Drilling equipment parameter optimization workflow

Abstract

A method for controlling parameters for drilling equipment includes receiving input data related to drilling equipment. The method also includes creating a plurality of scenarios based upon the input data. Each scenario includes a plurality of first parameters. Creating the scenarios includes selecting first values for each of the first parameters. The method also includes selecting one or more of the scenarios and one or more second parameters. The method also includes simulating the one or more selected scenarios to determine second values for the one or more selected second parameters.

Description

PATENT Atorney Docket No.: IS24.1836-WODRILLING EQUIPMENT PARAMETER OPTIMIZATION WORKFLOWCross-Reference to Related Applications

[0001] This application claims priority to India Patent Application No. 202411095104, filed on December 3, 2024, which is incorporated by reference.Background

[0002] Data relating to equipment used at a wellsite may be measured in the field. For example, data relating to drilling equipment on a drilling rig may be measured using one or more sensors. The data may then be analyzed by a model to determine the performance and / or health of the equipment. However, an expert user (e.g., data scientist) still reviews the results. Therefore, what is needed is an improved system and method that produces results that may be reviewed by a user that is not an expert in the field.Summary

[0003] A method for controlling parameters for drilling equipment includes receiving input data related to drilling equipment. The method also includes creating a plurality of scenarios based upon the input data. Each scenario includes a plurality of first parameters. Creating the scenarios includes selecting first values for each of the first parameters. The method also includes selecting one or more of the scenarios and one or more second parameters. The method also includes simulating the one or more selected scenarios to determine second values for the one or more selected second parameters.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving input data related to drilling equipment. The input data is related to a health, a performance, and / or an ambient condition of the drilling equipment. The drilling equipment includes one or more generators. The operations also include creating a plurality of scenarios based upon the input data. Each scenario includes a plurality of first parameters. Creating the scenarios includes selecting first values for each of the first parameters. The operations alsoPATENT Atorney Docket No.: IS24.1836-WO include determining an availability and a quality of the input data for the scenarios. The availability and the quality are determined when the scenarios are BESS-enabled and load dependent start and stop (LDSS)-enabled. The availability and the quality are determined by determining that the input data is monotonic, determining that the first values are not repeated, and / or determining that the input data is complete within a predetermined range. The operations also include selecting one or more of the scenarios and one or more second parameters. The one or more second parameters include total energy, total fuel, economy, fuel saving, emission saving, genset saving, power overload, a percentage of time that each of one or more gensets is running, a price of the fuel, an offset cost for carbon dioxide (CO2), a cost to run each of one or more engines per hour, or a combination thereof. The operations also include simulating the one or more selected scenarios to determine second values for the one or more selected second parameters.

[0005] A non-transitory computer-readable medium is also disclosed. The medium includes instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving input data related to drilling equipment. The input data is related to a health, a performance, and an ambient condition of the drilling equipment. The drilling equipment includes one or more generators, one or more engines, one or more gensets including a combination of one of the generators and one of the engines, one or more batteries, a top drive, a drawworks, and a mud pump. The input data also includes a number of the one or more generators, an electrical current generated by the one or more generators, a voltage generated by the one or more generators, a temperature of the one or more generators, and a total power used by the top drive, the drawworks, and the mud pump. The operations also include creating a plurality of scenarios based upon the input data. Each scenario includes a plurality of first parameters. Creating the scenarios includes selecting first values for each of the first parameters. The first parameters include a rig contractor, a name of a drilling rig where the drilling equipment is located, a technology installed on the drilling rig, a time zone where the drilling rig is located, a number of the one or more engines on the drilling rig, a determination whether fuel is available, a determination whether the one or more engines are enabled, a determination whether battery energy storage system (BESS) power is available, a number of the one or more engines running, a power generated by a first of the one or more generators, a power generated by a second of the one or more generators, and a date and time. The operations also include determining an availability and a quality of the input data for the scenarios.PATENT Atorney Docket No.: IS24.1836-WOThe availability and the quality are determined when the scenarios are BESS-enabled and load dependent start and stop (LDSS)-enabled. The availability and the quality are determined by determining that the input data is monotonic, determining that the first values are not repeated, and determining that the input data is complete within a predetermined range. The availability and the quality of the input data are displayed in a single row or column with a first color or shading indicating time periods where the input data is available with the quality above a threshold, a second color or shading indicating time periods where the input data is available with the quality below the threshold, and a third color or shading indicating time periods where the input data is unavailable. The operations also include selecting one or more of the scenarios and one or more second parameters. The one or more second parameters include total energy, total fuel, economy, fuel saving, emission saving, genset saving, power overload, a percentage of time that each genset is running, a price of the fuel, an offset cost for carbon dioxide (CO2), and a cost to run each of the engines per hour. The operations also include simulating the one or more selected scenarios to determine second values for the one or more selected second parameters. The one or more selected scenarios are simulated in response to determining that more than a predetermined amount of the input data is available with the quality above the threshold. The one or more selected scenarios are simulated using an artificial intelligence (Al) machine learning (ML) model.

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

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

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

[0009] Figure 2 illustrates a flowchart of a method for controlling (e.g., optimizing) parameters for drilling equipment on a drilling rig, according to an embodiment.

[0010] Figure 3 illustrates an image of input data, according to an embodiment.PATENT Atorney Docket No.: IS24.1836-WO

[0011] Figure 4 illustrates an image of a scenario including a portion of the input data, according to an embodiment.

[0012] Figure 5 illustrates an image showing an availability and a quality of the input data, according to an embodiment.

[0013] Figure 6 illustrates an image showing titles assigned to first parameters in the input data, according to an embodiment.

[0014] Figure 7 illustrates an image showing two scenarios selected, according to an embodiment.

[0015] Figure 8 illustrates an image showing values for second parameters in three simulated scenarios, according to an embodiment.

[0016] Figure 9 illustrates a graph showing results for multiple scenarios, according to an embodiment.

[0017] Figure 10 illustrates an image showing the parameters and / or scenarios after the parameter(s) have been modified, according to an embodiment.

[0018] Figure 11 illustrates a flowchart of a portion of the method 200, according to an embodiment.

[0019] Figure 12 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

[0020] 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.

[0021] 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 firstPATENT Atorney Docket No.: IS24.1836-WO 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.

[0022] 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.

[0023] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.System Overview

[0024] 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).

[0025] 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, anPATENT Atorney Docket No.: IS24.1836-WO analysis / visualization component 142 and a workflow component 144. Tn operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0026] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0027] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Obj ect classes can be used to instantiate obj ect instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0028] 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,PATENT Atorney Docket No.: IS24.1836-WO output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0029] 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 ).

[0030] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0031] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0032] 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® frameworkPATENT Atorney Docket No.: IS24.1836-WO where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

[0033] 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.

[0034] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0035] As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0036] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0037] 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 anyPATENT Atorney Docket No.: IS24.1836-WO 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.).

[0038] 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.

[0039] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in thePATENT Atorney Docket No.: IS24.1836-WOOCEAN® 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.).Drilling Equipment Parameter Optimization Workflow

[0040] The system and method described herein may analyze parameters of (e.g., drilling) equipment at a wellsite. More particularly, the system and method may analyze and control (e.g., optimize) parameters of drilling equipment on a drilling rig. Figure 2 illustrates a flowchart of a method 200 for controlling (e.g., optimizing) parameters for drilling equipment on a drilling rig, according to an embodiment. An illustrative order of the method 200 is provided below; however, one or more portions of the method 200 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 200 may be performed with a computing system (described below).

[0041] The method 200 may include receiving input data related to drilling equipment, as at 205. Figure 3 illustrates an image of a file 300 that includes the input data, according to an embodiment. More particularly, the input data may be related to a health, a performance, and / or an ambient condition of the drilling equipment. The drilling equipment may be or include one or more generators, one or more engines, one or more gensets including a combination of one of the generators and one of the engines, one or more batteries, a top drive, a drawworks, a mud pump, or a combination thereof. The input data also may also or instead include a number of the one or more generators, an electrical current generated by the one or more generators, a voltage generated by the one or more generators, a temperature of the one or more generators, a total power used by the top drive, the drawworks, and / or the mud pump, or a combination thereof.

[0042] The method 200 may also include creating a plurality of scenarios based upon the input data, as at 210. Figure 4 illustrates an image of a scenario 400 including a portion of the input data, according to an embodiment. Each scenario may include a plurality of first parameters 410A- 4101. Creating the scenarios 400 may include selecting first values for each of the first parameters 410A-410I. As shown in Figure 4, the first parameters may include a rig contractor 410A, a name of a drilling rig where the drilling equipment is located 410B, a technology installed on the drilling rig 410C, a time zone where the drilling rig is located 410D, a number of the one or more engines on the drilling rig 410E, a determination whether fuel and / or fuel rate is available 41 OF, a determination whether the one or more engines are enabled 410G, a determination whether batteryPATENT Atorney Docket No.: IS24.1836-WO energy storage system (BESS) power is available 41 OH, a date and time 4101, a number of the one or more engines running, a power generated by a first of the one or more generators, a power generated by a second of the one or more generators, or a combination thereof.

[0043] The method 200 may also include determining an availability and / or a quality of the input data for the scenarios, as at 215. Figure 5 illustrates an image 500 showing the availability and the quality of the input data, according to an embodiment. The availability and the quality may be determined when the scenarios are BESS-enabled and load dependent start and stop (LDSS)- enabled. The availability and the quality may be determined by determining that the input data is monotonic, determining that the first values are not repeated, and / or determining that the input data is complete within a predetermined range. The availability and the quality of the input data may be displayed in a single row or column with a first color or shading 510 indicating time periods where the input data is available with the quality above a threshold, a second color or shading 520 indicating time periods where the input data is available with the quality below the threshold, and a third color or shading 530 indicating time periods where the input data is unavailable.

[0044] The method 200 may also include assigning titles to the first parameters 410A-410I in the input data, as at 220. Figure 6 illustrates an image showing titles assigned to the first parameters 410A-410I in the input data, according to an embodiment. More particularly, a web application may scan the fields and / or titles in the input data, and the fields and / or titles may be assigned to the first parameters 410A-410I. For example, a user may assign the titles to the first parameters 410A-410I. In another embodiment, the titles may be automatically detected and / or assigned.

[0045] The method 200 may also include selecting one or more of the scenarios and one or more second parameters, as at 225. Figure 7 illustrates an image showing two scenarios 710, 720 selected, according to an embodiment. As shown in Figure 8, the one or more second parameters may be or include total energy 810A, total fuel 810B, economy 810C, fuel saving 810D, emission saving 810E, genset saving 81 OF, power overload 810G, a percentage of time that each genset is running 81 OH, a price of the fuel 8101, an offset cost for carbon dioxide (CO2) 810J, a cost to run each of the engines per hour 81 OK, or a combination thereof.

[0046] The method 200 may also include simulating the one or more selected scenarios to determine second values for the one or more selected second parameters, as at 230. Figure 8 illustrates an image showing second values for the second parameters 810A-810K in threePATENT Atorney Docket No.: IS24.1836-WO simulated scenarios, according to an embodiment. The one or more selected scenarios may be simulated in response to determining that more than a predetermined amount of the input data is available with the quality above the threshold. The one or more selected scenarios may be simulated using an artificial intelligence (Al) machine learning (ML) model.

[0047] The method 200 may also include comparing the second values of the one or more selected second parameters in the one or more simulated scenarios, as at 235. An example of this is shown in Figure 8. Figure 9 also illustrates a graph 900 showing results (e.g., savings) for multiple (e.g., simulated) scenarios, according to an embodiment. The simulated scenarios may be compared against one another based upon cost and / or production. For example, the simulated scenarios may be returned to a dashboard for comparison to verify payback periods after entering the cost details.

[0048] The method 200 may also include displaying results of the comparison, as at 240. The results may be displayed on a graph showing cumulative production and / or savings in cost over time. Examples of this are shown in Figures 8 and 9.

[0049] The method 200 may also include modifying one or more of the first and / or second parameters based upon or in response to the comparison, as at 245. Figure 10 illustrates an image 1000 showing the first and / or second parameters and / or scenarios after the parameter(s) have been modified, according to an embodiment. The values of the first and / or second parameters may be modified to reduce cost, reduce emissions, and / or increase production. The user may be able to see the status of the progress achieved by the optimization.

[0050] In an embodiment, modifying may include generating or transmitting a signal that recommends, instructs, or causes a physical action to occur to modify the one or more of the first and / or second parameters. In another embodiment, modifying may include performing the physical action to modify the one or more of the first and / or second parameters.

[0051] Thus, the method 200 is configured to evaluate multiple sets of operating parameters for a power system. The method 200 applies established electrical, mechanical, and control -system relationships that represent generator behavior and / or battery behavior. Generator behavior includes fuel-consumption characteristics, ramp-rate limits, allowable loading ranges, and generator start and stop actions that depend on thresholds and timing. Battery behavior includes allowed charge power, allowed discharge power, round-trip efficiency, thermal loading, and stored energy represented as State of Charge (SoC).PATENT Atorney Docket No.: IS24.1836-WO

[0052] The method 200 receives high-resolution power data and processes the data to generate categorized operating conditions. The method 200 resamples and validates the received data, fdls missing values where appropriate, and identifies operating conditions including High Power Steady State (HPSS), Medium Power Steady State (MPSS), Low Power Steady State (LPSS), and High Power Transient (HPT). These categories reflect periods of sustained load, moderate fluctuations, low-load operation, and rapid short-duration load changes.

[0053] The method 200 evaluates a plurality of candidate operating-parameter sets using the same categorized data. The candidate sets may include generator start thresholds, generator stop thresholds, minimum generator run-time timers, minimum offline timers, allowable loading limits, battery charge-power limits, battery discharge-power limits, SoC limits, battery-assist thresholds, allowable generator overload duration, allowable voltage deviation, allowable frequency deviation, and a number of generators permitted online. For each candidate set, the method 200 calculates performance metrics that may include fuel usage, generator operating hours, minimum and maximum SoC, battery thermal loading, overload durations, and any violations of allowable limits.

[0054] The method 200 removes candidate sets that do not satisfy operational or safety constraints and identifies a candidate set that provides improved performance according to selected criteria. By evaluating many candidate sets across identical operating conditions, the method 200 enables optimized parameter selection that extends the capabilities of fixed LDSS thresholds or static rule-based control logic.Example 1. Project-Design Optimization of Battery Size and C Rate

[0055] The method 200 may evaluate several battery configurations that differ in energy capacity measured in kilowatt hours, charge and discharge capability measured in C rate, and maximum instantaneous power output. By applying HPSS, MPSS, LPSS, and HPT categories, the method 200 determines how each configuration would perform under representative loading conditions. The evaluation considers generator run-time, loading margins, minimum run-time timers, minimum offline timers, battery thermal behavior, SoC confidence margins, and expected overload exposure. For example, the method 200 may determine that a larger battery with a higher C rate provides sufficient support during HPT intervals to reduce generator starts and ramp-rate excursions. In another embodiment, a smaller battery may provide adequate performance duringPATENT Atorney Docket No.: IS24.1836-WOHPSS and MPSS operation at lower cost. The method 200, therefore, supports selection of a cost- effective battery configuration based on quantified tradeoffs.Example 2, Operational Optimization During Stable Conditions

[0056] During operation, the method 200 classifies power measurements into HPSS, MPSS, LPSS, or HPT. When extended LPSS or MPSS conditions are detected, the method 200 evaluates whether fewer generators can be used without violating operational constraints. The evaluation may consider generator start and stop thresholds, required minimum run-time and minimum offline periods, allowable loading ranges, battery charge limits, permissible SoC ranges, and voltage and frequency requirements. If the method 200 determines that one fewer generator can be operated while maintaining SoC, thermal margins, and power-quality limits, it may recommend transitioning a generator to standby. This adjustment reduces fuel usage and generator run-time during stable load conditions.Example 3, Operational Optimization During Transient Events

[0057] When repeated HPT periods are detected, the method 200 may evaluate whether the battery can provide additional support during transient load spikes. The evaluation may consider higher battery discharge-power limits, modified SoC thresholds for transient assistance, limits on generator overload duration, and adjusted response thresholds that determine when battery support is initiated. If the method 200 determines that increased battery support reduces ramp-rate stress, avoids LDSS-triggered generator starts, improves compliance with voltage and frequency limits, or reduces overload exposure while maintaining acceptable SoC and thermal margins, it may recommend updated operating parameters. These adjustments improve transient performance and reduce mechanical and thermal stress on the system.

[0058] Figure 11 illustrates a flowchart of a portion of the method 200, according to an embodiment. More particularly, the flowchart shows an example workflow for evaluating and selecting operating parameters. The workflow begins by starting an optimization run and loading initial setup parameters. High-resolution power data is loaded, resampled, validated, and analyzed to identify power-profile categories including HPSS, MPSS, LPSS, and HPT. The workflow generates a grid of candidate parameter combinations and applies each candidate set to the categorized data using the same digital-twin logic. Performance metrics are aggregated for dailyPATENT Atorney Docket No.: IS24.1836-WO intervals and for each power-profile category. Safety filters are applied including minimum SoC limits, maximum allowable battery thermal loading, and limits on generator power-limit duration. The remaining candidate sets are ranked according to fuel-consumption performance or other selected criteria, and a best-performing set is selected. The workflow then generates an updated configuration that reflects the recommended operating parameters.Exemplary Computing System

[0059] 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. The computer system 1201A includes one or more analysis modules 1202 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 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 1207 to allow the computer system 1201A to communicate over a data network 1209 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 1201 A, and may be located in different physical locations, e.g., computer systems 1201A 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).

[0060] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0061] 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 system 1201A, in some embodiments, storage media 1206 may be distributed within and / or across multiple internal and / or external enclosures ofPATENT Atorney Docket No.: IS24.1836-WO computing system 1201 A and / or additional computing systems. Storage media 1206 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), B LURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0062] In some embodiments, computing system 1200 contains one or more method execution module(s) 1208. In the example of computing system 1200, computer system 1201A includes the method execution module 1208. 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.

[0063] 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 hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0064] 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 otherPATENT Atorney Docket No.: IS24.1836-WO appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0065] 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 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.

[0066] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

PATENT Atorney Docket No.: IS24.1836-WOCLAIMSWhat is claimed is:

1. A method for controlling parameters for drilling equipment, the method comprising: receiving input data related to drilling equipment; creating a plurality of scenarios based upon the input data, wherein each scenario comprises a plurality of first parameters, and wherein creating the scenarios comprises selecting first values for each of the first parameters; selecting one or more of the scenarios and one or more second parameters; and simulating the one or more selected scenarios to determine second values for the one or more selected second parameters.

2. The method of claim 1, wherein the input data is related to a health, a performance, and / or an ambient condition of the drilling equipment.

3. The method of claim 1, wherein the drilling equipment comprises one or more generators.

4. The method of claim 3, wherein the first parameters comprise a rig contractor, a name of a drilling rig where the drilling equipment is located, a technology installed on the drilling rig, a time zone where the drilling rig is located, a number of one or more engines on the drilling rig, a determination whether fuel is available to the drilling rig, a determination whether the one or more engines are enabled, a determination whether battery energy storage system (BESS) power is available, a number of the one or more engines running, a power generated by a first of the one or more generators, a power generated by a second of the one or more generators, a date and time, or a combination thereof.

5. The method of claim 1, further comprising determining an availability and a quality of the input data for the scenarios, wherein the one or more selected scenarios are simulated after confirming that the availability and the quality are greater than or equal to a threshold.PATENT Atorney Docket No.: IS24.1836-WO6. The method of claim 5, wherein the availability and the quality are determined when the scenarios are battery energy storage system (BESS)-enabled and / or load dependent start and stop (LDSS)-enabled.

7. The method of claim 5, wherein the availability and the quality are determined by determining that the input data is monotonic, determining that the first values are not repeated, and determining that the input data is complete within a predetermined range.

8. The method of claim 1, wherein the one or more second parameters comprise total energy, total fuel, economy, fuel saving, emission saving, genset saving, power overload, a percentage of time that each of one or more gensets is running, a price of the fuel, an offset cost for carbon dioxide (CO2), a cost to run each of one or more engines per hour, or a combination thereof.

9. The method of claim 1, further comprising comparing the second values of the one or more selected second parameters in the one or more simulated scenarios by displaying the second values of the one or more selected second parameters in the one or more simulated scenarios side-by-side.

10. The method of claim 9, further comprising performing a physical action to modify one or more of the first and / or second parameters based upon or in response to the comparison.

11. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: receiving input data related to drilling equipment, wherein the input data is related to a health, a performance, and / or an ambient condition of the drilling equipment, and wherein the drilling equipment comprises one or more generators; creating a plurality of scenarios based upon the input data, wherein each scenario comprises a plurality of first parameters, and wherein creating the scenarios comprises selecting first values for each of the first parameters;PATENT Atorney Docket No.: IS24.1836-WO determining an availability and a quality of the input data for the scenarios, wherein the availability and the quality are determined when the scenarios are BESS-enabled and load dependent start and stop (LDSS)-enabled, wherein the availability and the quality are determined by determining that the input data is monotonic, determining that the first values are not repeated, and / or determining that the input data is complete within a predetermined range; selecting one or more of the scenarios and one or more second parameters, wherein the one or more second parameters comprise total energy, total fuel, economy, fuel saving, emission saving, genset saving, power overload, a percentage of time that each of one or more gensets is running, a price of the fuel, an offset cost for carbon dioxide (CO2), a cost to run each of one or more engines per hour, or a combination thereof; and simulating the one or more selected scenarios to determine second values for the one or more selected second parameters.

12. The computing system of claim 11, wherein the input data also comprises data related to the one or more engines, the one or more gensets which include a combination of one of the generators and one of the engines, one or more batteries, a top drive, a drawworks, a mud pump, or a combination thereof, and wherein the input data also comprises a number of the one or more generators, an electrical current generated by the one or more generators, a voltage generated by the one or more generators, a temperature of the one or more generators, a total power used by the top drive, the drawworks, and / or the mud pump, or a combination thereof.

13. The computing system of claim 12, wherein the first parameters comprise a rig contractor, a name of a drilling rig where the drilling equipment is located, a technology installed on the drilling rig, a time zone where the drilling rig is located, a number of the one or more engines on the drilling rig, a determination whether fuel is available on the drilling rig, a determination whether the one or more engines are enabled, a determination whether battery energy storage system (BESS) power is available, a number of the one or more engines running, a power generated by a first of the one or more generators, a power generated by a second of the one or more generators, a date and time, or a combination thereof.PATENT Atorney Docket No.: IS24.1836-WO14. The computing system of claim 1 1, wherein the availability and the quality of the input data are displayed in a single row or column with a first color or shading indicating time periods where the input data is available with the quality above a threshold, a second color or shading indicating time periods where the input data is available with the quality below the threshold, and / or a third color or shading indicating time periods where the input data is unavailable.

15. The computing system of claim 14, wherein the one or more selected scenarios are simulated in response to determining that more than a predetermined amount of the input data is available with the quality above the threshold, and wherein the one or more selected scenarios are simulated using an artificial intelligence (Al) machine learning (ML) model.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: receiving input data related to drilling equipment, wherein the input data is related to a health, a performance, and an ambient condition of the drilling equipment, wherein the drilling equipment comprises one or more generators, one or more engines, one or more gensets including a combination of one of the generators and one of the engines, one or more batteries, a top drive, a drawworks, and a mud pump, and wherein the input data also comprises a number of the one or more generators, an electrical current generated by the one or more generators, a voltage generated by the one or more generators, a temperature of the one or more generators, and a total power used by the top drive, the drawworks, and the mud pump; creating a plurality of scenarios based upon the input data, wherein each scenario comprises a plurality of first parameters, wherein creating the scenarios comprises selecting first values for each of the first parameters, and wherein the first parameters comprise a rig contractor, a name of a drilling rig where the drilling equipment is located, a technology installed on the drilling rig, a time zone where the drilling rig is located, a number of the one or more engines on the drilling rig, a determination whether fuel is available, a determination whether the one or more engines are enabled, a determination whether battery energy storage system (BESS) power is available, a number of the one or more engines running, a power generated by a first of the one or more generators, a power generated by a second of the one or more generators, and a date and time;PATENT Atorney Docket No.: IS24.1836-WO determining an availability and a quality of the input data for the scenarios, wherein the availability and the quality are determined when the scenarios are BESS-enabled and load dependent start and stop (LDSS)-enabled, wherein the availability and the quality are determined by determining that the input data is monotonic, determining that the first values are not repeated, and determining that the input data is complete within a predetermined range, wherein the availability and the quality of the input data are displayed in a single row or column with a first color or shading indicating time periods where the input data is available with the quality above a threshold, a second color or shading indicating time periods where the input data is available with the quality below the threshold, and a third color or shading indicating time periods where the input data is unavailable; selecting one or more of the scenarios and one or more second parameters, wherein the one or more second parameters comprise total energy, total fuel, economy, fuel saving, emission saving, genset saving, power overload, a percentage of time that each genset is running, a price of the fuel, an offset cost for carbon dioxide (CO2), and a cost to run each of the engines per hour; and simulating the one or more selected scenarios to determine second values for the one or more selected second parameters, wherein the one or more selected scenarios are simulated in response to determining that more than a predetermined amount of the input data is available with the quality above the threshold, wherein the one or more selected scenarios are simulated using an artificial intelligence (Al) machine learning (ML) model.

17. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise comparing the second values of the one or more selected second parameters in the one or more simulated scenarios.

18. The non-transitory computer-readable medium of claim 17, wherein the operations further comprise displaying results of the comparison, wherein the results are displayed on a graph showing cumulative production and / or savings in cost over time.

19. The non-transitory computer-readable medium of claim 17, wherein the operations further comprise modifying the one or more first and / or second parameters based upon the comparison.PATENT Atorney Docket No.: IS24.1836-WO20. The non-transitory computer-readable medium of claim 19, wherein modifying comprises generating and transmitting a signal that recommends, instructs, or causes a physical action to occur to modify the one or more first and / or second parameters.

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