may provide a common look and feel for application user interface components.

[0056] As an example, domain objects can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, reserv oirs, etc., while property7objects may be used to provide property7values 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).

[0057] As an example, 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 sitesand accessible via one or more networks. As an example, a model simulation layer 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, which can recreate instances of the relevant domain objects.

[0058] As an example, the system 300 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 workflow 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 at least in part in the PETREL framework, for example, that operates on seismic data, seismic attribute(s), etc.

[0059] As an example, seismic data can be data acquired via a seismic survey where sources and receivers are positioned in a geologic environment to emit and receive seismic energy where at least a portion of such energy can reflect off subsurface structures. As an example, a seismic data analysis framework or frameworks (e.g., consider the OMEGA framework, marketed by Schlumberger, Houston, Texas) may be utilized to determine depth, extent, properties, etc. of subsurface structures. As an example, seismic data analysis can include forward modeling and / or inversion, for example, to iteratively build a model of a subsurface region of a geologic environment. As an example, a seismic data analysis framework may be part of or operatively coupled to a seismic-to-simulation framework (e.g., the PETREL framework, etc ).

[0060] As an example, a workflow may be a process implementable at least in part in a framework environment and by one or more frameworks. As an example, a workflow may include one or more worksteps that access a set of instructions such as a plug-in (e.g., external executable code, etc.). As an example, a framework environment may be cloudbased where cloud resources are utilized that may be operatively coupled to one or more pieces of field equipment such that data can be acquired, transmitted, stored, processed,analyzed, etc., using features of a framework environment. As an example, a framework environment may employ various types of services, which may be backend, frontend or backend and frontend services. For example, consider a client-server type of architecture where communications may occur via one or more application programming interfaces (APIs), one or more microservices, etc.

[0061] As an example, a framework may provide for modeling petroleum systems. For example, the modeling framework marketed as the PETROMOD framework (SLB, Houston, Texas), which includes features for input of various types of information (e.g., seismic, well, geological, etc.) to model evolution of a sedimentary basin. The PETROMOD framework provides for petroleum systems modeling via input of various data such as seismic data, well data and other geological data, for example, to model evolution of a sedimentary basin. The PETROMOD framework may predict if, and how, a reservoir has been charged with hydrocarbons, including, for example, the source and timing of hydrocarbon generation, migration routes, quantities, pore pressure and hydrocarbon type in the subsurface or at surface conditions. In combination with a framework such as the PETREL framework, workflows may be constructed to provide basin-to-prospect scale exploration solutions. Data exchange between frameworks can facilitate construction of models, analysis of data (e.g., PETROMOD framework data analyzed using PETREL framew ork capabilities), and coupling of workflows.

[0062] As mentioned, a drillstring can include various tools that may make measurements. As an example, a wireline tool or another type of tool may be utilized to make measurements. As an example, a tool may be configured to acquire electrical borehole images. As an example, the fullbore Formation MicroImager (FMI) tool (Schlumberger, Houston, Texas) can acquire borehole image data. A data acquisition sequence for such a tool can include running the tool into a borehole with acquisition pads closed, opening and pressing the pads against a wall of the borehole, delivering electrical current into the material defining the borehole while translating the tool in the borehole, and sensing current remotely, which is altered by interactions with the material.

[0063] Analysis of formation information may reveal features such as, for example, vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc. As an example, a tool may acquire information that may help to characterize areservoir, optionally a fractured reservoir where fractures may be natural and / or artificial (e.g., hydraulic fractures).

[0064] As an example, information acquired by a tool or tools may be analyzed using a framework such as the TECHLOG framework. As an example, the TECHLOG framework can be interoperable with one or more other frameworks such as, for example, the PETREL framework.

[0065] As an example, various aspects of a workflow may be completed automatically, may be partially automated, or may be completed manually, as by a human user interfacing with a software application that executes using hardware (e.g.. local and / or remote). As an example, a workflow may be cyclic, and may include, as an example, four stages such as, for example, an evaluation stage (see, e.g., the evaluation equipment 310), a planning stage (see, e.g., the planning equipment 320), an engineering stage (see. e.g., the engineering equipment 330) and an execution stage (see, e.g.. the operations equipment 340). As an example, a workflow may commence at one or more stages, which may progress to one or more other stages (e.g., in a serial manner, in a parallel manner, in a cyclical manner, etc.).

[0066] As an example, a workflow can commence with an evaluation stage, which may include a geological service provider evaluating a formation (see, e g., the evaluation block 314). As an example, a geological service provider may undertake the formation evaluation using a computing system executing a software package tailored to such activity'; or, for example, one or more other suitable geology platforms may be employed (e.g., alternatively or additionally). As an example, the geological service provider may evaluate the formation, for example, using earth models, geophysical models, basin models, petrotechnical models, combinations thereof, and / or the like. Such models may take into consideration a variety of different inputs, including offset well data, seismic data, pilot well data, other geologic data, etc. The models and / or the input may be stored in the database maintained by the server and accessed by the geological service provider.

[0067] As an example, a workflow may progress to a geology and geophysics ("G&G") service provider, which may generate a well trajectory (see, e.g., the generation block 324), which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework. As an example, a G&G service provider may determine a well trajectory or a section thereof, based on. for example, one ormore model(s) provided by a formation evaluation (e.g., per the evaluation block 314), and / or other data, e.g., as accessed from one or more databases (e.g.. maintained by one or more servers, etc.). As an example, a well trajectory may take into consideration various “basis of design” (BOD) constraints, such as general surface location, target (e.g., reservoir) location, and the like. As an example, a trajectory' may incorporate information about tools, bottomhole assemblies, casing sizes, etc., that may be used in drilling the well. A well trajectory determination may take into consideration a variety of other parameters, including risk tolerances, fluid weights and / or plans, bottom-hole pressures, drilling time, etc.

[0068] As an example, a workflow may progress to a first engineering service provider (e.g., one or more processing machines associated therewith), which may validate a well trajectory and, for example, relief well design (see, e.g., the validation block 328). Such a validation process may include evaluating physical properties, calculations, risk tolerances, integration with other aspects of a workflow, etc. As an example, one or more parameters for such determinations may be maintained by a server and / or by the first engineering service provider; noting that one or more model(s), well trajectory (ies), etc. may be maintained by a server and accessed by the first engineering service provider. For example, the first engineering service provider may include one or more computing systems executing one or more software packages. As an example, where the first engineering service provider rejects or otherwise suggests an adjustment to a well trajectory, the well trajectory may be adjusted or a message or other notification sent to the G&G service provider requesting such modification.

[0069] As an example, one or more engineering service providers (e.g.. first, second, etc.) may provide a casing design, bottom-hole assembly (BHA) design, fluid design, and / or the like, to implement a well trajectory' (see, e.g., the design block 338). In some embodiments, a second engineering service provider may perform such design using one of more software applications. Such designs may be stored in one or more databases maintained by one or more servers, which may, for example, employ STUDIO framework tools (Schlumberger, Houston, Texas), and may be accessed by one or more of the other service providers in a workflow.

[0070] As an example, a second engineering service provider may seek approval from a third engineering service provider for one or more designs established along with a well trajectory . In such an example, the third engineering service provider may consider variousfactors as to whether the well engineering plan is acceptable, such as economic variables (e.g., oil production forecasts, costs per barrel, risk, drill time, etc.), and may request authorization for expenditure, such as from the operating company’s representative, wellowner’s representative, or the like (see, e.g., the formulation block 334). As an example, at least some of the data upon which such determinations are based may be stored in one or more database maintained by one or more servers. As an example, a first, a second, and / or a third engineering service provider may be provided by a single team of engineers or even a single engineer, and thus may or may not be separate entities.

[0071] As an example, where economics may be unacceptable or subject to authorization being withheld, an engineering service provider may suggest changes to casing, a bottom-hole assembly, and / or fluid design, or otherwise notify and / or return control to a different engineering service provider, so that adjustments may be made to casing, a bottomhole assembly, and / or fluid design. Where modifying one or more of such designs is impracticable within well constraints, trajectory, etc., the engineering service provider may suggest an adjustment to the well trajectory and / or a workflow may return to or otherwise notify an initial engineering service provider and / or a G&G service provider such that either or both may modify the well trajectory.

[0072] As an example, a workflow can include considering a well trajectory, including an accepted well engineering plan, and a formation evaluation. Such a w orkflow may then pass control to a drilling service provider, which may implement the well engineering plan, establishing safe and efficient drilling, maintaining well integrity, and reporting progress as well as operating parameters (see. e.g., the blocks 344 and 348). As an example, operating parameters, formation encountered, data collected while drilling (e.g., using logging-while-drilling or measuring-while-drilling technology), may be returned to a geological service provider for evaluation. As an example, the geological service provider may then re-evaluate the well trajectory, or one or more other aspects of the well engineering plan, and may, in some cases, and potentially within predetermined constraints, adjust the well engineering plan according to the real -life drilling parameters (e.g., based on acquired data in the field, etc.).

[0073] Whether the well is entirely drilled, or a section thereof is completed, depending on the specific embodiment, a workflow may proceed to a post review (see, e.g., the evaluation block 318). As an example, a post review7may include reviewing drillingperformance. As an example, a post review may further include reporting the drilling performance (e.g., to one or more relevant engineering, geological, or G&G service providers).

[0074] Various activities of a workflow may be performed consecutively and / or maybe performed out of order (e.g., based partially on information from templates, nearby wells, etc. to fill in any gaps in information that is to be provided by another service provider). As an example, undertaking one activity may affect the results or basis for another activity, and thus may, either manually or automatically, call for a variation in one or more workflow activities, work products, etc. As an example, a server may allow for storing information on a central database accessible to various service providers where variations may be sought by communication with an appropriate service provider, may be made automatically, or may otherwise appear as suggestions to the relevant service provider. Such an approach may be considered to be a holistic approach to a well workflow, in comparison to a sequential, piecemeal approach.

[0075] As an example, various actions of a workflow may be repeated multiple times during drilling of a wellbore. For example, in one or more automated systems, feedback from a drilling service provider may be provided at or near real-time, and the data acquired during drilling may be fed to one or more other service providers, which may adjust its piece of the workflow accordingly. As there may be dependencies in other areas of the workflow, such adjustments may permeate through the workflow, e.g., in an automated fashion. In some embodiments, a cyclic process may additionally or instead proceed after a certain drilling goal is reached, such as the completion of a section of the wellbore, and / or after the drilling of the entire wellbore, or on a per-day, week, month, etc. basis.

[0076] Well planning can include determining a path of a well (e.g., a trajectory) that can extend to a reservoir, for example, to economically produce fluids such as hydrocarbons therefrom. Well planning can include selecting a drilling and / or completion assembly which may be used to implement a well plan. As an example, various constraints can be imposed as part of well planning that can impact design of a well. As an example, such constraints may be imposed based at least in part on information as to known geology of a subterranean domain, presence of one or more other wells (e.g., actual and / or planned, etc.) in an area (e.g., consider collision avoidance), etc. As an example, one or more constraints may be imposed based at least in part on characteristics of one or more tools, components, etc. As anexample, one or more constraints may be based at least in part on factors associated with drilling time and / or risk tolerance.

[0077] As an example, a system can allow for a reduction in waste, for example, as may be defined according to LEAN. In the context of LEAN, consider one or more of the following types of waste: transport (e.g., moving items unnecessarily, whether physical or data); inventory (e.g., components, whether physical or informational, as work in process, and finished product not being processed); motion (e.g., people or equipment moving or walking unnecessarily to perform desired processing); waiting (e.g., waiting for information, interruptions of production during shift change, etc.); overproduction (e.g., production of material, information, equipment, etc. ahead of demand); over processing (e.g.. resulting from poor tool or product design creating activity); and defects (e.g., effort involved in inspecting for and fixing defects whether in a plan, data, equipment, etc.). As an example, a system that allows for actions (e.g., methods, workflows, etc.) to be performed in a collaborative manner can help to reduce one or more types of waste.

[0078] As an example, a system can be utilized to implement a method for facilitating distributed well engineering, planning, and / or drilling system design across multiple computation devices where collaboration can occur among various different users (e.g., some being local, some being remote, some being mobile, etc.). In such a system, the various users via appropriate devices may be operatively coupled via one or more networks (e.g., local and / or wide area networks, public and / or private networks, land-based, marine-based and / or areal networks, etc ).

[0079] As an example, a system may allow well engineering, planning, and / or drilling system design to take place via a subsystems approach where a wellsite system is composed of various subsystems, which can include equipment subsystems and / or operational subsystems (e.g.. control subsystems, etc.). As an example, computations may be performed using various computational platforms / devices that are operatively coupled via communication links (e.g., network links, etc.). As an example, one or more links may be operatively coupled to a common database (e.g., a server site, etc ). As an example, a particular server or servers may manage receipt of notifications from one or more devices and / or issuance of notifications to one or more devices. As an example, a system may be implemented for a project where the system can output a well plan, for example, as a digitalwell plan, a paper well plan, a digital and paper well plan, etc. Such a well plan can be a complete well engineering plan or design for the particular project.

[0080] Although not shown, in addition to the subsystems described above, in some embodiments, a Generative Al (GenAI) system or subsystem may be incorporated into the system 300 to provide new content based on machine learning models trained to provide responses to freestyle user prompts. In some embodiments, the GenAI system may employ neural networks that may be trained w ith large language models to understand context and generate text. In some embodiments, the GenAI system may employ various ty pes of models to assist in providing results. The models may include tranformers. variational autoencoders (VAEs), generative adversarial networks (GANs). Autoregressive models, diffusion models, neural styple transfer models, and the like. As such, the GenAI system may interact or engage with the various workflows, models, datasets, data objects, or other information that may be provided by other subsystems as described above. The GenAI system may be integrated within the embodiments described below to provide GenAI results and analysis for various domains and w orkflows, while allowing a user to operate on a single platform system.

[0081] Fig. 4 shows an example of a wellsite system 400, specifically, Fig. 4 shows the wellsite system 400 in an approximate side view' and an approximate plan view' along with a block diagram of a system 470.

[0082] In the example of Fig. 4, the wellsite system 400 can include a cabin 410, a rotary table 422, drawworks 424, a mast 426 (e.g., optionally carrying a top drive, etc.), mud tanks 430 (e.g., with one or more pumps, one or more shakers, etc.), one or more pump buildings 440, a boiler building 442, an HPU building 444 (e.g., with a rig fuel tank, etc.), a combination building 448 (e.g., with one or more generators, etc.), pipe tubs 462, a catwalk 464, a flare 468, etc. Such equipment can include one or more associated functions and / or one or more associated operational risks, which may be risks as to time, resources, and / or humans.

[0083] As shown in the example of Fig. 4. the wellsite system 400 can include a system 470 that includes one or more processors 472, memory 474 operatively coupled to at least one of the one or more processors 472, instructions 476 that can be, for example, stored in the memory 474, and one or more interfaces 478. As an example, the system 470 caninclude one or more processor-readable media that include processor-executable instructions executable by at least one of the one or more processors 472 to cause the system 470 to control one or more aspects of the wellsite system 400. In such an example, the memory 474 can be or include the one or more processor-readable media where the processor-executable instructions can be or include instructions. As an example, a processor-readable medium can be a computer-readable storage medium that is not a signal and that is not a carrier wave.

[0084] Fig. 4 also shows a battery 480 that may be operatively coupled to the system 470, for example, to power the system 470. As an example, the battery' 480 may be a back-up battery that operates when another power supply is unavailable for powering the system 470. As an example, the battery 480 may be operatively coupled to a network, which may be a cloud network. As an example, the battery 480 can include smart battery circuitry and may be operatively coupled to one or more pieces of equipment via a SMBus or other type of bus.

[0085] In the example of Fig. 4, services 490 are shown as being available, for example, via a cloud platform. Such services can include data services 492, query services 494 and drilling services 496. As an example, the services 490 may be part of a system such as the system 300 of Fig. 3.

[0086] Fig. 5 shows a schematic diagram depicting an example of a drilling operation of a directional well in multiple sections. Wellsite drilling system 500 includes systems that generate data that may be stored, managed, and curated in one or more of an Operations Platform, such as in Fig. 13A (e.g., 1301C) as discussed below (see also Figs. 13B and 13C), and some subsurface related data may be stored in a Subsurface Platform 130 IB.

[0087] The drilling operation depicted in Fig. 5 includes a wellsite drilling system 500 and a field management tool 520 for managing various operations associated with drilling a bore hole 550 of a directional well 517. The wellsite drilling system 500 includes various components (e.g., drillstring 512, annulus 513, bottom hole assembly (BHA) 514, kelly 515, mud pit 516, etc.). As shown in the example of Fig. 5, a target reservoir may be located away from (as opposed to directly under) the surface location of the well 517. In such an example, special tools or techniques may be used to ensure that the path along the bore hole 550 reaches the particular location of the target reservoir.

[0088] As an example, the BHA 514 may include sensors 508, a rotary steerable system (RSS) 509, and a bit 510 to direct the drilling toward the target guided by a pre-determined survey program for measuring location details in the well. Furthermore, the subterranean formation through which the directional well 517 is drilled may include multiple layers (not shown) with varying compositions, geophysical characteristics, and geological conditions. Both the drilling planning during the well design stage and the actual drilling according to the drilling plan in the drilling stage may be performed in multiple sections (see, e.g., sections 501, 502. 503 and 504), which may correspond to one or more of the multiple layers in the subterranean formation. For example, certain sections (e.g.. sections 501 and 502) may use cement 507 reinforced casing 506 due to the particular formation compositions, geophysical characteristics, and geological conditions.

[0089] In the example of Fig. 5, a surface unit 511 may be operatively linked to the wellsite drilling system 500 and the field management tool 520 via communication links 518. The surface unit 511 may be configured with functionalities to control and monitor the drilling activities by sections in real time via the communication links 518. The field management tool 520 may be configured with functionalities to store oilfield data (e.g., historical data, actual data, surface data, subsurface data, equipment data, geological data, geophysical data, target data, anti-target data, etc.) and determine relevant factors for configuring a drilling model and generating a drilling plan. The oilfield data, the drilling model, and the drilling plan may be transmitted via the communication link 518 according to a drilling operation workflow. The communication links 518 may include a communication subassembly.

[0090] During various operations at a wellsite, data can be acquired for analysis and / or monitoring of one or more operations. Such data may include, for example, subterranean formation, equipment, historical and / or other data. Static data can relate to, for example, formation structure and geological stratigraphy that define the geological structures of the subterranean formation. Static data may also include data about a bore, such as inside diameters, outside diameters, and depths. Dynamic data can relate to. for example, fluids flowing through the geologic structures of the subterranean formation over time. The dynamic data may include, for example, pressures, fluid compositions (e.g., gas oil ratio, water cut, and / or other fluid compositional information), states of various equipment, and other information.

[0091] The static and dynamic data collected via a bore, a formation, equipment, etc. may be used to create and / or update a three-dimensional model of one or more subsurfaceformations. As an example, static and dynamic data from one or more other bores, fields, etc. may be used to create and / or update a three-dimensional model. As an example, hardware sensors, core sampling, and well logging techniques may be used to collect data. As an example, static measurements may be gathered using downhole measurements, such as core sampling and w ell logging techniques. Well logging involves deployment of a dow nhole tool into the wellbore to collect various downhole measurements, such as density, resistivity, etc., at various depths. Such well logging may be performed using, for example, a drilling tool and / or a wireline tool, or sensors located on downhole production equipment. Once a well is formed and completed, depending on the purpose of the well (e.g., injection and / or production), fluid may flow to the surface (e.g., and / or from the surface) using tubing and other completion equipment. As fluid passes, various dynamic measurements, such as fluid flow rates, pressure, and composition may be monitored. These parameters may be used to determine various characteristics of a subterranean formation, dow nhole equipment, downhole operations, etc.

[0092] As an example, a system can include a framework that can acquire data such as, for example, real time data associated with one or more operations such as, for example, a drilling operation or drilling operations. As an example, consider the PERFORMTM toolkit framework (SLB, Houston, Texas).

[0093] As an example, a service can be or include one or more of OPTIDRILLTM, OPTILOGTM and / or other services marketed by Schlumberger Limited, Houston, Texas.

[0094] The OPTIDRILLTM technology can help to manage downhole conditions and BHA dynamics as a real time drilling intelligence service. The sendee can incorporate a rigsite display (e.g., a w ellsite display) of integrated downhole and surface data that provides actionable information to mitigate risk and increase efficiency. As an example, such data may be stored, for example, to a database system (e.g., consider a database system associated with the STUDIOTM framework).

[0095] The OPTILOGTM technology can help to evaluate drilling system performance with single- or multiple-location measurements of drilling dynamics and internal temperature from a recorder. As an example, post-run data can be analyzed to provide input for future well planning.

[0096] As an example, information from a drill bit database may be accessed and utilized. For example, consider information from Smith Bits (SLB. Houston. Texas), which may include information from various operations (e.g., drilling operations) as associated with various drill bits, drilling conditions, formation types, etc.

[0097] As an example, one or more QTRAC services (SLB, Houston Texas) may be provided for one or more wellsite operations. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[0098] As an example, one or more M-I SWACO™ services (M-I L.L.C., Houston, Texas) may be provided for one or more wellsite operations. For example, consider services for value-added completion and reservoir drill-in fluids, additives, cleanup tools, and engineering. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[0099] As an example, one or more ONE-TRAX™ services (e.g., via the ONE- TRAX software platform, M-I L.L.C., Houston, Texas) may be provided for one or more wellsite operations. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[0100] As an example, various operations can be defined with respect to WITS or WITSML, which are acronyms for well-site information transfer specification or standard (WITS) and markup language (WITSML). WITS / WITSML specify how a drilling rig or offshore platform drilling rig can communicate data. For example, as to slips, which are an assembly that can be used to grip a drillstring in a relatively non-damaging manner and suspend the drillstring in a rotary table, WITS / WITSML define operations such as “bottom to slips” time as a time interval between coming off bottom and setting slips, for a current connection; “in slips” as a time interval between setting the slips and then releasing them, for a current connection; and “slips to bottom’’ as a time interval between releasing the slips and returning to bottom (e.g., setting weight on the bit), for a current connection.

[0101] Well construction can occur according to various procedures, which can be in various forms. As an example, a procedure can be specified digitally and may be, for example, a digital plan such as a digital well plan. A digital well plan can be an engineering plan for constructing a wellbore. As an example, procedures can include information such as well geometries, casing programs, mud considerations, well control concerns, initial bitselections, offset well information, pore pressure estimations, economics and special procedures that may be utilized during the course of well construction, production, etc. While a drilling procedure can be carefully developed and specified, various conditions can occur that call for adjustment to a drilling procedure.

[0102] As an example, an adjustment can be made at a rigsite when acquisition equipment acquires information about conditions, which may be for conditions of drilling equipment, conditions of a formation, conditions of fluid(s), conditions as to environment (e.g., weather, sea, etc.), etc. Such an adjustment may be made on the basis of personal knowledge of one or more individuals at a rigsite. As an example, an operator may understand that conditions call for an increase in mudflow rate, a decrease in weight on bit, etc. Such an operator may assess data as acquired via one or more sensors (e.g., torque, temperature, vibration, etc.). Such an operator may call for performance of a procedure, which may be a test procedure to acquire additional data to understand better actual physical conditions and physical phenomena that may occur or that are occurring. An operator may be under one or more time constraints, which may be driven by physical phenomena, such as fluid flow, fluid pressure, compaction of rock, borehole stability, etc. In such an example, decision making by the operator can depend on time as conditions evolve. For example, a decision made at one fluid pressure may be sub-optimal at another fluid pressure in an environment where fluid pressure is changing. In such an example, timing as to implementing a decision as an adjustment to a procedure can have a broad ranging impact. An adjustment to a procedure that is made too late or too early can adversely impact other procedures compared to an adjustment to a procedure that is made at an optimal time (e.g., and implemented at the optimal time).

[0103] Figs 6 - 10 depict various alternative energy systems in which the embodiments described herein may be implemented. The alternative eenery systems may include wind, solar, and tidal- or wave-based generation, and said systems generate data that may be stored, managed, and curated in a Sustainability Platform, such as in Fig. 13A (1301D as discussed below (see also Figs. 13B and 13C).

[0104] As shown in FIG. 6, a wind turbine 600 generally comprises a nacelle 602 housing a generator (not shown in FIG. 6). Nacelle 602 is a housing mounted atop a tower 604, only a portion of which is shown in FIG. 6. The tower 604 may be on land or at sea. The height of tow er 604 is selected based upon factors and conditions known in theart, and may extend to heights up to 60 meters or more. The wind turbine 600 may be installed on any terrain providing access to areas having desirable wind conditions. The terrain may vary greatly and may include, but is not limited to, mountainous terrain or offshore locations. Wind turbine 600 also comprises a rotor 606 that includes one or more rotor blades 608. Although wind turbine 600 illustrated in FIG. 6 includes three rotor blades 608, there are no specific limits on the number of rotor blades 608 required.

[0105] The wind turbine 600 and tower 604 includes a large variety of equipment and components that are susceptible to vandalism and / or burglary7, particularly in wind turbines 600 installed in more remote locations. Certain components are susceptible to theft, while others are subject to damage or destruction from access. In addition, exterior surfaces of the tower 604 may be damaged, requiring repair or servicing.

[0106] Wind turbine 600 utilizes one or more cameras, sensors, and other devices 610 that may emit data for transmission to a remote location for analysis to determine whether components are missing, damaged or otherwise require maintenance. In addition, if unauthorized personnel are detected, authorities or emergency services may be contacted and / or dispatched to the wind turbine 600 and tower 604.

[0107] FIG. 7 shows a wind turbine monitoring system 700 according to an embodiment of the present disclosure. The system 200 includes a central monitoring device 701 and a plurality of wind turbines 600 in one or more fields. The number of wind turbines 600 in the system 700 is not limited and may include one or a large number of wind turbines 600. A device 610 is mounted on or within one or more of the wind turbines 600 and respective towers 604, and generates data 710 that may include without limitation operating and environmental conditions, computational capability of the data processing infrastructure (including ability to manage and use cryptography keys, hashes and capabilities), equipment- related data, sensor data and measurements, maintenance information, visual data from camera(s), and the like. The central monitoring device 701 may be a data acquisition device such as a computer, a data storage device, or other analysis tool. In another embodiment, the central monitoring device 701 may be a communication device, tablet, or other computational device usable by personnel. In another embodiment the central monitoring device 701 is the power control for a wind turbine farm or a utility operating the wind turbine farm. The central monitoring device 701 may be autonomous or may be integrated within the wind farm control. The data 710 may be transmitted to and / or from the wind turbine 600 andtower 704 in order to provide control or otherwise communicate with the wind turbine 600 in response to a condition requiring maintenance in response to any received signals. In certain embodiments, equipment or other operational parameters may be transmitted and received.

[0108] In some embodiments, data 710 includes blockchain managed data in accordance with embodiments according to the present disclosure. In some embodiments, central monitoring device 701 places data 710 in a cloud 715 for access by others over a network.

[0109] While in Fig. 7 data 710 emitted from device 610 is via wireless transmission according to typical methods, in other embodiments, wired connections, such as via ethemet, may be used for data transmission to central monitoring device 710.

[0110] In Fig. 8. the sun 802 emits radiation collected by solar panel 810, which includes an instrumentation package 812 utilizing one or more cameras, sensors, and other devices that may emit data for transmission to a remote location for analysis to determine whether components are missing, damaged or otherwise require maintenance. In addition, if unauthorized personnel are detected, authorities or emergency services may be contacted and / or dispatched to the solar panel 810.

[0111] FIG. 9 shows a solar panel monitoring system 900 according to an embodiment of the present disclosure. The system 200 includes a central monitoring device 901 and a plurality of solar panels 810 in one or more fields. The number of panels 810 in the system 900 is not limited and may include one or a large number of panels. Instrumentation package 812 is mounted on or within one or more of the panels, and generates data 920 that may include without limitation operating and environmental conditions, equipment-related data, sensor data and measurements, maintenance information, visual data from camera(s), and the like. The central monitoring device 901 may be a data acquisition device such as a computer, a data storage device, or other analysis tool. In another embodiment, the central monitoring device 901 may be a communication device, tablet, or other computational device usable by personnel. In another embodiment the central monitoring device 901 is the power control for a solar panel farm or a utility operating the farm. The central monitoring device 901 may be autonomous or may be integrated within the solar panel farm control. The data 920 may be transmitted to and / or from the panel 810 in order to provide control or otherwise communicate with the panel 810 in response to acondition requiring maintenance in response to any received signals. In certain embodiments, equipment or other operational parameters may be transmitted and received.

[0112] In some embodiments, data 920 includes blockchain managed data in accordance with embodiments according to the present disclosure. In some embodiments, central monitoring device 901 places data 920 in a cloud 915 for access by others over a network.

[0113] While in Fig. 9 data 920 emitted from device 812 is via wireless transmission according to typical methods, in other embodiments, wired connections, such as via ethemet, may be used for data transmission to central monitonng device 901.

[0114] In Fig. 10, ocean 1050 has wave and tidal fluctuations that move one or more water-based power generation devices that include buoyant actuators 1010. whose overall system assemblies include an instrumentation package 1012 utilizing one or more cameras, sensors, and other devices that may emit data for transmission to a remote location for analysis to determine whether components are missing, damaged or otherwise require maintenance. In addition, if unauthorized personnel or testy sharks are detected, authorities or emergency services may be contacted and / or dispatched to the water-based power generation devices.

[0115] System 1000 according to an embodiment of the present disclosure includes a central monitoring device 1001 and a plurality of water-based power generation devices that include buoyant actuators 1010 in one or more fields in the sea. The number of water-based power generation devices in the system 1000 is not limited and may include one or a large number. Instrumentation package 1012 is located on or within the water-based power generation devices, and generates data 1020 that may include without limitation operating and environmental conditions, equipment-related data, sensor data and measurements, maintenance information, visual data from camera(s), and the like. The central monitoring device 1001 may be a data acquisition device such as a computer, a data storage device, or other analysis tool, either above or below the surface of the ocean 1050. In some embodiments, the central monitoring device 1001 may be on a vessel. In another embodiment, the central monitoring device 1001 may be a communication device, tablet, or other computational device usable by personnel. In another embodiment the central monitoring device 1001 is the power control facility on land for the utili ty operating the arrayof water-based power generation devices. The central monitoring device 1001 may be autonomous or may be integrated within the controls for the array. The data 1020 may be transmitted to and / or from the water-based power generation device(s) in order to provide control or otherwise communicate in response to a condition requiring maintenance in response to any received signals. In certain embodiments, equipment or other operational parameters may be transmitted and received.

[0116] In some embodiments, data 1020 includes blockchain managed data in accordance with embodiments according to the present disclosure. In some embodiments, central monitoring device 1001 places data 1020 in a cloud 1015 for access by others over a network.

[0117] While in Fig. 10 data 1020 emitted from device 1012 is via wireless transmission (e.g., using hydrophones and other data transmission and conversion techniques for sea-based communications transitioning through the water column through and past the surface for reception on or above the surface), in other embodiments, wired connections, such as via ethemet, may be used for data transmission to central monitoring device 1001 (e.g., when central monitoring device 1001 is on-shore and connected via cables).

[0118] While other power generation environments, including without limitation, remote geothermal generation locations, nuclear, and others are not depicted by figures, those with skill in the art will appreciate that the disclosed data transmission capabilities may also use the disclosed blockchain inventions. Similarly, infrastructure such as electrical transmission lines and grids that need various secure and traceable information transmission may also use the disclosed blockchain inventions.

[0119] Fig. 11 shows an example of a system 1700 that can be a well construction ecosystem, and which generates data that may be stored, managed, and curated in an Operations Platform, such as in Fig. 13A 1301C as discussed below (see also Figs. 13B and 13C). In some embodiments, data from system 1700 may be stored, managed, and curated in Operations Platform 1301C-1 that includes storage and services platforms for Wells Data 1301C-la as well. That is. the Operations Platform 1301C-1 and the Wells Data Services 1301C-la may perform analysis and generate commands to modify operations of various seismic data acquisition / analysis operations, hydrocarbon extraction operations, drilling operations, alternative energy' harvesting operations, and the like, as presented in Figs. 1-10.

[0120] As shown, the system 1700 can include one or more instances of a slips status engine (SSE) 1701 (see, e.g., the system 900 of Fig. 9, etc.) and can include a rig infrastructure 1710 and a drill plan component 1720 that can generation or otherwise transmit information associated with a plan to be executed utilizing the rig infrastructure 1710, for example, via a drilling operations layer 1740, which includes a wellsite component 1742 and an offsite component 1744. As shown, data acquired and / or generated by the drilling operations layer 1740 can be transmitted to a data archiving component 1750, which may be utilized, for example, for purposes of planning one or more operations (e g., per the drilling plan component 1720, Operations Platform 1301C-1, etc.).

[0121] As an example, a system may be a distributed environment, for example, a “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based sendee).

[0122] As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

[0123] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 12 illustrates an example of such a computing system 1200, in accordance with some embodiments. The computing system 1200 may include a computer or computer system 1201 A, which may be an individual computer system 1201 A or an arrangement of distributed computer systems (e.g., cloud computing). The computer system 1201A includes one or more analysis modules 1202 that are configured to perform varioustasks according to some embodiments, such as one or more methods disclosed herein. In some embodiments, analysis modules 1202 include machine learning, artificial intelligence, and / or neural net logic for supervised, unsupervised, and other forms of machine learning as those with skill in the art will appreciate.

[0124] To perform these various tasks, the analysis module 1202 executes independently, or in coordination with, one or more processors 1204, which is (or are) connected to one or more storage media 1206. The processor(s) 1204 is (or are) also connected to a network interface 1208 to allow the computer system 1201 A to communicate over a data network 1210 with one or more additional computer systems and / or computing systems, such as 1201B, 1201C. and / or 1201D (note that computer systems 1201B, 1201C and / or 1201D may or may not share the same architecture as computer system 1201A, and may be located in different physical locations, e.g., computer systems 1201 A and 1201B may be located in a processing facility, while in communication with one or more computer systems such as 1201C and / or 1201D that are located in one or more data centers, and / or located in varying countries on different continents). Those with skill in the art will appreciate that network 1210 may include cloud processing and / or storage infrastructure so that some or all of computing system 1200 may be cloud processing enabled to support Software-as-a-Service (SaaS) offerings.

[0125] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0126] The storage media 1206 may be implemented as one or more computer- readable or machine-readable storage media. Note that while in the example embodiment of Figure 12 storage media 1206 is depicted as within computer sy stem 1201 A, in some embodiments, storage media 1206 may be distributed within and / or across multiple internal and / or external enclosures of computing system 1201 A and / or additional computing systems. Storage media 1206 may include one or more different forms of memory7including semiconductor memory7devices 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, orother ty pes of optical storage, or other ty pes 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.

[0127] In some embodiments, computing system 1200 contains one or more analysis module(s) 1209. In the example of computing system 1200, computer system 1201 A includes the analysis module 1209. In some embodiments, an application-specific analysis module may be used to perform some aspects of one or more embodiments of the methods disclosed herein, e.g., as one non-limiting example, analysis module 1209 may be a subsurface seismic application-specific analysis module for processing seismic data for demultiples processing such as surface-related multiple removal. In other embodiments, a plurality7of analysis modules may be used to perform some aspects of methods herein.

[0128] It should be appreciated that computing system 1200 is merely one example of a computing system, and that computing system 1200 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 12, and / or computing system 1200 may have a different configuration or arrangement of the components depicted in Figure 12. The various components shown in Figure 12 may be implemented in hardware, software, or a combination of both hardw are and software, including one or more signal processing and / or application specific integrated circuits. One or more computer system(s) in Figure 12 may be implemented as virtual machines accessible via cloud 1210.

[0129] 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.

[0130] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative and / or recursive 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 1200, Figure 12), 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.Energy Data Platform

[0131] Turning to Figure 13 A. an example of a computing system-based Energy Data Platform 1300-1 for energy data storage, analysis, and processing is provided. User Computer System 1301A, which in some embodiments may be like the computing system 1200, or even a computer system 1201 A. is employed by an end user to gain access to one or more data type platforms, such as Subsurface, Operations, Sustainability’ and / or Enterprise Platforms (i.e., 1301B, 1301C, 1301D, and 1301E, respectively). These individual platforms will be discussed further below.

[0132] In some embodiments, a user employing User Computer System 1301A may gain access to other portions of the Energy' Data Platform 1300-1 via a network or cloud 1305, which may be similar to the network 1210 of Fig. 12.

[0133] Given the varying nature of data, data schemas, data types, access methods and requirements, security, and use cases across data Platforms 1301B - 1301E, Unification Services 1310 are provided to address these varying circumstances and disparities. In varying embodiments, Unification Services 1310 may include, without limitation, a unified applications programming interface (API) gateway, format conversion services, visualization tools, data relationship and lineage services, application relationship and lineage sen ices, among others.

[0134] Indeed, the Unification Services 1310 may collect data acquired from each of the data Platforms 1301B - 1301E (e.g.. via Applications and Management Services 1320) and modify the datasets such that they are interpretable by different platforms or systems. The different platforms or systems store data in varying formats, at different frequencies, for different changing volumes, and the like. To enable a user to consume information from another system, the requested information may be identified, copies of the information maybe generated, links (e.g., digital access locations) may be created, and the like. In addition, the duplicated information may need to be synchronized with the original datasets to keep the information in synchronization. Indeed, poorly maintained copies or redundant data sets erode trust in quality of data required for making certain decisions. With this in mind, Unification Services 1310 may address these concerns to allow users to consume information from multiple systems while maintaining the integrity of the respective datasets. As such, the Unification Services 1310 may convert the format in which datasets are stored in a database, package the datasets in a particular format for a different API suitable for different platforms than the originating platforms, and the like. In addition, the Unification Services 1310 may present the acquired datasets in a visualization tool that provides a user with the ability to view datasets from any of the platforms, individually or overlaid with others. The Unification Services 1310 may track or associate relationships between datasets based on time periods in which the datasets were acquired, relationships between equipment that acquired the datasets or in which the datasets measure respective performances, applications (e.g., operations, analysis) related to the datasets, and the like.

[0135] In order to provide access to data and application solutions for differing roles a user at User Computer System 1301 A may require, Applications and Management Services 1320 is also provided in Energy Data Platform 1300-1 and will be discussed further with respect to Figs. 15A and 15B.

[0136] Figure 13B presents an alternative implementation Energy Data Platform 1300-2, in which one or more of Platforms 1301B, 1301C, 1301D, and 1301E may be optional, (e.g., in some embodiments, Subsurface Platform 1301B, Operations Platform 1301C, and Sustainability Platform 130 ID may be implemented, but Enterprise Platform 1301E is not). Further, in some implementations, some or all of Unification Sendees 1310 may be implemented on User Computer System 1301A as a local copy Unification Services 1318. Likewise, in varying embodiments, some or all of Application and Management Services 1319 may be installed on User Computer System 1301 A, while other or additional applications and services may reside in Energy Data Platform 1300-2 at Application and Management Services 1320.

[0137] Figure 13C presents an alternative implementation Energy Data Platform 1300-3, in which User Computer System 1301 A accesses all of Energy Data Platform 1300-3 via network 1305, which may be a public cloud, a private cloud, a hybrid cloud, a cloud withdistributed computational resources, etc. In the specific example of Energy Data Platform 1300-3, User Computer System 1301A accesses one or more Virtual Machine(s) 1306 to gain access to the rest of Energy Data Platform 1300-3. Notably, Virtual Machine(s) 1306 may perform the computational tasks of gaining access to and processing data in Energy Data Platform 1300-3 in a scalable fashion so that User Computer Sy stem 1301A avoids performing complex and computationally-intensive functions, but instead, gains access to the complex, computationally intensive applications and services available from the full scope of Energy Data Platform 2400-3 via the various operations performed by various Virtual Machines 1306.

[0138] Figure 13D is an alternative implementation Energy Data Platform 1300-4, in which User Computer System 1301 A accesses some of Energy Data Platform 1300-3 via network 1305. In this embodiment, Subsurface Platform 1301B resides in an alternative network 1307 that is either on premises / co-located with User Computer System 1301A or within a separate cloud.

[0139] In some cases, sensitive data, such as hydrocarbon data, prospect data, production data, etc., may be stored in network 1307 on-site for a company due to regulatory considerations or legal requirements. For example, in an alternative embodiment not depicted. Subsurface Platform 1301B, Operations Platform 1301 C, and Sustainability Platform 1301D may be instantiated at a company’s location co-located, or on that company’s private network, along with User Computer System 1301A. And in that embodiment, Enterprise Platform 130 IE may be stored in a cloud 1305 managed by a third- party company.

[0140] In some embodiments where one or more platforms like Subsurface Platform 1301B are within the same network as User Computer 1301 A, instances of Application and Management Services 1320 and Unification Services 1310 may be run on a computing system, either within User Computer System 1301 A or on a virtual machine. This facilitates User Computer System 1301A gaining access to the one or more platforms, e.g., Subsurface Platform 1301B, that are within the same network. Such an arrangement can improve data security’ by keeping sensitive data within the same private network.

[0141] Indeed, by employing the embodiments described with respect to Fig. 13D, for example, the user may have access to various models produced and managed by variousentities or platforms. These models may include simulation models, reservoir models, geomechanics models, machine learning models, Al models, and the like. Indeed, by employing the unification services component 1310, the various models generated by or stored within any particular platform (e.g., 1301B-E) may be incorporated into or applied to other domains associated with different platforms. In this way, the operations employed by the user may be tightly coupled to the datasets that are stored in different platforms to enable the user to perform operations using datasets, models, and data objects from different domains in an efficient and useful manner.

[0142] In addition, by employing the embodiments described herein, the GenAI system may engage with different platforms (e.g.. 1301B-E), which may have different large language models (LLMs) that provides the user more flexibility' to provide more context to the GenAI system via a central platform system. As a result, the GenAI system may generate various GenAI user experiences for any particular domain or platform. For instance, a conversational chat interaction experience, a semantic search experience, a help guide experience, a workflow automation experience, and the like. In the case of semantic search, the GenAI system may decipher or understand user intent from an input prompt in a natural language manner to determine a manner in which to respond by using and combining information from structured queries and unstructured datasets, such as documents and images. The GenAI system may blend information from the various data stores accessible to different platforms (e g., 1301B-E) and form a coherent human like response. While determining generative responses, the GenAI system may also present information in visually interpretative aids such as maps, logging data visualizers, and seismic data visualizers. While presenting the data, the GenAI system may recommend to the user its interpretation of data quality, objects that can be matched to other system objects (entity matching), and help the user to orchestrate tasks that improves data quality.

[0143] Indeed, in some embodiments, in addition the various platforms described above, a GenAI platform may be part of the Energy Data Platform (e.g., 1300-1). That is, the GenAI platform may include software tools, software libraries, large language models, GenAi models, textual vector databases, and the like to enable the user to engage with generative Al functionalities, such as chat, help assistance, and the like. In some embodiments, the unification services 1310 may enable other platforms to access the GenAi platform to perform generative Al operations, while leveraging the information and context provided bythe respective platform. That is, the Operations Platform 1301C may provide additional datasets (e.g., structured / unstructured) that may be used by the GenAI platform to perform generative Al functions.

[0144] It should be noted that by using the unification services 1310, the present embodiments do not move datasets, models, or data objects across platforms. Indeed, the unification sendees provide conversion functions, API interface capabilities, and the like to allow the various platforms to use datasets, models, and data objects available on different platforms in a manner in which analysis and other functions can be applied to the datasets while tightly coupled to the respective platform. In this way, datasets, models, and other data objects are securely managed by the respective platform without distributing or physically moving the data or information outside the respective platforms.

[0145] For instance, the virtual machines 1330 implemented within the user computer system 1301 A may access datasets, models, data objects, and the like provided by the platforms 1301C-E, such that the virtual machines 1330 may use the datasets to generate Al models, employ GenAI operations, and the like for other platform services (e.g., subsurface platform 1304B) without physically copying or moving the datasets, models, or data objects from the source platforms. In this way, the data is securely managed, while providing the ability for the user computer system 1301 A to tightly couple to the information available on other platforms via the unification services 1310. By way of example, a user (e.g., a production engineer) may want to load entity-matched master data around wells and reservoirs from subsurface platform 1301B or the enterprise platform 1301E. The unification services 1310 may match these wells and reservoirs from the Subsurface Platform 1301B, connect to the Operations Platform 1301C, and ingest and continuously update the respective datasets in Operations Platform 1301C. As the Operations Platform 1301C collects high frequency production data, the unification services 1310 may also ingest contextualized production data back into Subsurface Platform 130 IB, and allow the user to use that for reservoir matching, decline curve analysis or production forecasting using ML or Al techniques, and the like. At all times, the unifications services 1310 may maintain direction of data flow, data frequency matching, and constant updates.

[0146] In some embodiments, copies of datasets may be generated to perform certain analysis or functions. In this case, the unification sendees may generate a blended dataset for the different platforms to use to collaborate for the purpose of any particular analysis orfunction. However, this blended dataset may be accessible only for the purposes of a particular analysis or function and may be deleted by writing over respective memory locations with pseudo random data values or other true deletion operations. In some embodiments, access to the datasets stored on a particular platform may be provided for a period of time for the respective operations or analysis to be completed. After the period of time expires, the respective platform may revoke credentials or disconnect the communicative link to prevent another platform or system from continuing to access the models, datasets, data objects, or the like.

[0147] Figure 14A illustrates a conceptual depiction of Subsurface Platform 1301B, which includes one or more subsurface databases 1301B-la for storing subsurface-related data, e.g., seismic and / or wellbore data. In some implementations, the databases 1301B-la may be implemented as an Open Subsurface Data Universe (OSDU)-compliant database; an example being Microsoft’s Energy Data Services (MEDS). Subsurface Platform 1301B also includes Subsurface Data Services 1301B-lb, which is varying embodiments includes data contextualization and curation capabilities to provide desired data for accomplishing particular tasks or services for users of the Energy Data Platform.

[0148] Among other types of data. Subsurface Platform 1301B may store one or more of the following, non-limiting examples: seismic measurements, seismic volumes, wellbore measurements, reservoir formation data, stratigraphy data, ID mechanical earth models, geomechanical data, ID measurements, 2D measurements, 3D measurements, time-lapse data, key attributes of formations such as permeability, porosity, resistivity etc., rock physics data, earth models, basin- wide models, fault and / or fracture models, and many more. Models, measurements, and interpretations may be in ID, 2D, 3D, and / or time lapse. In some examples, operational telemetry data may be stored to record subsurface operations, such as a wireline job.

[0149] Figures 14B and 14C illustrates a conceptual depiction of Operations Platform 1301C, which in the example of Operations Platform 1301C-1, includes storage and services platforms for each of Wells Data 1301C-la, Production Data 1301C-lb, and Asset and Equipment Data 130101c. In the example of Operations Platform 130102, one or more of the storage and services platforms may be optional, so that only a subset may be instantiated for a given Energy Data Platform implementation.

[0150] Wells data and services may include, without limitation through these examples, well construction schematics, drill path trajectories, well locations, drilling modeling information, drill string data, ROP data, telemetry, drilling and measurement measurements in ID, 2D, and 3D, digital twin models, rig information, drilling plans, formation data, drilling fluid data, Logging While Drilling / Measure While Drilling data, facility and field plans, formation evaluation intervention and stimulation data, plans and models, tool instrumentation data, environmental data, and / or integrated models.

[0151] The platform for Production Data and Services (i.e., 1301C-lb) may include, without limitation through these examples, production schematics, flow rates, digital twins for production environment, pipe and pipeline schematics, edge / Intemet of Things data for production, artificial lift, completions, valves, midstream, surface, downstream, subsea production data, plans, and models.

[0152] The platform for Asset and Equipment Data and Services (i.e., 1301C-lc) may include, without limitation through these examples, surface, midstream, and downstream schematics, model and digital twins, including with integration capabilities, equipment diagnostic data, maintenance and service history' data, manufacturing data, inventory data, shipping data, export control information, transportation and distribution data, among others.

[0153] In one implementation, Operations Platform 1301C may use an integrated, cross-discipline data platform that can manage data storage and sendees for field operations, manufacturing and maintenance, automation of process and equipment, all of which is intended to automatically populate data models for operations and planning, as w ell as transportation and distribution, refinery operations and support, pipeline operations, management, simulation, support, maintenance and the like, e.g., Cognite’s Data Fusion product can cross discipline and data type boundaries so as to store and manage many forms of data associated with field operations and its support.

[0154] Figure 14D illustrates a conceptual depiction of Sustainability7Platform 1301D. which in this example includes one or more sustainability databases and sustainability data services. In varying embodiments, the platform may include, without limitation through these examples, emissions data, carbon and greenhouse gas data and reduction plan data, w ater data, leakage data, product and service footprint data, renewable energy data (e.g.. wind, solar, geothermal, hydrogen, tidal, and other alternative energy data),etc. Fig. 17 provides one example with additional details that may be illustrative of the types of functionality that can be implemented in different Platforms in an Energy Data Platform.

[0155] Figure 14E illustrates a conceptual depiction of Enterprise Platform 1301E, which in this example includes one or more enterprise databases and related data services. In varying embodiments, the platform may include, human resources (HR), Finance, Legal, supply chain, inventory, etc. ERP systems’ content and other business data systems may be managed by Enterprise Platform 1301E and thus made accessible to the entire Energy Data Platform 1300.

[0156] Turning to Figs. 15A and 15B. Applications and Management and Services layer 1320 is shown in two alternatives, i.e., 1320-1, with 5 or more applications and management modules, and 1320-2, with at least one application and management module as needed given the overall Energy Data Platform configuration. Describing 1320-1 in further detail, layer 2420 includes various services which will be discussed below.

[0157] Discovery' Services may enable data discovery', workspaces, and automated data ingestion and curation capabilities, among other functionality'. As such, various user roles across Energy Data Platform 1300 are enabled to easily access and work with not only their own typical types of data, but also that from other disciplines that are stored in different Platforms, such as from both Wells Data and Production data.

[0158] External Data Sharing Services may enable a user of Energy Data Platform 1300 to access one or more external databases via network 1507-1 through 1507-n. For example, a government may' establish a database 1501 with exploration data, such as seismic, to allow' users of Energy Data Platform 1300 to review' the exploration data in a virtual room or other arrangement to decide on bidding (e.g., a geologist using subsurface data from governmental database 1507-1, such as the Egypt Upstream Gateway available from the Egyptian Ministry of Petroleum and SLB, and other sources of external data).

[0159] Analytics Services may provide data science w orkflows and machine learning capabilities to enhance rapid understanding of technical and / or business information. In addition, Analytics may generate models (e.g., machine learning models, Al models) based on the datasets collected across multiple platforms. In this way, the generated models may be managed by the user via the User Computer 1301 A and the like. That is, the generated models related to the different platforms (e.g.. 1301 A-D) may be accessible to users via theEnergy Data Platform 1300, such that the user may interact with the generated models to predict or plan subsurface analysis, production operations, sustainability operations, enterprise functions, GenAI functions, and the like.

[0160] Business Intelligence Services may take many forms and can include processing to manage data and produce visualizations that analyze underlying data, e.g. Microsoft PowerBI.

[0161] Applications, which may take many forms to select, curate, process, analyze, model, simulate, etc., to create solutions to business and technical problems, e.g., given subsurface, wells, and production data, simulating a reservoir to optimize or improve hydrocarbon production from a given reservoir with a drilling and stimulation campaign.

[0162] Generative Artificial Intelligence (GenAI), which may use the datasets (e.g., structured, unstructured) to enhance or update large language models that may be leveraged to generate responses to user inquiries and the like. By providing access to the datasets 1507- n from various platforms (e.g., 1301 A-D), the Gen Al system may provide more focused and relevant responses to user inquires in any particular domain or subject area related to the industries and applications described herein. GenAI may also include a set of platform services that may enable the user to generate GenAI based experiences such as chatbot, assistance interfaces for performing certain operations in applications, semantic and conversational components to enable the GenAI to provide discernable outputs, and the like. In the case of semantic search, the GenAI system may understand user intent in a natural language manner and determine a manner in which to respond by using and combining information from structured queries and unstructured data sets such as documents and images. The GenAI system may blend information from the various data stores in the overall platform and form a coherent human like response. The GenAI system, while determining generative responses, may also present information in visually interpretative aids such as maps, logging data visualizersm, and seismic data visualizers. While presenting the data, the GenAI system may recommend to the user its interpretation of data quality, objects that can be matched to other system objects (entity matching), and help the user to orchestrate tasks that improves data quality.Energy Data Platform Users

[0163] Given the broad scope of data and intelligence spanning an instance of Energy Data Platform 2400, many different types of users can benefit from the breadth of data and services it enables and provides. Without limitation, the following are some examples of job roles that may use one or more aspects of Energy Data Platform 1300:Geologist Production Engineer Artificial Lift EngineerDrilling Engineer Well Services Engineer Well Cementing EngineerChemist Mud Logging Specialist GeophysicistPetrophysicist Subsea Engineer Testing EngineerWell Placement Engineer Field Development Planning Well Planning EngineerEngineerRock Analysis Engineer Fluid Analysis Engineer Control and AutomationEngineerPetroleum / Reservoir Geoscientist Civil EngineerEngineerStructural Engineer Commercialization Materials EngineerPersonnelQuality and Reliability Repair Personnel Inventory PersonnelPersonnelCustomer Service Personnel Quality Assurance Personnel Electrical EngineerData Scientist Process Engineer loT or IIoT EngineerEnvironmental Specialist Health Safety Environment Construction Designer (HSE) SpecialistFacility7Specialist Import-Export Specialist Trade Control CompliancePersonnelSupplier Chain Personnel Logistics Personnel Regulatory CompliancePersonnelHR Personnel Finance Personnel Accounting PersonnelController Internal Audit Personnel Tax PersonnelIT Personnel Software Engineer Technical Marketing PersonnelSales Personnel Marketing Personnel Communications PersonnelIntelligence Personnel Management Personnel Legal Personnel

[0164] Atention is now directed to Fig. 16, which discloses a method 1600 of using an Energy Data Platform according to some embodiments. In varying implementations, method 1600 may be performed on a computing system-based Energy Data Platform for energy data storage, analysis, and processing, such as the foregoing example Energy Data Platforms 1300-1, 1300-2, 1300-3 or 1300-4 disclosed in Figs. 13A - 13D, respectively. Although the following description of the method 1600 will be described as being performed by the user computer system 1301 A in a particular order, it should be understood that the method 1600 may be performed by any suitable computing system in any suitable order.

[0165] Referring now to the method 1600. at block 1602, the user computer system 1301 A may retrieve a first model, one or more datasets, or one or more data objects from a first platform in the Energy Data Platform (see, e.g., Fig. 13 A, a user at user computer system 1301A accessing any platform in the Energy' Data Platform, such as the example of Subsurface Platform 1301B).

[0166] In some embodiments, the models discussed herein may be related to domain specific simulation models (e.g., drilling model, production simulation model), generative Al models, Al models, machine learning models, and the like. The generative Al models may include Al models used to generate new content such as text, images, music, code, and the like. Example generative Al models may include Generative Pre-trained Transfer (GPT) for text generation, DALL-E for image generation, MusicLM for music composition, and the like.

[0167] Al models may refer to any model that uses artificial intelligence or machine learning to perform a task. The Al models may be used for applications such as classification, regression, decision-making, natural language processing, computer vision, robotics, automating workflows, and the like.

[0168] At block 1604, the user computer system 1301A may retrieve a second model, other datasets, or other data objects from a second platform in the Energy Data Platform (see e.g., Fig. 13A, a user at user computer system 1301 A accessing any platform in the Energy- Data Platform, such as the example of Operations Platform 1301C). In general, the second model, other datasets, or other data objects may be automatically, or semi-automatically withuser input, be identified based at least in part on a correspondence between both models or data objects via the Unification Services 1310 or the like.

[0169] In some implementations, the user computer system 1301 A may analyze various aspects of the retrieved models, datasets, or data objects to better ascertain differences between the two. As such, at block 1606, the user computer system 1301A may determine one or more correspondences or associations between models, datasets, or data objects through common lineages, relationships, user histories of access to various platforms, references to what object(s), data, or areas of interest the models or data objects represent (see e.g., Fig. 14A referring to Applications and Management Services layer 1320 and Fig. 15A 1320-1, applications, business intelligence. GenAI, and / or discovery services within the Applications and Management Services layer).

[0170] In addition, the user computer system 1301A may. at block 1607, determine (e.g.. via unification layer) the correspondence and / or associations between models or data objects through job roles associated with users interacting with the Energy Data Platform (e.g., a drilling planner has a job role related to wells and drilling, and intends to work on her drilling plan regarding a specific area of interest; thus, the program to identify one or more models for the drilling planner will take into account her job role in addition to other factors as discussed above, in order to identify a model). In further implementations, the correspondence between models or data objects may be determined in a data modeling process relying on one or more of conceptual data models, domain models, logical data models, GenAI models, Al models, machine learning models, and physical data models; for some implementations, data modeling processes to determine a correspondence between models or data objects can include:• analysis or identification of entities, things, events, concepts, locations, data types, etc. in the datasets under analysis so there are logically discrete categories;• identification of key attributes in the logically discrete categories;• identification of relationships between attributes in the logically discrete categories; sometimes modeling languages may be employed in this task;• map the key attributes to the logically discrete categories; and• assign and normalize key attributes to balance storage versus performance for queries and exploration of data sets to find desired model or data object correspondences.

[0171] In some instances, at block 1608, the user computer system 1301A may determine whether the information received from the two platforms correspond to the same platform (i.e., the two model or data object retrievals are from the same platform, such as the example of Subsurface Platform 2401B). In the same manner, at block 1610, the user computer system 1301 A may determine whether the information received from the platforms correspond to different platforms are different (i.e., as user or multiple users may retrieve models and data objects from different platforms, such as the example of a 3D earth model retrieved as the model or data object from the first platform, e.g., Subsurface Platform 2401B, and a drilling plan as the second model or data object retrieved from the second platform, e.g., Operations Platform 2401C). Further, at block 1612, the user computer system 1301A may analyze the collected datasets and determine whether the sets of collected data correspond to different data types.

[0172] After determining the differences between the two models, datasets, or data objects, the user computer system 1301A may continue method 1600 at block 1614 by modifying or adjusting the first model, the datasets, or the data object based on the contents of the second model or data object. In some embodiments, the user computer system 1301 A may, at block 1616, store the modified or adjusted model, dataset, or data object in the first platform, depending on the user's or users’ needs. In some cases, at block 1618, the user computer system 1301A may modify or adjust the second model or data object based on the contents of the first model, datasets, or data objects, or the modified or adjusted model or data object; and similarly, depending on the user’s or users’ needs. At block 1620, the user computer system 1301 A may store the modified or adjusted second model or data object in the second platform.

[0173] At block 1622, the user computer system 1301 A may generate commands for controlling equipment depicted in Figs. 1-10 based on the adjusted model, datasets, data objects, or the like (e.g., adjusted first model, adjusted second model). That is. the adjusted data may provide insight into various workflows, equipment in various systems (e.g.. Figs. 1- 10), and the like. For instance, the adjusted data may indicate that equipment is operating out of specification, due for a repair, in need of maintenance, operating under an alarm condition, or the like. As such, the generated command may provide alterations to the operations of the respective equipment, devices, or the like of the system to improve the overall operation of the system. By way of example, the datasets or models related to drilling obtained from theoperations platform 1301C may be updated or adjusted based on the datasets or models from the subsurface platform 1301B. That is. the drilling operations may be updated based on interpretations performed using the subsurface platform 1301B that provides insight into the various rock formations of a subsurface area in which the drilling operations may be performed. In this example, the trajectory of the drilling, the speed in which the drill operates, the manner in which a borehole is fractured, and other operations may be updated based on the analysis performed using the subsurface platform 1301B.

[0174] At block 1624, the user computer system 1301A may send the generated commands to the respective devices. In some embodiments, the commands may be sent to a particular platform for implementation. That is. the commands may be distributed to the respective platform that may' perform the respective operations. As such, the respective platform may distribute the commands to respective control systems for devices or systems, actuator devices, or other control devices that implement the commands. The commands may include adjusting operations (e.g., speed, torque, direction) of equipment, modifying set points (e.g., alarm points, operational speed target), and the like. Indeed, it should be noted that the commands may be sent to any' suitable device for improving a respective operation of a respective system including those systems listed above in Figs. 1-11.

[0175] It will be apparent that users from different disciplines, e.g. geophysicists and drilling engineers, can readily use the Energy Data Platform to collaborate with one another. An additional advantage is that through applications available within the Energy7Data Platform, model and data object curation services can be implemented in order to facilitate appropriate model and data object identification based on the job role and job tasks user(s) are undertaking.

[0176] In determining appropriate model and / or data object identification, techniques used by those with skill in the art may be employed. For example, data quality dimensions may be used for curation purposes, including without limitation, for example, completeness, uniqueness, timeliness, validity', accuracy, and consistency, among potentially others. Completeness may be measured as the portion of stored or received data over the expected size of the complete dataset. Thus, missing channels or missing contextual information about the data objects or models may represent incomplete or misidentified data to obtain for a given workflow for a given person performing a given job function within a given discipline, e.g., a reduction in the completeness dimension of data quality7.

[0177] Data uniqueness may be based on the notion that data is not required more than once, and may be measured or identified as the percentage of unique data that is stored or received against the total amount of data that is stored or received. For example, measurements received from multiple sources may provide different information, or redundant information. To avoid giving one data set too much weight during curation and / or identification, such redundant data might, for example, be given a lower weight or ignored.

[0178] The timeliness data quality dimension measures the degree to which data represents a given point in time. This is measured as the percentage of on time data stored or received against the total amount of data that is stored or received. Data arriving too late or out of order may reduce the timeliness data dimension.

[0179] The validity' data quality dimension is based on whether the data conforms to an expected syntax, e.g., format, type, range, as prescribed in its definition. The measurement is the percentage of valid data stored or received against the total amount of data stored or received. Examples of invalid data include data that is out of range or configured with the wrong (i.e., not prescribed or otherwise unexpected) units.

[0180] The accuracy dimension expresses the degree to which the data correctly described the object or event. The measurement may be the percentage of accurate data stored or received against the total stored or received. Data that might be excluded from accurate data, thereby lowering the accuracy dimension value, might include data signals with high standard deviations or incoherent spikes.

[0181] The consistency dimension expresses the absence of difference, when comparing two or more representations of a thing against a definition. This may be quantified as the percentage of consistent data stored or received against the total amount of data stored or received. For example, a pump speed (e.g.. strokes per minute or SPM) dropping to zero when standpipe pressure remains constant reflects inconsistent data. As another example, a bottom-hole assembly showing no measuring-while-drilling tool when the channel from this tool is being received may be indicative of an inconsistent data.

[0182] Any one or more of the foregoing data quality dimensions may be taken into consideration to evaluate during curation and / or identification of models and / or data objects to use for a given person at a given time for a given task when using an Energy Data Platform.

[0183] While not discussed explicitly with respect to method 1600, some implementations include a unification services layer, such as that depicted in Fig. 13A 1310. which can address and resolve many issues related to data format and schema differences, measurement differences, vary ing international standards across countries, and many more issues. Also while not discussed explicitly with respect to the foregoing method, those with skill in the art will understand that, as depicted throughout Figs. 13 A - 13D, many network and / or cloud architectures may be used in different implementations of the Energy Data Platform used to perform the example of method 1300.

[0184] As shown in Figs. 13A - 14, some implementations include a sustainability platform. Sustainability platform 1700 depicts one example sustainability platform that may be used in the Energy Data Platform examples of Figs. 13A - 15, Sustainability Platform 1301D. In the example here in Fig. 17, sustainability7platform 1700 includes an overall system with digital foundation sendees and digital infrastructure. Sustainability' platform 1700 may perform various analysis operations to determine action plans using measure, report, and verify operational workflows. In addition, sustainability platform 1700 may provide abatement planning and modeling workflows, as well as abatement operations. To facilitate each of these operations, in some implementations, sustainability7platform 1700 may include digital foundation services and data infrastructure that may allow sustainability platform 1700 to discover providers, workflow systems, and other components that may be available for sustainability platform 1700 to use to generate consumables to improve enterprise operations sustainability. In addition, digital foundation services may provide data ingestion and transformation operations to process and transform datasets into processable data. In this way, sustainability platform 1700 may provide domain specific aggregation of datasets for processing or use by other workflows or analysis. In addition, digital foundation services may include computational engines that may perform various types of analysis, generate reports, simulate projections, and the like. The data infrastructure may enable users to analyze the data using data analysis tools. That is, the data analysis tools may be provided via sustainability platform 1700 to access the data and use data analysis tools.

[0185] By facilitating the interactions, integration, and exchange of data betw een data providers and consumers, sustainability platform 1700 may provide enhanced capabilities for processing data, generating action plans, and the like. Sustainability platform 1700 also allows for sustainability data discovery / measurement operations, dataingestion / transformation operations, data aggregation / integration operations, and footprint calculations / computation operations to be performed via the platform, and for use by and in an Energy Data Platform as described above.

[0186] In addition, sustainability platform 1700 may provide sustainability reports, dashboards, and comparisons. Data Science and self-serve analytics may be accessed via sustainability platform 1700, or in some embodiments, via applications in an application and management layer 1320 such as in Fig. 13B. Moreover, sustainability / decarbonization planning (e.g., via domain workflows plug-ins) may be performed. In the same manner, sustainability platform 1700 may enable scenarios / optimization / automation / orchestration tools to be implemented based on physics / AI / ML / hybrid modeling / simulation of key processes.

[0187] Sustainability platform 1700 may develop and execute action plans (e.g., implemented as commands via method 1600) with the ability to update those action plans during execution, including on an iterative basis. As such, sustainability platform 1700 may facilitate execution monitoring / assessment / re-planning operations. In addition, sustainability platform 1700 may monitor and track decarbonization Lifecycle Management (e.g., via digital twin), while providing sustainability provider marketplace for additional services (e.g., data, science / algorithms / PCFs, Offsets, etc.)

[0188] In one embodiment, an Energy Data Platform is provided that includes one or more computing systems that include a plurality of data type platforms, such as a Subsurface Platform, an Operations Platform, a Sustainability Platform, and an Enterprise Platform, an application management layer, a unification services layer, and a first network for accessing the Energy7Data Platform.

[0189] In some examples, the plurality of data type platforms, the application management layer, and the unification services layer are accessible on the first network.

[0190] In some examples, the one or more computing systems includes a user computer system, and further comprising, in response to a user command, the user computer system accesses other parts of the Energy Data Platform.

[0191] In some examples, the Energy Data Platform includes a user computer system, and wherein the plurality of data type platforms, the application management layer, and theunification sendees layer are in a cloud accessible to the user computer system via the first network.

[0192] In some examples, the Operations Platform includes one or more of a Wells data platform, a Production Data platform, and an Asset and Equipment Data platform.

[0193] In some examples, the user computer system includes a unification services layer.

[0194] In some examples, the user computer system includes an application management layer.

[0195] In some examples, the Energy Data Platform includes one or more virtual machines (2406) disposed in the cloud, and in response to a user command, the user computer system accesses other parts of the Energy Data Platform through the one or more virtual machines.

[0196] In some examples, at least one data type platform of the plurality of data type platforms is disposed in a second network.

[0197] In some examples, the second network is a second cloud.

[0198] In some examples, the user computer system is configured to access at least one data type platform of the plurality of data ty pe platforms from the first network, and access at least one data type platform of the plurality of data type platforms from the second network.

[0199] In some examples, the user computer system includes one or more virtual machines.

[0200] In some examples, a virtual machine of the one or more virtual machines is configured to access both the first cloud and the second cloud.

[0201] In some examples, the Energy Data Platform also provides a first user employing the user computer system access to other parts of the Energy Data Platform.

[0202] In some examples, in the first user has a job role selected from the group consisting of Geologist, Drilling Engineer, Chemist, Petrophysicist, Well PlacementEngineer, Rock Analysis Engineer, Petroleum / Reservoir Engineer, Structural Engineer, Quality and Reliability Personnel, Customer Service Personnel. Data Scientist. Environmental Specialist, Facility Specialist, Supplier Chain Personnel, HR Personnel, Controller, IT Personnel, Sales Personnel, Intelligence Personnel, Production Engineer, Well Services Engineer, Mud Logging Specialist, Subsea Engineer, Field Development Planning Engineer, Fluid Analysis Engineer, Geoscientist, Commercialization Personnel, Repair Personnel, Quality Assurance Personnel, Process Engineer. HSE Specialist, Import-Export Specialist, Logistics Personnel, Finance Personnel, Internal Audit Personnel, Software Engineer, Marketing Personnel, Management Personnel, Artificial Lift Engineer, Well Cementing Engineer, Geophysicist, Testing Engineer. Well Planning Engineer, Control and Automation Engineer, Civil Engineer. Materials Engineer, Inventory Personnel, Electrical Engineer, loT Engineer, IIoT Engineer, Construction Designer, Trade Control Compliance Personnel, Regulatory Compliance Personnel, Accounting Personnel, Tax Personnel, Technical Marketing Personnel, Communications Personnel, and Legal Personnel.

[0203] In some examples, the Energy Data Platform enables execution of one or more methods, using one or more computing systems, to implement an Energy Data Platform as described in any one of claims 1 through 15.

[0204] In some examples, one or more computer programs implement a unification services layer in an Energy Data Platform.

[0205] In some examples, one or more computer programs implement an applications and management services layer in an Energy Data Platform.

[0206] In some examples, one or more computer programs implement subsurface data services in a Subsurface Platform.

[0207] In some examples, one or more computer programs implement operations data services in an Operations Platform.

[0208] In some examples, one or more computer programs implement sustainability data services in a Sustainability Platform.

[0209] In some examples, one or more computer programs implement enterprise data services in an Enterprise Platform.

[0210] In some examples, one or more computer programs implement wells data services in a Wells Data Platform.

[0211] In some examples, one or more computer programs implement production data services in a Production Data Platform.

[0212] In some examples, one or more computer programs implement asset and equipment data services in an Asset and Equipment Data Platform.

[0213] In accordance with an embodiment, a method for a geophysicist is provided to use an Energy Data Platform to refine a subsurface earth model, wherein the method comprises: at a user computer system: starting an earth model building application from an application suite disposed in an application and management services layer; using the earth model building application to retrieve the earth model from a subsurface platform; performing one or more fault location adjustments on the earth model using the earth model building application, wherein the geophysicist invokes an analytics program disposed in the application and management services layer to perform machine learning on the earth model to improve the one or more fault locations; using a cross-domain application from the application suite to retrieve a 3D drilling model from a wells data platform disposed in an operations platform, wherein the cross-domain application uses a data curation and translation application program interface disposed in an unification services layer; updating one or more attributes of the earth model based at least in part on the 3D drilling model; and storing the updated earth model in the subsurface platform.

[0214] In some examples, the foregoing method also includes at a second user computer system, a drilling engineer adjusting a drilling plan by: starting a drill planning application from the application suite disposed in the application and management services layer; retrieving the drilling plan from the wells data platform; retrieving the updated earth model from the subsurface platform by using a second cross-domain application from the application suite, wherein the second cross-domain application uses a second data curation and translation application program interface disposed in the unification services layer; updating the drilling plan based at least in part on the updated earth model; and storing the updated drilling plan in the wells data platform.

[0215] In some examples, the cross-domain application identifies the 3D drilling model to retrieve from the wells data platform at least in part by determining the correspondence between the earth model and the 3D drilling model.

[0216] In some examples, the second cross-domain application identifies the earth model to retrieve from the subsurface platform at least in part by determining the correspondence between the 3D drilling model and the earth model.

[0217] In some examples, the cross-domain application identifies the 3D drilling model to retrieve from the wells data platform at least in part by the relationship or lineage or both of the earth model and the 3D drilling model.

[0218] In some examples, the user computer system, the second user computer system, or both, are virtual machines instantiated in the Energy Data Platform.

[0219] In some examples, the subsurface platform and the wells data platform are stored in a first cloud and a second cloud, respectively.

[0220] In accordance with an embodiment, a method for using an Energy Data Platform that stores a plurality of datatypes is provided, comprising: at one or more user computer systems: in response to receiving a first user command, retrieving a first model from a first platform selected from the group consisting of a Subsurface Platform, an Operations Platform, a Sustainability Platform, and an Enterprise Platform, in response to receiving a second user command, retrieving a second model from a second platform selected from the group consisting of the Subsurface Platform, the Operations Platform, the Sustainability Platform, and the Enterprise Platform, wherein a model curation application automatically selects the second model to retrieve based at least in part on determining at least one correspondence between the first and second models.

[0221] In some examples, the at least one correspondence is the relationship or lineage, or both, of the first and second models.

[0222] In some examples, the first and second platforms are the same.

[0223] In some examples, the first and second models represent different data types.

[0224] In some examples, the first user issued both the first and second user commands.

[0225] In some examples, the first and second platforms are different.

[0226] In some examples, the first and second models represent different data types modeling the same object.

[0227] In some examples, a first user issued the first user command and a second user issued the second user command.

[0228] In some examples, the model curation application is disposed in an application suite disposed in an application and management services layer.

[0229] In some examples, the method also includes adjusting at least one of the first and second models with a conversion utility disposed in a unification services layer.

[0230] In some examples, the first and second platforms are in one or more cloud computing environments.

[0231] In some examples, the method also includes the first user moditying at least one of the first and second models based at least in part on the contents of the second model; and storing the modified model in its respective platform.

[0232] In some examples, the method also includes the second user modity ing at least one of the first and second models based at least in part on the contents of the first model; and storing the modified model in its respective platform.

[0233] In some examples, the first and second users, respectively , have one or more job roles selected from the group consisting of Geologist, Drilling Engineer, Chemist, Petrophysicist. Well Placement Engineer, Rock Analysis Engineer. Petroleum / Reservoir Engineer, Structural Engineer, Quality’ and Reliability’ Personnel, Customer Service Personnel, Data Scientist, Environmental Specialist, Facility Specialist, Supplier Chain Personnel, HR Personnel, Controller, IT Personnel, Sales Personnel, Intelligence Personnel, Production Engineer, Well Services Engineer. Mud Logging Specialist, Subsea Engineer, Field Development Planning Engineer, Fluid Analysis Engineer, Geoscientist, Commercialization Personnel, Repair Personnel, Quality Assurance Personnel, ProcessEngineer, HSE Specialist, Import-Export Specialist, Logistics Personnel, Finance Personnel, Internal Audit Personnel, Software Engineer, Marketing Personnel. Management Personnel, Artificial Lift Engineer, Well Cementing Engineer, Geophysicist, Testing Engineer, Well Planning Engineer, Control and Automation Engineer, Civil Engineer, Materials Engineer, Inventory Personnel, Electrical Engineer, loT Engineer, IIoT Engineer, Construction Designer, Trade Control Compliance Personnel. Regulatory Compliance Personnel, Accounting Personnel. Tax Personnel. Technical Marketing Personnel. Communications Personnel, and Legal Personnel.

[0234] In some examples, the model curation application automatically selects the second model to retrieve based at least in part on a job role of the second user.

[0235] In accordance with an embodiment, a method performed by an Energy7Data Platform to provide access to a plurality of energy data objects is presented, comprising: at a server computing system disposed in the Energy7Data Platform: receiving a first user command for retrieving a first model from a first platform selected from the group consisting of a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), and an Enterprise Platform (1301E); sending the first model in response to the first user command: receiving a second user command for retrieving an unidentified second model from a second platform selected from the group consisting of the Subsurface Platform, the Operations Platform, the Sustainability Platform, and the Enterprise Platform, wherein a model curation application identifies the second model to retrieve based at least in part on determining at least one correspondence between the first model and the unidentified second model; sending the identified second model in response to the second user command.

[0236] In accordance with an embodiment, a method for an Energy7Worker to use an Energy Data Platform to utilize a plurality of energy data objects is provided, comprising: at a user computer system: starting a first energy data object working application from an application suite disposed in an application and management services layer; using the first energy7data object working application to retrieve a first energy7data object from a first energy data management and storage platform; performing one or more adjustments on the first energy data object using the first energy7data object yvorking application; using a crossdomain application from the application suite to retrieve a second energy data object disposed in a second energy7data management and storage platform, wherein the cross-domain application uses a data curation and translation application program interface disposed in anunification sen-ices layer; updating the first energy- data object based at least in part on the second energy data object; and storing the updated first energy data object in the first energy data management and storage platform.

[0237] In some examples, also includes a second Energy Worker: utilizing the Energy Data Platform to retrieve the second energy data object and the updated first energy data object that is automatically identified by the Energy Data Platform for review by the second Energy Worker; updating the second energy data object based at least in part on the updated first energy data object; and storing the updated second energy- data object in the second energy data management and storage platform.

[0238] In some examples, the Energy Worker has one or more job roles selected from the group consisting of Geologist, Drilling Engineer, Chemist, Petrophysicist, Well Placement Engineer, Rock Analysis Engineer. Petroleum / Reservoir Engineer, Structural Engineer, Quality and Reliability Personnel. Customer Service Personnel, Data Scientist, Environmental Specialist, Facility Specialist, Supplier Chain Personnel, HR Personnel, Controller, IT Personnel, Sales Personnel, Intelligence Personnel, Production Engineer, Well Services Engineer, Mud Logging Specialist, Subsea Engineer, Field Development Planning Engineer, Fluid Analysis Engineer, Geoscientist, Commercialization Personnel, Repair Personnel, Quality Assurance Personnel, Process Engineer, HSE Specialist, Import-Export Specialist, Logistics Personnel, Finance Personnel, Internal Audit Personnel, Software Engineer, Marketing Personnel, Management Personnel, Artificial Lift Engineer, Well Cementing Engineer, Geophysicist, Testing Engineer. Well Planning Engineer, Control and Automation Engineer, Civil Engineer. Materials Engineer, Inventory Personnel, Electrical Engineer, loT Engineer, IIoT Engineer, Construction Designer, Trade Control Compliance Personnel, Regulatory7Compliance Personnel, Accounting Personnel, Tax Personnel, Technical Marketing Personnel, Communications Personnel, and Legal Personnel.

[0239] In accordance with an embodiment, a method is performed by an Energy Data Platform to provide access to a plurality- of energy- data objects, comprising: at a sen- er computing system disposed in the Energy Data Platform: receiving an instruction from a first user computer system to initiate a first energy data object working application from an application suite disposed in an application and management services layer in the Energy- Data Platform; executing the first energy data object working application to identify and retrieve a first energy- data object from a first energy- data management and storage platform;sending a copy of the first energy' data object to the first user computer system; receiving a second instruction from the first user computer system to retrieve an unidentified second energy data object disposed in a second energy data management and storage platform, wherein the second instruction initiates a cross-domain application from the application suite that, uses a data curation and translation application program interface disposed in an unification serv ices layer in the Energy Data Platform, and automatically identifies the second energy data object; sending a copy of the second energy data object to the first user computer system; receiving from the first user computer system an updated first energy data object that includes changes based at least in part on the second energy' data object; and storing the updated first energy' data object in the first energy data management and storage platform.

[0240] In some examples, the method includes: receiving a third instruction from a second user computer system to retrieve the second energy data object; based at least in part on receiving the third instruction, sending to the second user computer system a message to suggest loading the updated first energy data object; receiving a fourth instruction from the second user computer system to retrieve the updated first energy data object; sending the updated first energy data object to the second user computer system; receiving an updated second energy data object; and storing the updated second energy data object in the second energy data management and storage platform.

[0241] Those with skill in the art will appreciate that the workflows and example implementations described herein, including method 1600 may be practiced in many environments, including without limitation oil & gas applications in which the described infrastructure may be deployed include wireline operations, drilling and well construction operations, and production facility' and testing operations as w ell as other energy generation, capture, and transmission environments in which the described blockchain infrastructure may be deployed include solar power installations, nuclear power plants, electrical transmission lines and grids, hydroelectric power plants and infrastructure, tidal, current, and wave energy' installations, geothermal power sites, wind energy' sites, and other pow er generation facilities along with their grids, instrumentation, transmission lines, and sensors that have data emitting capabilities where the data emitted may be collected and managed.

[0242] Moreover, method 1600 is not explicitly depicted as including various computer-readable storage medium (CRM) blocks, but each of the noted operations inmethod 1600 can include processor-executable instructions that can instruct a computing system, which can be a control system, to perform one or more of the actions described with respect to their respective blocks (i.e., 1602, 1604, 1606, 1607, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, and 1624 may be implemented with one or more CRM blocks).

[0243] Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. § 112, paragraph 6 or any laws or rules promulgated to address or interpret functional claiming techniques for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘’means for"’ together with an associated function.

Claims

CLAIMSWhat is claimed is:

1. An Energy Data Platform (2400-1), comprising: one or more computing systems, including: a plurality of data type platforms comprising a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (GenAI) platform; an application management layer (1320) configured to receive a plurality of datasets from the plurality7of data type platforms; a unification services layer (1310) configured to convert at least a portion the plurality of datasets acquired from a first data type platform of the plurality of data type platforms into a plurality of converted datasets, wherein the plurality of converted datasets is accessible to a second data type platform of the plurality of data type platforms; and a first network (1305) for accessing the Energy Data Platform.

2. The Energy Data Platform of claim 1, wherein the plurality of data type platforms, the application management layer, and the unification services layer are accessible on the first network.

3. The Energy Data Platform of claim 2, wherein the one or more computing systems comprises a user computer system (1301A) configured to access other parts of the Energy Data Platform in response to a user command.

4. The Energy Data Platform of claim 3, wherein the user computer system comprises a unification services layer.

5. The Energy' Data Platform of claim 4, further comprising one or more virtual machines (1306) disposed in the cloud, and in response to a user command, the user computer system accesses other parts of the Energy Data Platform through the one or more virtual machines.

6. The Energy Data Platform of claim 5, wherein at least one data type platform of the plurality7of data type platforms is disposed in a second network (2407).

7. The Energy Data Platform of claim 6, wherein the second network is a second cloud.

8. The Energy Data Platform of claim 6, wherein the user computer system is configured to: access at least one data type platform of the plurality7of data type platforms from the first network, and access at least one data type platform of the plurality of data type platforms from the second network.

9. The Energy Data Platform of claim 8, wherein the user computer system comprises one or more virtual machines (2430).

10. The Energy Data Platform of claim 3, wherein the user computer system comprises an application management layer.

11. The Energy Data Platform of claim 3, wherein the user computer system configured to access other parts of the Energy Data Platform.

12. The Energy' Data Platform of claim 1, further comprising a user computer system, and wherein the plurality' of data ty pe platforms, the application management layer, and the unification services layer are in a cloud accessible to the user computer system via the first network.

13. The Energy Data Platform of claim 1, wherein the Operations Platform comprises a Wells data platform (1301C-la), a Production Data platform (1301C-lb), an Asset and Equipment Data platform (1301C-lc), or any combination thereof.

14. A method, using one or more computing systems to implement an Energy' Data Platform as described in any7one of claims 1 through 15.

15. A method for a geophysicist to use an Energy Data Platform to refine a subsurface earth model, comprising: at a user computer system: starting an earth model building application from an application suite disposed in an application and management sendees layer; using the earth model building application to retrieve the earth model from a subsurface platform; performing one or more fault location adjustments on the earth model using the earth model building application,wherein the geophysicist invokes an analytics program disposed in the application and management services layer to perform machine learning on the earth model to improve the one or more fault locations; using a cross-domain application from the application suite to retrieve a 3D drilling model from a wells data platform disposed in an operations platform, wherein the cross-domain application uses a data curation and translation application program interface disposed in a unification services layer; updating one or more attributes of the earth model based at least in part on the 3D drilling model; and storing the updated earth model in the subsurface platform.

16. The method of claim 15, further comprising: at a second user computer system, a drilling engineer adjusting a drilling plan by: starting a drill planning application from the application suite disposed in the application and management services layer; retrieving the drilling plan from the wells data platform; retrieving the updated earth model from the subsurface platform by using a second cross-domain application from the application suite, wherein the second cross-domain application uses a second data curation and translation application program interface disposed in the unification services layer; updating the drilling plan based at least in part on the updated earth model; and storing the updated drilling plan in the wells data platform.

17. The method of claim 16, wherein the user computer system, the second user computer system, or both, are virtual machines instantiated in the Energy Data Platform.

18. The method of claim 15, wherein the cross-domain application identifies the 3D drilling model to retrieve from the wells data platform at least in part by determining the correspondence between the earth model and the 3D drilling model.

19. The method of claim 15, wherein the second cross-domain application identifies the earth model to retrieve from the subsurface platform at least in part by determining the correspondence between the 3D drilling model and the earth model.

20. The method of claim 15, wherein the cross-domain application identifies the 3D drilling model to retrieve from the wells data platform at least in part by the relationship or lineage or both of the earth model and the 3D drilling model.

21. The method of claim 15. wherein the subsurface platform and the wells data platform are stored in a first cloud and a second cloud, respectively.

22. A method of using an Energy Data Platform that stores a plurality of data types, comprising: at one or more user computer systems (1301A): in response to receiving a first user command, retrieving a first model from a first platform selected from the group consisting of a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (Al) Platform;in response to receiving a second user command, retrieving a second model from a second platform selected from the group consisting of the Subsurface Platform, the Operations Platform, the Sustainability Platform, the Enterprise Platform, and the Generative Artificial Intelligence (Al) Platform; wherein a model curation application automatically selects the second model to retrieve based at least in part on determining at least one correspondence between the first and second models.

23. The method of claim 22, wherein the at least one correspondence is the relationship or lineage, or both, of the first and second models.

24. The method of claim 22, wherein the first and second platforms are the same.

25. The method of claim 22, wherein the first and second models represent different data types.

26. The method of claim 22, wherein a first user issued both the first and second user commands.

27. The method of claim 22, wherein the first and second platforms are different.

28. The method of claim 22, wherein the first and second models represent different datatypes modeling the same object.

29. The method of claim 22, wherein a first user issued the first user command and a second user issued the second user command.

30. The method of claim 22, wherein the model curation application is disposed in an application suite disposed in an application and management services layer.

31. The method of claim 22, further comprising adjusting at least one of the first and second models with a conversion utility disposed in a unification services layer.

32. The method of claim 22, wherein the first and second platforms are in one or more cloud computing environments.

33. The method of claim 22. further comprising: the first user modifying at least one of the first and second models based at least in part on the contents of the second model; and storing the modified model in its respective platform.

34. The method of claim 22, further comprising: modifying at least one of the first and second models based at least in part on the contents of the first model; and storing the modified model in its respective platform.

35. The method of claim 22, wherein the first and second users, respectively, have one or more job roles selected from the group consisting of Geologist, Drilling Engineer, Chemist, Petrophysicist, Well Placement Engineer, Rock Analysis Engineer, Petroleum / Reservoir Engineer, Structural Engineer, Qualify and Reliability Personnel, Customer Service Personnel, Data Scientist, Environmental Specialist, Facility Specialist, Supplier Chain Personnel, FIR Personnel, Controller, IT Personnel, Sales Personnel, Intelligence Personnel, Production Engineer, Well Services Engineer, Mud Logging Specialist, SubseaEngineer, Field Development Planning Engineer, Fluid Analysis Engineer, Geoscientist, Commercialization Personnel, Repair Personnel, Qualify Assurance Personnel, Process Engineer, HSE Specialist, Import-Export Specialist, Logistics Personnel, FinancePersonnel, Internal Audit Personnel, Software Engineer, Marketing Personnel, Management Personnel, Artificial Lift Engineer, Well Cementing Engineer, Geophysicist, Testing Engineer, Well Planning Engineer, Control and Automation Engineer, Civil Engineer, Materials Engineer, Inventory Personnel, Electrical Engineer, loT Engineer, IIoT Engineer, Construction Designer, Trade Control Compliance Personnel, Regulatory7Compliance Personnel, Accounting Personnel, Tax Personnel, Technical Marketing Personnel, Communications Personnel, and Legal Personnel.

36. The method of claim 22, wherein the model curation application automatically selects the second model to retrieve based at least in part on a job role of the second user.

37. A method performed by an Energy Data Platform to provide access to a plurality of energy data objects, comprising: at a server computing system disposed in the Energy7Data Platform: receiving a first user command for retrieving a first model from a first platform selected from the group consisting of a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (GenAI) Platform; sending the first model in response to the first user command; receiving a second user command for retrieving an unidentified second model from a second platform selected from the group consisting of the Subsurface Platform, the Operations Platform, the Sustainability7Platform, the Enterprise Platform, and the Generative Artificial Intelligence (GenAI) Platform; wherein a model curation application identifies the second model to retrieve based at least in part on determining at least one correspondence between the first model and the unidentified second model;sending the identified second model in response to the second user command.

38. A method for an Energy Worker to use an Energy Data Platform to utilize a plurality' of energy' data objects, comprising: at a user computer system: starting a first energy' data object working application from an application suite disposed in an application and management services layer; using the first energy' data object working application to retrieve a first energy data object from a first energy data management and storage platform; performing one or more adjustments on the first energy data object using the first energy' data object working application; using a cross-domain application from the application suite to retrieve a second energy data object disposed in a second energy' data management and storage platform, wherein the cross-domain application uses a data curation and translation application program interface disposed in a unification services layer; updating the first energy data object based at least in part on the second energy data object; and storing the updated first energy data object in the first energy data management and storage platform.

39. The method of claim 38, further comprising a second Energy Worker: utilizing the Energy Data Platform to retrieve the second energy’ data object and the updated first energy data object that is automatically identified by the Energy Data Platform for review by the second Energy Worker;updating the second energy data object based at least in part on the updated first energy data object: and storing the updated second energy data object in the second energy data management and storage platform.

40. The method of claim 38, wherein the Energy Worker has one or more job roles selected from the group consisting of Geologist, Drilling Engineer, Chemist, Petrophysicist, Well Placement Engineer, Rock Analysis Engineer, Petroleum / Reservoir Engineer, Structural Engineer, Quality and Reliability Personnel, Customer Service Personnel, Data Scientist, Environmental Specialist, Facility Specialist, Supplier Chain Personnel, HR Personnel, Controller, IT Personnel, Sales Personnel, Intelligence Personnel, Production Engineer, Well Services Engineer, Mud Logging Specialist, Subsea Engineer, Field Development Planning Engineer, Fluid Analysis Engineer, Geoscientist, Commercialization Personnel, Repair Personnel, Quality Assurance Personnel, Process Engineer, HSE Specialist, Import- Export Specialist, Logistics Personnel, Finance Personnel, Internal Audit Personnel, Software Engineer, Marketing Personnel, Management Personnel, Artificial Lift Engineer, Well Cementing Engineer, Geophysicist, Testing Engineer, Well Planning Engineer, Control and Automation Engineer, Civil Engineer, Materials Engineer, Inventory Personnel, Electrical Engineer, loT Engineer, IIoT Engineer, Construction Designer, Trade Control Compliance Personnel, Regulatory Compliance Personnel, Accounting Personnel, Tax Personnel. Technical Marketing Personnel, Communications Personnel, and Legal Personnel.

41. A method performed by an Energy Data Platform to provide access to a plurality of energy data objects, comprising:at a server computing system disposed in the Energy Data Platform: receiving an instruction from a first user computer system to initiate a first energy data object working application from an application suite disposed in an application and management services layer in the Energy Data Platform; executing the first energy' data object working application to identify and retrieve a first energy data object from a first energy data management and storage platform; sending a copy of the first energy' data object to the first user computer system; receiving a second instruction from the first user computer system to retrieve an unidentified second energy' data object disposed in a second energy' data management and storage platform, wherein the second instruction initiates a cross-domain application from the application suite that, uses a data curation and translation application program interface disposed in a unification services layer in the Energy Data Platform, and automatically identifies the second energy data object; sending a copy of the second energy data object to the first user computer system; receiving from the first user computer system an updated first energy data object that includes changes based at least in part on the second energy' data object; and storing the updated first energy data object in the first energy data management and storage platform.

42. The method of claim 41. further comprising:receiving a third instruction from a second user computer system to retrieve the second energy data object; based at least in part on receiving the third instruction, sending to the second user computer system a message to suggest loading the updated first energy data obj ect; receiving a fourth instruction from the second user computer system to retrieve the updated first energy data object; sending the updated first energy data object to the second user computer system; receiving an updated second energy data object; and storing the updated second energy data object in the second energy data management and storage platform.

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