Greenhouse gas emissions allocation using data science for industrial applications

Abstract

A method for determining greenhouse gas (GHG) emissions for an entity includes receiving input data. The input data includes an information model, emissions data, and financial data for the entity. The information model defines a plurality of units of the entity. The method also includes determining a relationship between the financial data and the emissions data for at least one unit of the plurality of units. The method further includes generating updated emissions data based on the input data and the relationship between the financial data and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof. The method also includes determining one or more GHG emission intensities for the entity based on the updated emissions data. The method also includes determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

Description

PATENT Atorney Docket No.: IS23.1137-WO-PCTGREENHOUSE GAS EMISSIONS ALLOCATION USING DATA SCIENCE FORINDUSTRIAL APPLICATIONSCross-Reference to Related Applications

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 679,340 filed on August 5, 2024, the entirety of which is incorporated by reference herein.Background

[0002] Companies are grappling with the challenge of reporting their greenhouse gas (GHG) emissions footprint and identifying associated emissions reduction opportunities. Conventional methods of reporting emissions often include a bottoms-up style, where business metrics are converted to emissions by means of equations and emissions factors. The bottoms-up style tends to be sufficient for current reporting standards, but usually lacks the granularity to identify process level emissions reduction opportunities. The bottoms-up style is also time and resource intensive, which risks increasingly being the case as reporting standards become more and more stringent, and more emissions calculations are produced.

[0003] What is needed then is an efficient system and method for high granularity GHG allocation for industrial applications.Summary

[0004] A method for determining greenhouse gas (GHG) emissions for an entity is disclosed. The method may include receiving input data. The input data may include an information model, emissions data, and financial data for the entity. The information model may define a plurality of units of the entity. The method may also include determining a relationship between the financial data and the emissions data for at least one unit of the plurality of units. The method may further include generating updated emissions data based on the input data and the relationship between the financial data and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof. The method may also include determining one or more GHG emission intensities for the entity based on the updated emissions data. The method may also include determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0005] A computing system is also disclosed. The computing system includes one or more processors and a method system. The method system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations for determining greenhouse gas (GHG) emissions for an entity. The operations may include receiving input data for the entity. The input data may include an information model defining a plurality of units. The input data may further include respective emissions data and respective financial data for each unit of the plurality of units. The operations may also include processing the input data. Processing the input data may include generating an index based on the information model, the emissions data, and the financial data. The operations may further include determining a relationship between the financial data and the emissions data for at least one unit of the plurality of units. The operations may also include identifying at least one unit of the plurality of units for which the respective emissions data is incomplete based on the information model and the emissions data to provide at least one identified unit. The operations may also include generating updated emissions data based on the input data and the relationship between the financial data and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof. Generating the updated emissions data may include completing the respective emissions data for the at least one identified unit. The operations may also include determining one or more GHG emission intensities for the entity based on one or more emissions intensity factors defined by one or more external databases, the updated emissions data, or a combination thereof. The operations may also include determining GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

[0006] A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations for determining greenhouse gas (GHG) emissions for an entity. The operations may include receiving input data for the entity. The input data may include an information model defining a plurality of units. The input data may further include respective emissions data and respective financial data for each unit of the plurality of units. The operations may also include determining a relationship between two or more of the financial data, the information model, and the emissions data for at least one unit of the plurality of units. The operations may further include identifying at least one unit of the plurality of units for which thePATENT Atorney Docket No.: IS23.1137-WO-PCT respective emissions data is incomplete based on the information model and the emissions data to provide at least one identified unit. The operations may also include generating updated emissions data based on the input data and the relationship between the two or more of the financial data, the information model, and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof. Generating the updated emissions data may include completing the respective emissions data for the at least one identified unit. The operations may also include determining one or more GHG emission intensities for the entity based on one or more emissions intensity factors defined by one or more external databases, the updated emissions data, or a combination thereof. The operations may also include determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

[0007] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0008] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0009] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0010] Figure 2 illustrates a schematic view of a workflow for determining and / or processing greenhouse gas (GHG) emissions data for an entity, according to an embodiment.

[0011] Figure 3 illustrates a schematic view of a workflow for determining and / or processing greenhouse gas (GHG) emissions data for an entity, according to an embodiment.

[0012] Figure 4 illustrates an exemplary flow-chart representing a workflow for determining carbon emissions for one or more customers of the entity, according to an embodiment.

[0013] Figure 5 illustrates a flowchart of a method for analyzing greenhouse gas (GHG) emissions data, according to an embodiment.

[0014] Figure 6 illustrates a flowchart of a method for determining greenhouse gas (GHG) emissions for an entity, according to an embodiment.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0015] Figure 7 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.Detailed Description

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0017] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0018] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0019] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures,PATENT Atorney Docket No.: IS23.1137-WO-PCT methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.System Overview

[0020] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

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

[0022] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 may include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0023] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In thePATENT Atorney Docket No.: IS23.1137-WO-PCT.NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes may be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0024] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0025] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc ).

[0026] As an example, the simulation component 120 may include one or more features of a simulator such as SYMMETRY™ software (SLB, Houston, Texas). More particularly, SYMMETRY™ may process workflows in a single integrated environment with accurate thermodynamic fluid representation and consistent modeling across multiple disciplines including process, production, and HSE. The simulator integrates steady-state and transient (e.g., dynamic) analyses that may be tailored for each domain. This approach enables users to optimize processes in upstream, midstream, and downstream sectors while maximizing profits and minimizing capital expenditures. It may also help reduce emissions, energy consumption, and waste.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0027] As an example, the simulation component 120 may include one or more features of a simulator such as PIPESIM™ (SLB, Houston, Texas). More particularly, PIPESIM™ is steadystate multiphase flow simulator that incorporates the three areas of flow modeling: multiphase flow, heat transfer and fluid behavior.

[0028] As an example, the simulation component 120 may include one or more features of a simulator such as OLGA™ (SLB, Houston, Texas). More particularly, OLGA™ is a dynamic multiphase flow simulator that models transient flow (e.g., time-dependent behaviors) to maximize production potential. Transient modeling is a component for feasibility studies and field development design. Dynamic simulation is useful in deep water and is used in both offshore and onshore developments to investigate transient behavior in pipelines and wellbores. Transient simulation with the OLGA™ simulator provides an added dimension to steady-state analysis by predicting system dynamics, such as time-varying changes in flow rates, fluid compositions, temperature, solids deposition, and operational changes.

[0029] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0030] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implementedPATENT Atorney Docket No.: IS23.1137-WO-PCT 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.).

[0031] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software may include a framework for model building and visualization.

[0032] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0033] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications may display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0034] As an example, the domain objects 182 may include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0035] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, aPATENT Atorney Docket No.: IS23.1137-WO-PCT user may store a project. At a later time, the project may be accessed and restored using the model simulation layer 180, which may recreate instances of the relevant domain objects.

[0036] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0037] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc ). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0038] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of aPATENT Atorney Docket No.: IS23.1137-WO-PCT workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).GHG Emissions Allocation Using Data Science for Industrial Applications

[0039] The present disclosure relates to the estimation of greenhouse gas (GHG) emissions quantities at a relatively higher level of granularity than they are reported, for example, by a company at a corporate level (or an entity level). This may be accomplished by combining relatively lower granularity GHG emissions data with relatively higher granularity corporate (or entity) data of other metrics using a data science approach. This may accelerate granular emissions estimations by using a data science approach in industrial applications. The outputs may be used to help with emissions forecasting. The outputs may also or instead be used as training data to train a model for the GHG forecasting.

[0040] The method uses corporate data (also referred to as entity data), and therefore enables more efficient emissions reporting and faster identification of reduction opportunities. The method also allocates corporate GHG emissions (also referred to as entity GHG emissions) to a more detailed level than which they were originally reported, by using granular corporate business metrics (also referred to as entity metrics) such as, but not limited to, revenue and / or activity. GHG emissions reported at a corporate (or entity) level data tend to be quite abstracted from a product / service and geographical viewpoint, whereas revenue and / or operational activities tend to be recorded with relatively higher granularity and have a more mature recording process. For example, revenue and / or operational activities may be recorded for virtually each operation, whereas GHG emissions are generally aggregated at a much higher level. The data science approach enables a breakdown of corporate GHG metrics (also referred to as entity GHG metrics) to the product and service level, by combining these with the revenue and activity data in a data science driven approach.

[0041] The method may be used to understand and forecast emissions from oil and gas operations, and to identify emissions reduction measures via technologies. The method may alsoPATENT Atorney Docket No.: IS23.1137-WO-PCT or instead be used to respond to customer requests for emissions estimations and reduction opportunities related to past and future operations. The method may also be used to help companies apply the data science method to their own datasets, to be able to accelerate the emissions reporting process, and identify global opportunities for emissions reduction. The method may also offer considerable scope for companies to accelerate emissions reporting, identify reduction opportunities, and implement emissions reduction measures.Generating Data Science Emissions Allocation Summary

[0042] Figures 2 and 3 generally illustrate schematic views of respective workflows for using existing corporate data (also referred to as entity data) to generate a data science emissions allocation summary, according to an embodiment. As further detailed below, the existing corporate data (also referred to as entity data) may be in the form of corporate (or entity) GHG emissions data, revenue, and / or operational activity information. The less granular GHG emissions data may then be broken down using the revenue data and product and service operational data with a data science driven approach. In one embodiment, “operations,” “operational activity,” and “activity” may be used interchangeably.Determining and Processing GHG Emissions Data

[0043] Figure 2 illustrates a schematic view 200 of a workflow for determining and / or processing greenhouse gas (GHG) emissions data for an entity, according to an embodiment. As illustrated in Figure 2, the method may include receiving input or input data, which may include corporate GHG inventory data, revenue data, products and services operational data, or a combination thereof. The input data may also include, but is not limited to, country relationships, geographical units, sub -geographical units, regions, carbon emission factors (e.g., CEDA emission intensities and / or uncertainties), additional supporting information, or the like, or any combination thereof. The method may also include processing the input data, which may include one or more of data merging, data cleaning, machine learning (ML), classification, or any combination thereof. The method may further include determining the GHG emissions data for the entity, which may include GHG emissions by activities, GHG emissions by locations, or any combination thereof. The GHG emissions data for the entity may also include a customer emissions profile.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0044] Figure 3 illustrates a schematic view 300 of a workflow for determining and / or processing greenhouse gas (GHG) emissions data for an entity, according to an embodiment. As illustrated in Figure 3, the method may include combining corporate GHG inventory data with corporate financial data, such as corporate revenue data to generate or determine a first output. The first output may be broken down to generate a carbon emissions life cycle view. The first output may also be combined with corporate revenue data and operational activity data to generate a second and / or a third output. The second output may be a carbon emissions estimation for each of the activities, each of the services, or a combination thereof. The third output may be a carbon emissions query for each customer of the entity.

[0045] Given the complexity of raw input GHG emission data, a combination of data statistics and machine learning (ML) techniques may be used. The method includes the carbon emission breakdown as part of the data preparation for the following analysis. The method also performs a carbon emission analysis at a higher granular level, which may include the activity, customer, and / or the life-cycle view or activity of the data.

[0046] Regarding the life-cycle view or activity of the data, the present disclosure includes machine learning (ML) based life-cycle analysis techniques that may improve the efficiency of life cycle analysis. The ML based life cycle analysis techniques may include utilizing a language model (e.g., a large language model) to generate a mapping. The disclosed ML based life cycle analysis techniques may reduce time to analyze data related to life cycle analysis by automating, or semi -automating (e.g., with reduced user input), the process, thereby streamlining the generation of life- cycle analysis maps. The disclosed techniques may accelerate the assessment but may also ensure a more efficient utilization of resources, allowing businesses to focus on strategic decisionmaking rather than time-consuming data processing. By utilizing natural language processing, the disclosed techniques may eliminate, or substantially reduce, manual data processing, ensuring a more efficient and accurate assessment process. Through the integration of large language models, the disclosed techniques may provide a comprehensive interpretation of environmental impact data. Going beyond numerical values, the system understands the intricacies of industry language, providing users with a nuanced understanding of the environmental footprint at each operational stage. Moreover, the disclosed techniques may facilitate improved communication of life cycle analysis by presenting visually intuitive life cycle analysis maps through a user-friendly interface. This visual representation makes it simpler for businesses to articulate their sustainability efforts,PATENT Atorney Docket No.: IS23.1137-WO-PCT aiding in internal decision-making, which may provide a further benefit of establishing a positive reputation with stakeholders.

[0047] The ML based life-cycle analysis techniques may include receiving, via a computing system, enterprise facility data associated with an enterprise. The method may also include receiving financial data associated the enterprise and greenhouse gas (GHG) emission data associated with the one or more operations. The method may also include generating a sustainability report associated with one or more facility operations of the enterprise based on aggregating the enterprise facility data, the financial data, and the GHG emission data. The method may also include sending the sustainability report to one or more engineering workflow systems that may determine one or more action plans associated with improving one or more sustainability parameters that corresponds to the one or more facility operations. The method may further include receiving the one or more action plans and sending one or more commands to one or more devices associated with the one or more facility operations based on the one or more action plans, such that the one or more commands are configured to cause the one or more devices to adjust one or more respective operations.

[0048] In an example, carbon emission datasets may have several levels from top to bottom. In some embodiments, the carbon emission datasets may be input with some missing information - missing one, two, or more than two levels of information. For example, the datasets may include division, business line, sub-business line, basin, geo-unit, sub-geo-unit, and country. Some of the carbon emission datasets may have less than the total amount of hierarchic information. To remedy this, the method may impute this missing information and break the carbon emission dataset down into a more regular and higher-level granularity. Based on these granulated carbon emission datasets, the method may use statistics and ML to perform the carbon emission analysis for the activities, customers, and life-cycle analysis.

[0049] The method may use statistics to perform the imputation for the breakdown of carbon emission data in the top-down approach. More particularly, the method may use data science (e.g., statistics and / or machine learning) to perform the carbon emission estimation for the activities and customers. In the life-cycle analysis, a large language model (LLM) as the foundation of generative artificial intelligence may be used for the life-cycle classification for the activities and then the life-cycle analysis.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0050] The method may enable a detailed emissions understanding in a fraction of the time that it takes to perform a conventional typical bottom-up analysis, which can take months for a single product or project. The direct application example of the method may provide a quick high-level granular carbon emission based on the raw low-level granular input carbon emission dataset and other business metrics. Based upon this high-level granularity of carbon emission, the management team can quickly identify the high-carbon-emission business lines or activities and then take the corresponding actions to improve them. This may also satisfy the reporting standards from oil companies, who want to know the general carbon emission amount during bidding activities.

[0051] Applications for this method may include one or more of: Product / Service level emissions reporting; Product / Service level emissions forecasting; Automated identification of decarbonization opportunities based upon forecasted emissions; Generalized oil and gas emissions forecasting capabilities; Exported methodology for oil and gas and hard to abate industries to accelerate reporting of GHG data and identification of reduction opportunities; or any combination thereof.

[0052] As noted above, the method may include a carbon emission breakdown or performing an emissions breakdown. To perform the emissions breakdown, an analysis may be performed to determine one or more relationships between two or more of the financial data, the activity data, and the GHG emissions. For example, the financial data (both revenue and spend) may exhibit a relatively strong relationship with emissions across almost all business lines and geographies. Accordingly, the either revenue or spend may be utilized to conduct or perform the emissions breakdown using revenue as a defining feature. To allocate GHG emissions to the missing business lines or geographic areas, a carbon distribution feature may be calculated or determined. The carbon distribution feature may be determined with the statistical probabilities for all possible business lines and geographic organizations within a certain level of detail. These business lines and geographic organizations may then share the carbon emissions based on these probabilities. Distribution features may vary between different emissions scopes and scope categories. To ensure minimal or no leakage of emissions data, a total sum-up of the probabilities may be 100%. The distribution features may be calculated within the certain granularity ranges, which may be based on the granularity of the imputation scenarios, for both revenue and GHG emissions. The statistical probabilities may be statistically calculated from the carbon emissions (CO2e) orPATENT Atorney Docket No.: IS23.1137-WO-PCT recognized revenues. Performing the emissions breakdown may provide the GHG emissions data and the financial data (e.g., revenue) at the same level of granularity. For example, performing the emissions breakdown may provide granularity down to at least a sub-unit level, a sub-business line level, a country level, a monthly level, or any combination thereof.Determining Carbon Emissions

[0053] Figure 4 illustrates an exemplary flow-chart 400 representing a workflow for determining carbon emissions for one or more customers of the entity, according to an embodiment. As illustrated in Figure 4, the GHG emissions data and the financial data, which may be at the same level of granularity after conducting the emissions breakdown, may be coupled, combined, or otherwise merged with one another for each GHG emissions scope and / or category to determine respective emission intensities for each scope and / or category. The emissions intensities may be represented as the GHG emissions per dollar, CChe / S, or the like. As illustrated in Figure 4, the respective emissions intensities of each scope and / or category may then be utilized to determine respective GHG emissions for a customer based on the financial data, such as the revenue for the customer. The GHG emissions may be determined at a granularity down to at least the sub-unit level, the sub-business line level, the country level, the monthly level, or any combination thereof. With respect to Figure 4, it may be assumed that the respective intensities of each category may be invariant for all customers within the granularity of any one or more of the unit, the sub-unit, sub-business line, business line, geographical unit, sub-geographical unit, or any combination thereof. It may also be assumed that the carbon emissions may be linearly proportional to the financial data, such as the spend value and / or the revenue value.

[0054] In one embodiment, the methods disclosed herein may include using a large language model (LLM) to refine the respective emissions intensities to at least a product and / or services level of granularity. For example, each sub-unit (e.g., sub-business line, sub -geographical unit) may be responsible for one or more products and / or services that are provided to one or more customers. To refine the emissions intensities down to the granularity of the products and / or services, and LLM based methodology may be employed. For example, the description of a given product and / or service may be matched with industry standard descriptions, such as NAICS codes, and the NAICS codes may be matched with emissions factors of a third party database, such as CEDA. The emissions factors, such as CEDA emissions factors may then be matched with eachPATENT Atorney Docket No.: IS23.1137-WO-PCT of the products and / or services. In view of the foregoing, the emission factors may be refined down to the product and / or the service level by considering the emissions factors, such as the CEDA emissions factors.Exemplary Workflow

[0055] Figure 5 illustrates a flowchart of a method 500 for analyzing greenhouse gas (GHG) emissions data, according to an embodiment. An illustrative order of the method 500 is provided below; however, one or more portions of the method 500 may be performed in a different order, simultaneously, repeated, or omited. At least a portion of the method 500 may be performed by a computing system.

[0056] The method 500 may include receiving first GHG emissions data, as at 505. The first GHG emissions data may be or include, but are not limited to, carbon dioxide equivalent (CChe) data related to the entity. For example, the first GHG emissions data may be or include CChe data related to one or more scopes, one or more categories, one or more activities, or the like, of the entity.

[0057] The method 500 may also include receiving corporate business metrics (also referred to as entity metrics), as at 510. The entity metrics may include financial data and / or operational activity associated with an entity (e.g., a corporation, an organization, a governmental agency, etc.). The entity metrics have a higher granularity than the first GHG emissions data.

[0058] The method 500 may also include generating second GHG emissions data based upon the entity metrics, as at 515. The second GHG emissions data may be or include, but are not limited to, carbon dioxide equivalent (CChe) data generated from the respective entity metrics for any one or more of the scopes, categories, activities, or the like, of the entity. The second GHG emissions data may have a relatively higher or relatively lower granularity than the entity metrics.

[0059] The method 500 may also include generating updated GHG emissions data based upon the first and second GHG emissions data, as at 520.

[0060] The method 500 may also include validating the updated GHG emissions data, as at 525. More particularly, the first GHG emissions data, the second GHG emissions data, the updated GHG emissions data, or a combination thereof may be validated with representative bottoms-up life cycle calculations. When the model disagrees with the bottoms-up calculation by more than a predetermined amount, a coefficient may be introduced to calibrate / tune it.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0061] The method 500 may also include generating a report based upon the updated GHG emissions data, as at 530. The report may include graphs showing GHG emissions by company organizational unit, phase of oil and gas exploration, activity, customer, location, or a combination thereof. In one embodiment, the report may be displayed (e.g., graphically).

[0062] The method 500 may also include generating opportunities to reduce future GHG emissions based upon the updated GHG emissions data, as at 535. The opportunities may be determined or generated by matching the resultant GHG emissions profile (e.g., the updated GHG emissions data) with technologies and approaches that can help reduce components of it. For example, if the emissions profile indicates a high level of emissions coming from drilling fluids, then a low carbon drilling fluid may be considered. In another example, if the emissions profile indicated a high level of GHG coming from use of electrical submersible pumps (ESPs), then the system may recommend to either implement higher efficiency ESPs or source low carbon electricity to power them. In another example, if the emissions profile shows a large amount of emissions from flaring, then the system may recommend a specific technology to reduce emissions from that operations, such as an enclosed flare.

[0063] The method 500 may also include performing a wellsite action, as at 540. The wellsite action may be based upon or in response to the report or the opportunities. The wellsite action may be or include generating and / or transmitting a signal (e.g., using a computing system) that instructs or causes a physical action to occur at a wellsite. The wellsite action may also or instead include performing the physical action at the wellsite. The physical action may include selecting where to drill a wellbore, drilling the wellbore, varying a weight and / or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and / or flow rate of a fluid pumped into the wellbore, or the like.

[0064] In another embodiment, the physical action may be or include using low carbon cement in well construction. This may reduce the embodied carbon of oilfield cements by removing the portland cement component of the slurry mixture and replacing it with a material that has a lower carbon footprint in its manufacture.

[0065] In another embodiment, the physical action may be or include switching from oil-based mud to water-based mud. This may reduce the embodied carbon of drilling fluids by replacing oil based systems with similar or better performing water based systems.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0066] In another embodiment, the physical action may be or include implementing waste heat recovery. Waste heat recovery uses a heat exchanger to transfer heat from process outputs to another part of the process, to increase efficiency and reduce fuel combustion for heating.

[0067] In another embodiment, the physical action may be or include installing electric controls to replace pneumatic actuators. Electric actuators can replace pneumatic systems that run on naturally produced (and methane rich) natural gas. In the case of the latter, this gas may be vented to the atmosphere and hence this source of emissions can be eliminated by this process. In another embodiment, the physical action may be or include installing vapor recovery units. They may capture the vent gas from the facility. The gas is either injected back into the process for eventual production, or flared. In either case, emissions are reduced.

[0068] In another embodiment, the physical action may be or include routing flare gas to power generation. This process takes gas which would otherwise be flared, for example, associated gas during oil production, and converts it to electrical power. This power is then used locally or connected to the electrical grid.

[0069] In another embodiment, the physical action may be or include using electric frac units with a renewable source of electricity. Implementation of hydraulic frac pumps that run on electricity (either from the grid, or produced locally), reduces emissions compared to the traditional alternative of a diesel powered frac unit.

[0070] In another embodiment, the physical action may be or include hybridizing the power system of the drilling rig. Installation of a software control system to optimize generator operations, along with battery storage, may improve rig power generation efficiency (e.g., analogous to a hybrid car).

[0071] In another embodiment, the physical action may be or include improving flare gas combustion efficiency by installing enclosed flares in place of open flares. Enclosed flare enabling improves combustion efficiency due to the controlled environment in which the "burn" is taking place, compared to regular flares where this volume is less constrained. The increased combustion efficiency reduces the overall GHG impact, because less methane is released to the atmosphere.Another Exemplary Workflow

[0072] Figure 6 illustrates a flowchart of a method 600 for determining greenhouse gas (GHG) emissions for an entity, according to an embodiment. An illustrative order of the method 600 isPATENT Atorney Docket No.: IS23.1137-WO-PCT provided below; however, one or more portions of the method 600 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 600 may be performed by a computing system.

[0073] The method 600 may include receiving input data including an information model, emissions data, and financial data for the entity, as at 602. One or more of the information model and the financial data may have or include a relatively higher granularity than the emissions data. The input data may be related to or associated with the entity. The entity may be or include, but is not limited to, a corporation, a country, a region, a municipality, a government, or the like, or a combination thereof. The information model may be a corporate information model or an entity information model. The information model may define a plurality of units of the entity. The units of the entity may be or include, but are not limited to, a business unit, a business-line, a geographical unit, a geographical area, a business group, a subsidiary of the entity, operating company, division, or the like, or any combination thereof. The information model may define one or more sub-units of each unit of the plurality of units. The subunits of the entity may be or include, but are not limited to, any sub-unit of the one or more units, a sub-business-line, a subbusiness unit, a sub -geographical unit, a sub-geographical area, a sub-business group, division, or the like, or any combination thereof. The information model may define one or more date fields for each unit of the plurality of units and each sub-unit of the one or more sub-units. The information model may define one or more operational activities for each unit of the plurality of units. The one or more operational activities data may include operational time per activity, operational environment, quantity of product delivered per unit of activity, products and services data, or the like, or any combination thereof. The information model may define an organizational structure of the entity. The organizational structure may include a hierarchy of the plurality of units. The hierarchy may represent one or more of a geographical structure, a business structure, a functional structure, or any combination thereof. The financial data may include a respective revenue value, a respective spend value, one or more respective invoices, or the like, or a combination thereof for at least one unit of the plurality of units, at least one sub-unit of the one or more sub-units, or a combination thereof. The revenue value may be associated with at least one date field of the one or more date fields. The revenue value may be based on one or more of revenue cycle information, quotes, field tickets, sales orders, recognized revenue, revenue factors, line items, or the like, or a combination thereof. The revenue value may include customer-levelPATENT Atorney Docket No.: IS23.1137-WO-PCT revenue data for one or more customers. The spend value may be associated with at least one date field of the one or more date fields. The spend value may include customer-level spend data for the one or more customers. The emissions data may include one or more emissions records. Each emissions record of the one or more emissions records may be associated with at least one unit of the plurality of units, at least one sub-unit of the one or more sub-units, or a combination thereof. Each emissions record of the one or more emissions records may include one or more of a scope designation as defined by a greenhouse gas accounting framework, a carbon dioxide equivalent value associated with at least one date field of the one or more date fields, or a combination thereof. The scope designation may correspond to one of Scope 1, Scope 2, or Scope 3. Scope 3 may include one or more categories as defined by the greenhouse gas reporting framework, the entity, or any combination thereof. At least one of Scope 1 and Scope 2 may include one or more categories as defined by the entity. In at least one embodiment, both Scope 1 and Scope 2 include one or more categories as defined by the entity.

[0074] In one embodiment, the method 600 may include processing the input data. Processing the input data may include generating an index based on the information model, the emissions data, and the financial data. Processing the input data may also include removing special characters from the input data. Processing the input data may also include removing redundancies.

[0075] The method 600 may also include determining a relationship between two or more of the financial data, the information model, and the emissions data for at least one unit of the plurality of units, as at 604. The relationship may be between the financial data and the emissions data. The relationship may be between the respective revenue value of the financial data and the respective carbon dioxide equivalent value of the emissions data. Determining the relationship between the two or more of the financial data, the information model, and the emissions data may include utilizing statistical probabilities.

[0076] In at least one embodiment, the method 600 may include identifying at least one unit of the plurality of units, at least one sub-unit of the one or more sub-units, or a combination thereof for which the respective emissions data is incomplete based on the information model and the emissions data to produce at least one identified unit, at least one identified sub-unit, or a combination thereof. In one example, the respective emissions data of the at least one identified unit, the at least one identified sub-unit, or a combination thereof may not include a respective greenhouse gas (GHG) emissions data associated therewith.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0077] The method 600 may further include generating updated emissions data based on the input data and the relationship between the two or more of the financial data, the information model, and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof, as at 606. Generating the updated emissions data may include determining one or more carbon distribution factors for one or more units of the plurality of units, one or more sub-units of each unit of the plurality of units, or a combination thereof based on two or more of the financial data, the information model, and the emissions data using the statistical probabilities. Generating the updated emissions data may also include generating the updated emissions data for the one or more units of the plurality of units, the one or more sub-units of each subunit of the plurality of units, or a combination thereof based on the one or more carbon distribution factors. Generating the updated emissions data may include determining the respective GHG emissions data for the at least one identified unit, the at least one identified sub-unit, or a combination thereof based on the one or more carbon distribution features.

[0078] The statistical probabilities may be determined from the respective carbon dioxide equivalent value of each emissions record of the emissions data, the respective revenue value of the financial data, or a combination thereof. The one or more carbon distribution features may include one or more carbon scope distribution features, one or more carbon category distribution features, or a combination thereof. Each scope designation may include a respective carbon scope distribution feature of the one or more carbon scope distribution features. Each category of the one or more categories of Scope 1, Scope 2, Scope 3, or a combination thereof may include a respective carbon category distribution feature of the one or more carbon category distribution features.

[0079] The method 600 may also include determining one or more GHG emission intensities for the entity based on one or more emissions intensity factors defined by one or more external databases, the updated emissions data, or a combination thereof, as at 608. Determining the one or more GHG emission intensities may include determining, for each unit of the plurality of units and each sub-unit of the one or more sub-units, a respective GHG emissions intensity for each scope designation and each category of the one or more categories based on the updated emissions data. The respective GHG emissions intensity may be represented as the respective carbon dioxide equivalent value per each scope designation or each category of one or more categories.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0080] In at least one embodiment, the one or more GHG emission intensities may be based on the updated emissions data and one or more emissions intensity factors from one or more external databases. Determining the one or more GHG emission intensities for the entity further may include matching the one or more operational activities with the one or more emissions intensity factors from the one or more external databases using a large language model (LLM). Matching the one or more operational activities with the one or more emissions intensity factors may include matching the one or more operational activities of the entity with industrial standard descriptions using the LLM. Matching the one or more operational activities with the one or more emissions intensity factors may also include matching the industrial standard descriptions with the one or more emissions intensity factors of the one or more external databases to associate the one or more operational activities with the emissions intensity factors of the one or more databases. The one or more external databases may be or include, but is not limited to, one or more third party databases. The one or more third party databases may be or include, but is not limited to, the Climate Emissions Data Aggregator (CEDA). The one or more emissions intensity factors may include respective intensities of the one or more third party databases. The industrial standard descriptions may include industry specific codes. The industry specific codes may include the North American Industry Classification System (NAICS) code, the International Standard Industrial Classification code (ISIC), the Standard Industrial Classification (SIC) code, or the like, or any combination thereof.

[0081] The method 600 may also include determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data, as at 610. Determining the GHG emissions for the entity may include determining a respective GHG emission for at least one customer of the one or more customers based on the one or more GHG emission intensities and the financial data. The respective GHG emissions for the at least one customer of the one or more customers may be based on the one or more GHG emission intensities and one or more of the respective customer-level revenue data, the respective customer-level spend data, or a combination thereof.Exemplary Computing System

[0082] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 7 illustrates an example of such a computing system 700, in accordancePATENT Atorney Docket No.: IS23.1137-WO-PCT with some embodiments. The computing system 700 may include a computer or computer system 701A, which may be an individual computer system 701A or an arrangement of distributed computer systems. The computer system 701A includes one or more analysis modules 702 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 702 executes independently, or in coordination with, one or more processors 704, which is (or are) connected to one or more storage media 706. The processor(s) 704 is (or are) also connected to a network interface 707 to allow the computer system 701A to communicate over a data network 709 with one or more additional computer systems and / or computing systems, such as 70 IB, 701C, and / or 70 ID (note that computer systems 70 IB, 701C and / or 70 ID may or may not share the same architecture as computer system 701 A, and may be located in different physical locations, e.g., computer systems 701 A and 70 IB may be located in a processing facility, while in communication with one or more computer systems such as 701 C and / or 70 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0083] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0084] The storage media 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 7 storage media 706 is depicted as within computer system 701 A, in some embodiments, storage media 706 may be distributed within and / or across multiple internal and / or external enclosures of computing system 701 A and / or additional computing systems. Storage media 706 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable orPATENT Atorney Docket No.: IS23.1137-WO-PCT 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.

[0085] In some embodiments, computing system 700 contains one or more method execution module(s) 708. In the example of computing system 700, computer system 701A includes the method execution module 708. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

[0086] It should be appreciated that computing system 700 is merely one example of a computing system, and that computing system 700 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 7, and / or computing system 700 may have a different configuration or arrangement of the components depicted in Figure 7. The various components shown in Figure 7 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0087] 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.

[0088] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 700, Figure 7), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.PATENT Atorney Docket No.: IS23.1137-WO-PCT

[0089] The foregoing description, for purposes of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

PATENT Atorney Docket No.: IS23.1137-WO-PCTCLAIMSWhat is claimed is:

1. A method for determining greenhouse gas (GHG) emissions for an entity, the method comprising: receiving input data comprising an information model, emissions data, and financial data for the entity, wherein the information model defines a plurality of units of the entity; determining a relationship between the financial data and the emissions data for at least one unit of the plurality of units; generating updated emissions data based on the input data and the relationship between the financial data and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof; determining one or more GHG emission intensities for the entity based on the updated emissions data; and determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

2. The method of Claim 1, wherein generating the updated emissions data comprises determining one or more carbon distribution factors for one or more units of the plurality of units based on two or more of the financial data, the information model, and the emissions data using the statistical probabilities.

3. The method of Claim 2, wherein the updated emissions data for the one or more units of the plurality of units are based on the one or more carbon distribution factors.

4. The method of Claim 1, wherein determining the one or more GHG emission intensities comprises determining, for each unit of the plurality of units, a respective GHG emissions intensity based on the updated emissions data.

5. The method of Claim 1, wherein determining the one or more GHG emission intensities for the entity comprises matching one or more operational activities of the entity with one or morePATENT Atorney Docket No.: IS23.1137-WO-PCT emissions intensity factors defined by one or more external databases using a large language model (LLM).

6. The method of Claim 5, wherein matching the one or more operational activities with the one or more emissions intensity factors comprises: matching the one or more operational activities of the entity with industrial standard descriptions using the LLM; and matching the industrial standard descriptions with the one or more emissions intensity factors of the one or more external databases to associate the one or more operational activities with the emissions intensity factors of the one or more databases.

7. The method of Claim 1, wherein determining the GHG emissions for the entity comprises determining a respective GHG emission for at least one customer of the entity based on the one or more GHG emission intensities and the financial data.

8. The method of Claim 1, wherein one or more of the information model and the financial data comprises a relatively higher granularity than the emissions data.

9. The method of Claim 1, further comprising displaying the GHG emissions for the entity.

10. The method of Claim 1, further comprising performing an action in response to determining the GHG emissions for the entity, wherein the action comprises generating or transmitting a signal that recommends, instructs, or causes a physical action to occur, and wherein the physical action comprises one or more of performing a wellsite action, using a low carbon cement in well construction, switching from an oil-based mud to a water-based mud, implementing a waste heat recovery process, replacing pneumatic actuators with electric controls, routing flare gas to power generation, utilizing electric frac units with a renewable source of electricity, hybridizing a power system of a drilling rig, or a combination thereof.

11. A computing system, comprising: one or more processors; andPATENT Atorney Docket No.: IS23.1137-WO-PCT a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations for determining greenhouse gas (GHG) emissions for an entity, the operations comprising: receiving input data for the entity, wherein the input data comprises an information model defining a plurality of units, and wherein the input data further comprises respective emissions data and respective financial data for each unit of the plurality of units; processing the input data, wherein processing the input data comprises generating an index based on the information model, the emissions data, and the financial data; determining a relationship between the financial data and the emissions data for at least one unit of the plurality of units; identifying at least one unit of the plurality of units for which the respective emissions data is incomplete based on the information model and the emissions data to provide at least one identified unit; generating updated emissions data based on the input data and the relationship between the financial data and the emissions data using a machine-learning (ML) model, statistical probabilities, or a combination thereof, wherein generating the updated emissions data comprises completing the respective emissions data for the at least one identified unit; determining one or more GHG emission intensities for the entity based on one or more emissions intensity factors defined by one or more external databases, the updated emissions data, or a combination thereof; and determining GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

12. The computing system of Claim 11, wherein the information model defines one or more operational activities for each unit of the plurality of units, and wherein determining the one or more GHG emission intensities for the entity comprises matching the one or more operational activities with the one or more emissions intensity factors defined by the one or more external databases using a large language model (LLM).PATENT Atorney Docket No.: IS23.1137-WO-PCT13. The computing system of Claim 11 , wherein generating the updated emissions data further comprises: determining one or more carbon distribution factors for one or more units of the plurality of units based on two or more of the financial data, the information model, and the emissions data using the statistical probabilities; and generating the updated emissions data for the one or more units of the plurality of units based on the one or more carbon distribution factors.

14. The computing system of Claim 11, determining the GHG emissions for the entity comprises determining a respective GHG emission for at least one customer of the entity based on the one or more GHG emission intensities and the financial data.

15. The computing system of Claim 11, wherein one or more of the information model and the financial data comprises a relatively higher granularity than the emissions data.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations for determining greenhouse gas (GHG) emissions for an entity, the operations comprising: receiving input data for the entity, wherein the input data comprises an information model defining a plurality of units, and wherein the input data further comprises respective emissions data and respective financial data for each unit of the plurality of units; determining a relationship between two or more of the financial data, the information model, and the emissions data for at least one unit of the plurality of units; identifying at least one unit of the plurality of units for which the respective emissions data is incomplete based on the information model and the emissions data to provide at least one identified unit; generating updated emissions data based on the input data and the relationship between the two or more of the financial data, the information model, and the emissions data using a machinelearning (ML) model, statistical probabilities, or a combination thereof, wherein generating the updated emissions data comprises completing the respective emissions data for the at least one identified unit;PATENT Atorney Docket No.: IS23.1137-WO-PCT determining one or more GHG emission intensities for the entity based on one or more emissions intensity factors defined by one or more external databases, the updated emissions data, or a combination thereof; and determining the GHG emissions for the entity based on the one or more GHG emission intensities and the financial data.

17. The non-transitory computer-readable medium of Claim 16, wherein generating the updated emissions data further comprises: determining one or more carbon distribution factors for one or more units of the plurality of units based on two or more of the financial data, the information model, and the emissions data using the statistical probabilities; and generating the updated emissions data for the one or more units of the plurality of units based on the one or more carbon distribution factors.

18. The non-transitory computer-readable medium of Claim 16, wherein: the information model defines one or more operational activities for each unit of the plurality of units; the one or more GHG emission intensities are based on the one or more emissions intensity factors defined by the one or more external databases and the updated emissions data; and determining the one or more GHG emission intensities for the entity further comprises matching the one or more operational activities with the one or more emissions intensity factors from the one or more external databases using a large language model (LLM).

19. The non-transitory computer-readable medium of Claim 18, wherein matching the one or more operational activities with the one or more emissions intensity factors comprises: matching the one or more operational activities of the entity with industrial standard descriptions using the LLM; and matching the industrial standard descriptions with the one or more emissions intensity factors defined by the one or more external databases to associate the one or more operational activities with the emissions intensity factors of the one or more external databases.PATENT Atorney Docket No.: IS23.1137-WO-PCT20. The non-transitory computer-readable medium of Claim 16, wherein: the financial data comprises a respective revenue value for at least one unit of the plurality of units; the emissions data comprises a respective carbon dioxide equivalent value for the at least one unit of the plurality of units; and the relationship is between the respective revenue value of the financial data and the respective carbon dioxide equivalent value of the emissions data.

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