A method may include receiving a description of an event occurring at a wellsite; extracting a failure mode from the description using a fine-tuned large language model (LLM); identifying a matching failure mode from historical data processed using the fine-tuned LLM, where the matching failure mode is associated with one or more remedial actions that successfully resolved the matching failure mode; and outputting the one or more remedial actions for implementation at the wellsite.
A method for steering a downhole tool to drill a wellbore in a subterranean formation includes receiving an initial wellbore plan for the downhole tool to drill through the subterranean formation. The method also includes receiving drilling data while the downhole tool is drilling through the subterranean formation using the initial wellbore plan. The method also includes comparing the initial wellbore plan to the drilling data. The method also includes determining a downlink command to transmit to the downhole tool based upon or in response to the comparison. The method also includes determining an importance of the downlink command based upon the comparison. The method also includes determining a time to transmit the downlink command to the downhole tool. The time is determined based upon the importance of the downlink command. The method also includes transmitting the downlink command to the downhole tool at the determined time.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
A method of preventing a collision of a subject wellbore in a downhole environment includes receiving offset wellbore data corresponding with one or more offset wellbores and identifying, based on the offset wellbore data, one or more no-go zones for each of the one or more offset wellbores. The method further includes determining a plurality of safe points corresponding with a potential intersection of the subject wellbore with the one or more no-go zones and, defining an escape zone within the plurality of safe points. The method further includes determining a trajectory for the subject wellbore within the escape zone.
A method for selecting potential offset drilling runs to automatically evaluate a drilling performance of a subject drilling run includes identifying the potential offset drilling runs based upon the subject drilling run. The potential offset drilling runs are identified based upon a status of a drilling system component that is used to drill the subject drilling run. The method also includes determining a score for each of the potential offset drilling runs, ranking the potential offset drilling runs based upon the score of each of the potential offset drilling runs, and identifying a subset of the potential offset drilling runs based upon the ranking. The method also includes performing a plurality of comparisons of the drilling performance of the subject drilling run against drilling performances of the subset and selecting one of a plurality of drilling system components used in the subset based at least partially upon the first comparisons.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
5.
USING DEEP-LEARNING MODELS TO AUTOMATICALLY IDENTIFY SUBSURFACE RESERVOIR BOUNDARIES IN REAL TIME
The disclosure focuses on using a boundary identification system to actively determine borders and boundaries in subsurface geological features, such as reservoirs. In various implementations, the boundary identification system uses an ensemble image model leveraging multiple image-to-image machine-learning models to efficiently and accurately generate reservoir boundaries from inversion result profiles and images. In many instances, the boundary identification system generates reservoir boundaries from inversion results in real-time. Additionally, in some instances, the boundary identification system further improves the accuracy of the ensemble image model by diversifying the inputs and using ensembling on the individual model outputs during inference.
G01V 3/26 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging operating with magnetic or electric fields produced or modified either by the surrounding earth formation or by the detecting device
A method can include receiving input for a field scenario that comprises fluid dynamics and chemical phenomena, where the input defines substances amenable to speciation; executing a fluid dynamics simulator to perform timesteps for transport of the substances while calling a chemical phenomena framework that determines speciation of the substances; and generating results for the field scenario based on the executing.
Disclosed are methods, systems, and computer programs that computationally select event data from captured seismic data. The methods comprise generating a learning model associated with a machine learning (ML) engine. The methods also comprise receiving seismic data that comprise a propagated wavefield transmitted within the subsurface of the resource site. The methods also comprise generating a data matrix using the received seismic data and resolving event data comprised in the received seismic data to generate a segmented wavefield. A geological model of the subsurface of the resource site may be generated using the segmented wavefield such that the geological model of the subsurface of the resource site indicates at least a multidimensional image of the subsurface of the resource site.
Certain aspects of the disclosure provide a method for solving an optimization problem using a genetic algorithm. The method generally includes generating chromosomes; performing iteratively until a termination condition is met; repairing one or more chromosomes that do not meet a first set of a plurality of constraints, determining a fitness score for each of the chromosomes based on at least one objective function and the plurality of constraints; identifying two or more parent chromosomes from the chromosomes using the fitness scores; generating child chromosome(s) from the parent chromosomes, wherein at least one child chromosome is mutated and does not meet a second set of the plurality of constraints; repairing the at least one child chromosome to comply with the second set of constraints; and replacing one or more of the chromosomes having lower fitness scores among the fitness scores determined for the chromosomes with the child chromosome(s).
The disclosed methods include: provisioning an artificial intelligence (AI) system; receiving, by a first data agent of the AI system and via a first LLM, natural language input; parsing, by the first data agent of the AI system, the natural language input and thereby generate query data for the AI system; generating, based on the query data, production analytics data for a resource site; selecting, based on one or more data elements in a database, at least one computing logic; determining, based on the production analytics data and the at least one computing logic, assessment data for fluid production optimization; and generating, based on the assessment data and using a second LLM of the one or more LLMs, a visualization on graphical display, the visualization comprising image or textual data indicating at least one data parameter for the fluid production optimization at the resource site.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06N 3/00 - Computing arrangements based on biological models
A method may include receiving imagery data from a wellsite that includes a catwalk system; analyzing the imagery data to detect movement with respect to the catwalk system; determining a risk to a human at the wellsite based on the detected movement; and, responsive to the determining, issuing an instruction to reduce the risk.
A method may include receiving imagery data from a wellsite; analyzing the imagery data for detection personal protective equipment; determining a risk to a human at the wellsite based on the analyzing; and, responsive to the determining, issuing an instruction to reduce the risk.
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
E21B 41/00 - Equipment or details not covered by groups
A method may include receiving imagery data from a wellsite; analyzing the imagery data to detect movement; determining a risk to a human at the wellsite based on the detected movement; and, responsive to the determining, issuing an instruction to reduce the risk.
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
A method for operating a facility, the method including receiving real-time facility data representing an operational state of the facility, determining that an area of the facility is restricted based on the real-time facility data and a set of facility access rules that associates the operational facility state with at least one restricted area of the facility, and receiving image data representing at least a portion of the restricted area of the facility. The set of facility access rules may be selectively adjusted, and a portion of the received image data may be marked as the restricted within a graphical interface that is displayed on a screen that is associated with a user. A determination may then be made if there is a violation of the set of facility access rules based on the received image data, allowing for a corrective action to then be taken.
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
14.
SYSTEMS AND METHODS FOR SMART PRODUCTION OPERATIONS
A method may include receiving a plurality of datasets from devices in a resource extraction site. The method may also involve identifying workflow systems associated with one or more operations of the devices. The method may also involve determining updated operational parameters for the devices based on the workflow systems and the plurality of datasets and generating one or more commands for implementing the one or more updated operational parameters for the one or more devices. The method may then include sending the commands to the devices, wherein the devices are configured to adjust the operations based on the updated operational parameters.
A method of identifying sidetrack zones in an underground wellbore includes receiving hole-casing data, and determining one or more candidate sidetrack zones based on identifying cement overlap areas from the hole-casing data. The method further includes receiving completion data for one or more completion elements and determining a completion depth for each of the one or more completion elements based on the completion data. The method further includes selecting one or more sidetrack zones by filtering the one or more candidate sidetrack zones based on one or more of the completion depths.
An Energy Data Platform (2400-1) may include one or more computing systems including a plurality of data type platforms comprising a Subsurface Platform (1301B), an Operations Platform (1301C), a Sustainability Platform (1301D), an Enterprise Platform (1301E), and a Generative Artificial Intelligence (GenAI) platform. The computing systems may also include an application management layer (1320) configured to receive a plurality of datasets from the plurality of data type platforms and a unification services layer (1310) configured to convert at least a portion the plurality of datasets acquired from a first data type platform of the plurality of data type platforms into a plurality of converted datasets. The plurality of converted datasets is accessible to a second data type platform of the plurality of data type platforms. The computing systems may also include a first network (1305) for accessing the Energy Data Platform.
Carbon emission auditing includes obtaining a supplier transaction record of an enterprise corresponding to a supplier entity from a transaction repository. A research response corresponding to the supplier entity is obtained from a large language model (LLM). A validity of the supplier transaction record based on the research response is further obtained from the LLM as a validation response. Field values of the record fields of the supplier transaction record are further verified by the LLM, and a resulting consistency response is generated. The LLM further determines an audit of the supplier transaction record based on the research response, the validation response and the consistency response. An explanation of the occurrence of an audit failure is generated by the LLM. The supplier transaction record is further modified, and a new scope emission category necessitated by the modification is assigned to the supplier transaction record.
Methods and apparatus to analyze emissions for industry operations include obtaining a definition of at least one of a first part, product, activity or usage, or service. Applicable emissions and activity data for the at least one of the first part, product, activity or usage, or service are identified. One or more differences between the at least one of the first part, product, activity or usage, or service and at least one of a second part, product, activity or usage, or service for which the applicable emissions data was collected are determined. Emissions for the at least one of the first part, product, activity or usage, or service are estimated by modifying the applicable emissions data based on the one or more differences, and the estimate emissions are presented.
A system for, and method of, drill deviation handling within a stand while drilling a wellbore are presented. The techniques include: receiving, by an electronic processor and during a stand, drill state data; comparing, by the electronic processor and during the stand, the drill state data to an active drill plan; detecting, by the electronic processor and based on the comparing, an out-of-tolerance deviation of a drill parameter; and providing, by the electronic processor, an alert of the out-of-tolerance deviation.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 47/024 - Determining slope or direction of devices in the borehole
20.
SIMULATION-ASSISTED, MACHINE-LEARNING SOLUTION TO PROVIDE OILFIELD SUSTAINABILITY
A method for predicting a likelihood that a physical phenomenon will occur in an area of interest at a wellsite includes receiving input parameters for a well in the area of interest. The method also includes generating or updating a geomodel based upon the input parameters. The geomodel includes a first model or a second model. The method also includes predicting a pressure result using the geomodel. The pressure result is based upon the input parameters. The method also includes predicting the likelihood that the physical phenomenon will occur in the future in the area of interest based upon the pressure results. The likelihood that the physical phenomenon will occur is predicted using a third model that is different than the first and second models.
According to an embodiment, a method for computationally generating resolved data associated with a subsurface of a resource site includes: receiving seismic data from one or more sensors deployed at the resource site; generating a velocity model using the seismic data; formatting the velocity model into a data matrix; executing a minimization computing operation on the data matrix to decompose the velocity model into background velocity data and perturbation data and thereby generate a decomposed dataset; attenuating the perturbation data comprised in the decomposed dataset to generate resolved data; generating, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and executing an energy development operation associated with the resource site based on the report. The subsurface, according to one embodiment, comprises a region in the subsurface of the resource site through which the propagated seismic wavefield travels prior to being received.
A method for calibrating a model of a subterranean formation includes capturing one or more measurements at a surface of a wellbore that extends into a subterranean formation. The measurements include a surface torque (STOR) on a drill string that extends into the wellbore and a surface weight (SWOB) on the drill string. The method also includes determining a friction factor based upon the STOR when a drill bit is off-bottom in the wellbore. The drill bit is coupled to a lower end of the drill string. The method also includes determining a downhole torque on the drill bit (DTOR) and a downhole weight on the drill bit (DWOB) when the drill bit is on-bottom in the wellbore. The method also includes identifying a rock type in the subterranean formation based at least partially upon the friction factor, the DTOR, and the DWOB.
A ML validation and verification system validates an ML model using cross validation, consistency check, and physical modeling. Cross validation includes splitting a set of original rock files into a training subset and a validation subset, and generating a synthetic rock file to be validated on the validation subset. Consistency check includes generating multiple synthetic rock files using different realization of the ML model and comparing them for consistency. The validation engine generates a synthetic physical simulation and an original physical simulation using the physical model and compares the two simulations.
A system and method that may include receiving real-time downhole data from one or more sensors of a drillstring disposed in a borehole in a subsurface geologic region during a directional drilling operation. The system and method also include selecting a drillstring drilling mode from a plurality of drillstring drilling modes. The system and method may additionally include predicting, in real-time, characteristics of a hole bottom of the borehole using the drilling mode model and at least a portion of the real-time downhole data. The system and method may further include controlling the directional drilling operation using one or more of the characteristics.
The disclosed methods include recording pulse data indicating laser pulse(s) transmitted into or out of one or more fibers within a fiber cable to generate a first fiber record. The method determines whether at least one of the one or more fibers is spliced following which a trace in the first fiber record corresponding to a splicing point within the fiber cable is identified. The fiber record may be split into an inbound section and an outbound section such that an outbound trace numbering of the outbound section is mirrored to align with a trace numbering of the inbound section to generate mirrored data. Geometry data may be assigned to the mirrored data to generate a geometrically formatted fiber record. Signal components within the geometrically formatted fiber record may be reconstructed and used in a combination process to generate a report indicating captured measurements along the fiber cable.
G01V 1/22 - Transmitting seismic signals to recording or processing apparatus
E21B 47/0228 - Determining slope or direction of the borehole, e.g. using geomagnetism using electromagnetic energy or detectors therefor
G01H 9/00 - Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
G01V 1/40 - SeismologySeismic or acoustic prospecting or detecting specially adapted for well-logging
G01V 8/20 - Detecting, e.g. by using light barriers using multiple transmitters or receivers
H04B 1/00 - Details of transmission systems, not covered by a single one of groups Details of transmission systems not characterised by the medium used for transmission
H04B 1/10 - Means associated with receiver for limiting or suppressing noise or interference
H04B 10/2507 - Arrangements specific to fibre transmission for the reduction or elimination of distortion or dispersion
A device includes a memory configured to store first executable code and a processor coupled to the memory. The processor is configured to calculate performance indicators for a sucker rod pump (SRP) based on performance data of the SRP, determine an operational frequency corresponding to operation of the SRP based on one performance indicator selected from the performance indicators, and initiate transmission of a control signal corresponding to the operational frequency to alter operation of the SRP to correspond to the operational frequency.
E21B 43/12 - Methods or apparatus for controlling the flow of the obtained fluid to or in wells
E21B 47/009 - Monitoring of walking-beam pump systems
F04B 47/02 - Pumps or pumping installations specially adapted for raising fluids from great depths, e.g. well pumps the driving mechanisms being situated at ground level
27.
ARTIFICIAL INTELLIGENCE (AI)-POWERED AUTOMATION OF FEASIBILITY MODELING FOR GEOPHYSICAL MONITORING TOOLS
Certain aspects of the disclosure provide a method of feasibility modeling for a plurality of geophysical monitoring tools. The method generally includes processing, using a flow simulation model configured to simulate carbon dioxide flow at a carbon capture and storage (CCS) site, model input parameters associated with the CCS site to generate flow simulation output data; extracting the flow simulation output data; processing the flow simulation output data to simulate a use of geophysical monitoring tools at the CCS site; for each geophysical monitoring tool: simulating a monitoring response; and determining a uncertainty level associated with the generated monitoring response; determining, using a Bayesian framework, a feasibility of implementing one or more of the plurality of geophysical monitoring tools at the CCS site based on the monitoring response and the uncertainty level associated with each of the geophysical monitoring tools and thereby generate a feasibility score for each tool.
A method including receiving, from a user device operated by a user, a request to access a pool of virtual machines. The virtual machines include a first subset of virtual machines and a second subset of virtual machines. The first subset of virtual machines are at a first provisioning level. The second subset of virtual machines includes a second provisioning level that is less than the first provisioning level. The request specifies a provisioning level request. The method also includes receiving a user profile associated with the user. The method also includes assigning, to the user, a selected virtual machine. The selected virtual machine is selected from among the first subset of virtual machines and the second subset of virtual machines based on the user profile and further based on the provisioning level request. The method also includes providing, to the user device, access to the selected virtual machine.
A method of operating a downhole system includes receiving trajectory data including a trajectory for steering a downhole tool toward a downhole target. The method includes identifying downhole tool data for the downhole tool. The method includes, based on the trajectory data and the downhole tool data, predicting one or more engineering metrics including one or more downhole tool metrics associated with an operation of the downhole tool in accordance with the trajectory and one or more completion metrics associated with a completion of the borehole at the downhole target. The method includes determining a coherency for the trajectory including determining whether the engineering metrics are within one or more predetermined thresholds. The method includes generating a report of at least some of the engineering metrics including a value of each engineering metric and an indication of whether the value is within the predetermined thresholds.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
The present disclosure describes a method may include receiving input data comprising geological data, an indication of a set of components to be placed in a layout for the site, and an emission cost estimate for each of the set of components. The method may also include defining uncertainty parameters and generating a plurality of planning scenarios to implement based on the components and uncertainty parameters. Additionally, the method may include determining facility placements, well trajectories, pipeline placements, and a net present value for each of the planning scenarios. Further, the method may include calculating a tax credit for each of the planning scenarios, ranking each of the planning scenarios based on a respective net present value and a respective tax credit to generate a ranked list of the plurality of planning scenarios, and generating a visualization comprising the ranked list of the planning scenarios.
E21B 43/00 - Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
E21B 43/30 - Specific pattern of wells, e.g. optimising the spacing of wells
E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
31.
METHOD OF CUSTOMER SENTIMENT ANALYSIS USING LOGS AND FEEDBACK
A method implements customer sentiment analysis using logs and feedback. Log data is received. The log data is processed with a text generation model to generate synthesized text. The synthesized text is processed with a sentiment prediction model to generate a sentiment prediction. The sentiment prediction model is trained with a training label received responsive to a similarity score of a training vector meeting a similarity threshold. The sentiment prediction is presented.
G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
G06F 18/241 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
A method can include determining carbon dioxide acoustic properties for at least supercritical carbon dioxide using thermodynamics that relate isothermal compressibility and adiabatic compressibility; determining fluid-saturated rock acoustic properties using the carbon dioxide acoustic properties; and performing a seismic workflow using the fluid-saturated rock acoustic properties.
E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
E21B 43/26 - Methods for stimulating production by forming crevices or fractures
Methods and systems for analyzing seismic data to generate multidimensional reports are disclosed. The methods include receiving, by a data engine, seismic data captured at a resource site. The data engine may constrain a machine learning (ML) engine configured for analyzing the captured seismic data. According to one embodiment, the ML engine may be constrained using boundary condition data associated with one or more subsurface geo-properties comprised in, or associated with the seismic data. For example, the boundary condition data may indicate one or more limit data, threshold data, or some other statistical parameter used to impose bounds on the analysis executed by the ML engine. The seismic data may then be analyzed based on the constrained ML engine to generate a multidimensional report. According to one embodiment, the multidimensional report comprises 2-dimensional image data and/or 3-dimensional image data associated with a subsurface of the resource site.
Methods, systems, and computing systems for operating an electrolyzer include obtaining a ratio of diffusivity in a material at a reference temperature, simulating operation of the electrolyzer in the material at a plurality of current density values at an operating temperature that is different from the reference temperature based at least in part on the ratio of diffusivity, and displaying a result comprising data representing the operation of the electrolyzer using a computer monitor.
A method can include receiving log data for different types of logs; identifying a portion of the log data that corresponds to a type of formation; defining combinations of the portion of the log data that correspond to the type of formation; implementing a machine learning model that generates scores for the combinations, where each of the scores indicates an ability of each of the combinations to predict one or more target logs therein as selected from the different types of logs; and outputting, based on a ranking of the scores, at least a top ranked one of the combinations that corresponds to the type of formation.
An enterprise system may include devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain a sustainability model representative of a state of operations of the enterprise and a current sustainability action plan associated with improving one or more sustainability parameters of the enterprise via one or more abatement technologies. The sustainability platform system may also determine that updated data is available for the abatement technologies and simulate an effect of the action plan on the sustainability parameters based on the updated data. The sustainability platform system may also determine whether the simulated effect of the action plan is effective to cause the sustainability parameters to be within one or more thresholds, and in response to determining that the simulated effect is effective, send commands to the devices to maintain their respective operations according to the current sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain a sustainability model representative of a state of operations of the enterprise and a current sustainability action plan associated with improving one or more sustainability parameters of the enterprise. The sustainability platform system may also receive updated carbon credit data, simulate an effect of the current sustainability action plan on the sustainability parameters based on the updated carbon credit data (generating simulated sustainability parameters), and determine whether the current sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data. In response to determining that the current sustainability action plan is effective, the sustainability platform system may send commands to the devices to maintain their operations according to the current sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
38.
UPDATING SUSTAINABILITY ACTION PLANS FOR AN ENTERPRISE BASED ON DETECTED CHANGE IN INPUT DATA
A method includes receiving datasets comprising image data, marketplace data, third-party data, internet-of-things (IoT) data, corporate data, or any combination thereof associated with enterprise operations corresponding to production data for operational tasks performed in a hydrocarbon production system, facility data for utility operations within buildings associated with the enterprise, or both. The method involves detecting a change in sustainability parameter data associated with the enterprise operations based on the datasets and a sustainability model representative of current sustainability parameters associated with the enterprise operations, updating the sustainability model based on the datasets, and sending the updated sustainability model to engineering workflow systems to determine action plans associated with improving the sustainability parameters or the additional sustainability parameters. The method may then involve sending commands to devices associated with the hydrocarbon production system, the buildings, or both based on the action plans to cause the devices to adjust their respective operations.
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain input data from one or more input data sources, and determine a respective authority level for each of the input data sources. The sustainability platform system may also determine uncertainty data associated with the input data based on the respective authority level for each of the input data sources, determine confidence parameters associated with the input data based on the uncertainty data, generate one or more sustainability action plans for improving the sustainability parameters of the enterprise based on the input data stored in the database, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system that searches input data sources for abatement technologies that, when implemented, improve one or more sustainability parameters of the enterprise. The sustainability platform system may determine respective technology parameters that correlate a respective abatement technology with a respective set of the sustainability parameters that the respective abatement technology is configured to improve and one or more deployment aspects of the respective abatement technology and store the abatement technologies and respective technology parameters in a database. Additionally, the sustainability platform system may generate a sustainability action plan that implements at least one abatement technology based on a match between a deployment aspect of the enterprise and the technology parameters of the abatement technology and send commands to the devices to adjust their respective operations according to the sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system and facility data includes utility operations within buildings associated with the enterprise. The method may involve receiving sustainability parameter data associated with corresponding to the enterprise based on the production data and the facility data. The method may include generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, sending the sustainability report to engineering workflow systems that determine action plans associated with improving the sustainability parameters associated with the one or more utility operations, and sending commands to devices associated with the buildings based on the action plans, such that the commands cause the plurality of devices to adjust their respective operations.
A method includes receiving datasets comprising image data, marketplace data, third-party data, internet-of-things (IoT) data, corporate data, or any combination thereof associated with enterprise operations corresponding to production data performed in a hydrocarbon production system, facility data associated with utility operations within buildings associated with the enterprise, or both. The method involves detecting a change in sustainability parameter data associated with the enterprise operations based on the datasets and a sustainability model, updating the sustainability model based on the datasets, sending the updated sustainability model to engineering workflow systems to determine action plans associated with improving the sustainability parameters, the additional sustainability parameters, or both. The method may involve sending commands to devices associated with the hydrocarbon production system, the buildings, or both based on the action plans, such that the commands cause the devices to adjust their operations.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system and wherein the facility data includes utility operations within buildings associated with the enterprise. The method may involve receiving sustainability parameter data associated with operations corresponding to the enterprise based on the production data and the facility data, generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, and sending the sustainability report to one or more engineering workflow systems to determine action plans associated with improving a portion of the sustainability parameters associated with the operational tasks. The method may then send commands to devices associated with the hydrocarbon production system based on the action plans, such that the commands cause the devices to adjust their respective operations.
A method includes receiving sustainability datasets associated with enterprise operations of an enterprise, such that the enterprise operations correspond to production data performed in a hydrocarbon production system, facility data related to utility operations within buildings associated with the enterprise, or both. The method may involve determining that one or more sustainability alerts are present for the enterprise operations based on the sustainability datasets; and identifying assets of the hydrocarbon production system, the one or more buildings, or both associated with the one or more sustainability alerts in response to determining that the one or more sustainability alerts are present. The method may include retrieving asset data associated with the assets, sending the asset data to engineering workflow systems, receiving updated action plans from the engineering workflow systems, and sending commands to devices to adjust operations based on the updated action plans.
A method may include receiving a sustainability model indicative of expected sustainability parameters associated with implementing action plans that correspond to enterprise operations of an enterprise over a period of time, such that the enterprise operations correspond to production data performed in a hydrocarbon production system, facility data corresponding to buildings associated with the enterprise, or both. The method may also include receiving an indication to optimize one sustainability parameter and identifying an engineering workflow system that improves a first sustainability parameter associated with the one sustainability parameter. The method may involve sending the sustainability model to the engineering workflow system and receiving an action plan to improve the sustainability parameter, such that commands may be sent to devices based on the action plan to cause the one or more devices to adjust one or more respective operations.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method may include receiving a sustainability model indicative of a plurality of sustainability parameters associated with enterprise operations corresponding to production data for operational tasks performed in a hydrocarbon production system, facility data corresponding to utility operations within buildings associated with the enterprise, or both. The method involves simulating implementing action plans via devices that correspond to the enterprise operations over time to determine an amount of greenhouse gas (GHG) emissions associated with the enterprise over time, generating a GHG footprint evolution report based on the amount of GHG emissions, and identifying at least one of a plurality of engineering workflow systems to reduce the amount of GHG emissions based on the GHG footprint evolution report. The method involves sending commands to devices of the plurality of devices based on the action plan, such that the commands are cause the devices to adjust their operations.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method may include receiving a sustainability model indicative of a plurality of sustainability parameters associated with enterprise operations corresponding to production data of a hydrocarbon production system or facility data of buildings associated with the enterprise. The method includes simulating implementing action plans via a plurality of devices that correspond to the enterprise operations over time to determine an amount of greenhouse gas (GHG) emissions associated with the enterprise, identifying at least one of a plurality of engineering workflow systems to reduce the amount of GHG emissions based on the GHG emissions, such that the plurality of engineering workflow systems determines maintenance or replacement operations for equipment in the enterprise. The method includes sending commands to devices of the devices based on the action plan, such that the commands are cause the devices to go offline at a schedule time period to perform maintenance or replacement of the devices.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/20 - Administration of product repair or maintenance
G06Q 10/30 - Administration of product recycling or disposal
48.
UPDATING SUSTAINABILITY ACTION PLANS BASED ON UPDATED REGULATIONS
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain a sustainability model representative of a state of operations of the enterprise and a currently implemented sustainability action plan associated with improving one or more sustainability parameters of the enterprise. The sustainability platform system may also receive updated regulation data, simulate an effect of the currently implemented sustainability action plan on the sustainability parameters based on the updated regulation data, generating simulated sustainability parameters, and determine whether the currently implemented sustainability action plan is effective based on a comparison of the simulated sustainability parameters to sustainability target data. In response to determining that the currently implemented sustainability action plan is effective, the sustainability platform system may send commands to the devices to maintain their operations according to the currently implemented sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system to determine a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of the enterprise. Additionally, the sustainability platform system may determine if changes to the input data are likely to have occurred based on the sentiment, and, if so, trigger a data search for the new input data. The sustainability platform system may also obtain the new input data via the input data sources based on the data search, generate one or more sustainability action plans for improving sustainability parameters of the enterprise based on the new input data, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
50.
GENERATING AND MAINTAINING A SUSTAINABILITY DATABASE FOR DETERMINING AND UPDATING SUSTAINABILITY ACTION PLANS
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain search parameters associated with one or more sustainability parameters of the enterprise, obtain input data from one or more input data sources, and determine confidence parameters for the input data based on the input data sources. The sustainability platform system may also store the input data and the confidence parameters in a database of the sustainability platform system, generate one or more sustainability action plans for improving the sustainability parameters of the enterprise based on the input data stored in the database, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system, and the facility data includes one or more utility operations within one or more buildings associated with the enterprise. The method may also include receiving sustainability parameter data associated with one or more operations corresponding to the enterprise based on the production data and the facility data, generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, and sending the sustainability report to one or more engineering workflow systems to determine action plans associated with improving the sustainability parameters. After receiving the action plans, the method includes sending commands to cause devices to adjust their respective operations.
An enterprise system may include one or more devices with sensors that measure operational parameters of the devices. The enterprise system may also include a sustainability platform system that obtains a sustainability model representative of a state of operations of the enterprise based on the measured operational parameters and receives sustainability target data that includes one or more threshold limits, one or more ranges, or both for one or more sustainability parameters. The sustainability platform system may also obtain one or more action plans for adjusting respective operations of the devices based on the sustainability model and the sustainability target data, simulate a performance of the action plans over a period of time relative to the sustainability parameters, and determine whether the simulated performance of the action plans is effective.
An enterprise system may include one or more facility level devices of an enterprise and a sustainability platform system that obtains a sustainability action plan associated with improving one or more sustainability parameters of the enterprise and simulates missing data relevant for measuring an effectiveness of the sustainability action plan and unavailable for use by the sustainability platform system to generate simulated sustainability data. Additionally, the sustainability platform system may determine whether the sustainability action plan is effective to cause the sustainability parameters to be within one or more target thresholds, one or more target ranges, or both based on the simulated sustainability data and in response to determining that the sustainability action plan is effective, send one or more commands to the facility level devices to cause the facility level devices to adjust one or more respective operations according to the sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain a sustainability model representative of a state of operations of the enterprise and a sustainability action plan associated with improving one or more sustainability parameters of the enterprise. The sustainability platform system may also determine confidence data associated with a likelihood that the sustainability action plan will improve the sustainability parameters, simulate an effect of the sustainability action plan on the sustainability parameters based on the sustainability model and the confidence data, and determine a selection of the sustainability action plan or an alternate sustainability action plan based on the simulated sustainability parameters. In response to selection of the sustainability action plan, the sustainability platform system may send commands to the devices for adjusting their respective operations according to the sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/30 - Administration of product recycling or disposal
55.
IDENTIFYING ABATEMENT TECHNOLOGIES FOR IMPLEMENTATION IN SUSTAINABILITY ACTION PLANS
An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain a sustainability model representative of a state of operations of the enterprise and sustainability target data of target constraints on one or more sustainability parameters of the enterprise. Additionally, the sustainability platform system may identify one or more abatement technologies from a database of multiple abatement technologies based on the sustainability model and the sustainability target data and generate a sustainability action plan for implementing at least one abatement technology of the identified abatement technologies such that the at least one abatement technology, when implemented via the sustainability action plan, is estimated to satisfy the sustainability target data. The sustainability platform system may also send commands to the devices to adjust their respective operations according to the sustainability action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/30 - Administration of product recycling or disposal
56.
EVALUATING ACTION PLANS FOR OPTIMIZING SUSTAINABILITY FACTORS OF AN ENTERPRISE
A method may include receiving a sustainability model indicative of a plurality of expected sustainability parameters associated with enterprise operations of an enterprise over a period of time. The method includes receiving a plurality of action plans for the enterprise, such that the plurality of action plans corresponds to operational characteristics of a plurality of devices that correspond to the one or more enterprise operations. The method receives sustainability variables associated with sustainability parameters for the enterprise operations, simulating a plurality of sustainability variables associated with performing the plurality of action plans over time with respect to the sustainability variables, determining a plurality of effectiveness values associated with performing the plurality of action plans relative to the sustainability variables, presenting the plurality of action plans with the one or more effectiveness values. The method includes sending commands to devices of the plurality of devices based on a selected action plan.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The disclosed technology is directed to methods and systems for optimizing gas storage (GS) operations. The methods comprise generating a GS model associated with a resource site such that the GS model comprises one or more parameters; determining risk thresholds for the GS operations based on risk profile data associated with the resource site; parameterizing, based on the risk thresholds, the one or more parameters; generating, using the parameterized GS model, a simulation plan for the GS operations at the resource site; executing the simulation plan across: multiple simulators in parallel, a defined uncertainty space derived from uncertainty data, multiple time periods, and the plurality of geological realizations. In some embodiments, the methods include aggregating analysis data generated from executing the simulation plan. The analysis data may indicate: gas concentration data; gas leakage data; and configuration data associated with configuring a monitoring system at the resource site.
G01V 1/28 - Processing seismic data, e.g. for interpretation or for event detection
G01V 3/08 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
A method of generating a three-dimensional (3D) seismic image volume includes receiving a plurality of two-dimensional (2D) seismic images. The method also includes generating a proxy volume representative of a three-dimensional volume that corresponds to the 3D seismic image volume based on the plurality of 2D seismic images, the proxy volume including multiple approximated 2D seismic images. The method also includes generating an approximate image volume including a first plurality of seismic images along a first trajectory based on updating the plurality of approximated 2D seismic images via a first machine learning algorithm. Further, the method includes generating the 3D seismic image volume including a second plurality of seismic images based on updating the first plurality of seismic images via a second machine learning algorithm in a second trajectory different from the first trajectory.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
A system for detecting methane includes a body and a shaft extending from the body. The system also includes a wind sensor coupled to the shaft. The wind sensor is configured to measure a wind direction and a wind intensity. The system also includes a pyranometer coupled to the body. The pyranometer is configured to measure ambient light. The system also includes a methane sensor positioned on or in the body. The methane sensor is configured to measure a methane concentration in ambient air.
E21B 47/13 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling by electromagnetic energy, e.g. of radio frequency range
E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
G01S 19/01 - Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
60.
IMPLEMENTATION OF GENERATIVE ARTIFICIAL INTELLIGENCE IN OILFIELD OPERATIONS
A method for monitoring a risk to a stability of a wellbore in a subsurface formation includes receiving first input data representing the wellbore or the subsurface formation. The method also includes extracting parameter-value pairs from the first input data. The method also includes determining an expected pore pressure gradient based upon the parameter-value pairs. The method also includes determining an expected fracture gradient based upon the parameter-value pairs. The method also includes determining a mud weight uncertainty profile for the wellbore based upon the expected pore pressure gradient and the expected fracture gradient.
G01V 1/40 - SeismologySeismic or acoustic prospecting or detecting specially adapted for well-logging
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
A method may include receiving, via an interface, a digital well plan for a well at a field site; automatically determining, based on a computational analysis of at least offset well data for offset wells at different field sites, a corresponding likelihood for each of a number of undesirable events for at least one section of the well specified by the digital well plan; automatically computing a potential risk metric for each of the number of undesirable events based at least in part on the corresponding likelihood for each of the number of undesirable events; and automatically generating a graphical user interface that includes a section identifier for each of the at least one section of the well, an undesirable event identifier for each of the number of undesirable events, and a potential risk identifier based on the potential risk metric for each of the number of undesirable events.
A method for characterizing a subsurface interval where drilling fluid is being lost includes receiving surface drilling values captured while drilling a wellbore through the subsurface interval. The method also includes analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values. The method also includes identifying a first changepoint in the on-bottom surface drilling values. The method also includes identifying a second changepoint in the on-bottom MSE values. The method also includes determining a cause of a lost circulation event in the subsurface interval based upon the first and second changepoints.
A method can include receiving data for a subsurface region as acquired using sensor assemblies disposed at seabed locations, where each sensor assembly includes a multi-component seabed sensor that acquires multi-component seabed seismic data and a water pressure sensor that acquires water pressure data; generating a noise model for one of the seabed locations using the water pressure data acquired by the sensor assembly disposed at the one of the seabed locations; adaptively subtracting the noise model from the multi-component seabed seismic data acquired by the sensor assembly disposed at the one of the seabed locations to generate noise attenuated seabed seismic data; and generating an image of at least a portion of the subsurface region using the noise attenuated seabed seismic data.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
G01V 1/38 - SeismologySeismic or acoustic prospecting or detecting specially adapted for water-covered areas
A method can include accessing a seismic data file for a seismic survey defined in part by a bin grid, where seismic data in the seismic data file are organized according to a data structure that includes headers and seismic trace data; performing an assessment of orthogonality of the bin grid of the seismic data file; responsive to detection of an orthogonality issue by the assessment, determining an orthogonal bin grid; extracting a trace header template using at least one of one or more introductory headers, where the trace header template specifies at least trace locations; performing a validation operation for validation of the trace header template; and, responsive to validation of the trace header template, outputting metadata that comports with the orthogonal bin grid and the validated trace header template for loading of the seismic data file to a data storage system.
G01V 1/38 - SeismologySeismic or acoustic prospecting or detecting specially adapted for water-covered areas
G01V 1/00 - SeismologySeismic or acoustic prospecting or detecting
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
A method includes receiving image data including a plurality of cells corresponding to a plurality of measured fluid properties of a subterranean region. The method also includes generating a plurality of nodes based on the plurality of cells, wherein each node of the plurality of nodes comprises relational information related to at least a portion of an arrangement of the plurality of cells. Further, the method includes generating a graph-based representation of the plurality of cells based on the plurality of nodes. Further still, the method includes generating a predicted graph-based representation of one or more fluid properties of the subterranean region over time based on a model of fluid properties in the subterranean region and the graph-based representation. Even further, the method includes adjusting one or more operation of one or more fluid systems associated with the subterranean region based on the predicted graph-based representation.
The disclosed technology is directed to methods and systems for detecting scale within a well production structure. The methods include generating a model for the well production structure. The method also includes executing a first test using the model to generate first scale data and executing a second test based on concurrently adjusting one or more of a plurality of model parameters using updated versions of synthetic or non-synthetic data to generate a plurality of second scale data. The method also includes executing a merging operation between scale data realizations comprised in the plurality of the second scale data to generate scaling signature data. The method further includes initiating generation of one or more of: a scale data visualization, or an intervention report, or an intervention signal for a control operation that mitigates against detected scale.
A method can include receiving cone penetrometer test data of measurements referenced with respect to depth in stratified material for a location at a site; processing the cone penetrometer test data using a machine learning model-based computational framework to identify one or more quality issues and to generate synthetic cone penetrometer test data at one or more depths; and outputting improved quality cone penetrometer data based at least in part on the generated synthetic cone penetrometer test data.
A method can include receiving seismic data-based interpretations for discontinuities in a subsurface geologic region; automatically assessing the interpretations, with respect to a type of action for joining individual pairs of the discontinuities, to generate ranked individual pairs of the discontinuities; and performing the type of action for joining a number of the ranked individual pairs of the discontinuities to improve accuracy of a model of the subsurface geologic region.
System and method for designing dithers having a pre-determined distribution within a dithers range, wherein the dithers range is chosen for the seismic survey, wherein a lower boundary of the dithers range is 4 seconds or ±2 seconds dithers distribution relative to nominal shotting times of sources of seismic waves in the seismic survey, and wherein an upper boundary of the dithers range is a largest value compatible with constraints of the seismic survey.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
70.
SYSTEM AND METHOD FOR ON-DEMAND AUTONOMOUS PRECISION INFILL DRILLING
A method for autonomously simulating infill drilling of one or more wells includes calculating a plurality of connected volumes in a reservoir model. The method also includes determining that a total hydrocarbon production of one or more existing wells inside the reservoir model has decreased below a predetermined hydrocarbon production threshold. The method also includes determining that one or more drilling rigs have a capacity to support drilling a first new well in response to determining that the total hydrocarbon production has decreased below the predetermined hydrocarbon production threshold. The first new well is a production well. The method also includes determining an opportunity index in response to determining that one or more drilling rigs have the capacity. The method also includes identifying a target in the opportunity index. The method also includes placing the first new well in the reservoir model in response to identifying the target.
E21B 43/30 - Specific pattern of wells, e.g. optimising the spacing of wells
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
A method can include receiving workflow specifications for an operational workflow performed using equipment in a field to produce hydrocarbons; configuring a dynamic reservoir simulation system according to the workflow specifications; receiving, by the dynamic reservoir simulation system, field data from the equipment; responsive to receipt of the field data, updating a model representative of one or more hydrocarbon production related physical phenomena in the field to generate an updated model of the dynamic reservoir simulation system; generating model-based results using the updated model; assessing quality of the model-based results to generate one or more quality metrics; and outputting, based at least in part on the model-based results, a control action for the operational workflow and at least one of the one or more quality metrics.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method can include receiving a seismic model for a geologic region, where the seismic model includes an acoustic portion that accounts for wavefield kinematics using one or more acoustic velocity parameters and an elastic portion that accounts for wavefield elastic plane-wave reflectivity using one or more elastic vector reflectivity parameters; performing a wavefield simulation using the seismic model; during the wavefield simulation, determining angle dependent wavefield amplitude correction terms, subject to one or more structural dip-based angle criteria, using the elastic portion of the model; during the wavefield simulation, applying the angle dependent wavefield amplitude correction terms to wavefield amplitudes of the wavefield simulation to enhance seismic energy-based accuracy of the wavefield simulation; and generating a simulated wavefield as an output of the wavefield simulation.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
A method includes receiving a seismic data volume including target traces. The method also includes sparse sampling the target traces to produce a subset of representative target traces. The method also includes generating a broad area map for each representative target trace. The area map includes multiple downward reflection points (DRPs) laid out as a grid and multiple blocks. The method also includes convolving a seismic trace pair for each DRP to produce a convolved trace. The method also includes calculating a contribution weight based on a root mean square (RMS) and a semblance attribute for each block at each time window. The method also includes summing the contribution weight for each block. The method also includes selecting a set of blocks that have summed contribution weight above a threshold value. The method also includes determining one or more apertures that encompass the set of blocks.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
According to an embodiment, a method for determining the gas storage capacity in a subsurface AOI comprises: receiving a stratigraphic model representing at least a portion of the AOI; receiving sensor data associated with one or more geo-parameters of the stratigraphic model; determining boundary condition data for the one or more geo-parameters based on the sensor data; imposing computational bounds on the one or more geo-parameters of the stratigraphic model using the boundary condition data thereby initializing the stratigraphic model for the AOI; executing a simulation operation on the stratigraphic model by varying one or more values of the one or more geo-parameters within limits constrained by the computational bounds imposed on the one or more geo-parameters to generate a multi-dimensional simulation dataset for the AOI; resolving the multi-dimensional simulation dataset to generate a report representing image or textual data that indicate one or more gas storage capacities of the AOI.
Disclosed is a method comprising: receiving seismic data; generating PP-wave image angle gathers data and PS-wave image angle gathers data using the seismic data; generating PP-wave point spread function (PSF) angle gathers that serve as a first convolution input; generating PS-wave PSF angle gathers that serve as a second convolution input; generating PP-wave synthetic angle gathers data using the first convolution input and a first reflectivity operator; generating PS-wave synthetic angle gathers data using the second convolution input and a second reflectivity operator; generating first output data using the PP-wave angle gathers data and the PP-wave synthetic angle gathers data; generating second output data using the PS-wave image angle gathers data and the PS-wave synthetic angle gathers data; generating optimization data using the first output or the second output together with a parameter of a geological model; and updating, using the optimization data, an elastic property of the geological model.
G01V 1/26 - Reference-signal-transmitting devices, e.g. indicating moment of firing of shot
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
G01V 1/40 - SeismologySeismic or acoustic prospecting or detecting specially adapted for well-logging
76.
METHODS AND COMPUTING SYSTEMS FOR ATTENUATING SHEAR NOISE
A method for attenuating shear noise in seismic data including receiving seismic data. The seismic data includes multi-component seismic data including a pressure component and a vertical component. The method also includes mapping the pressure component and the vertical component to generate mapped seismic data. The method also includes determining a correlation between the pressure component and the vertical component based upon the mapped seismic data. The method also includes attenuating shear noise in the seismic data to generate attenuated seismic data, the shear noise is attenuated based upon the correlation.
G01V 1/28 - Processing seismic data, e.g. for interpretation or for event detection
G01V 1/37 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy specially adapted for seismic systems using continuous agitation of the ground
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for use in the field of oil, gas, and natural resource exploration and production; Downloadable computer software for use in the field of oil, gas, and natural resource exploration and production for data gathering, design, engineering, operations, monitoring, surveillance, diagnosis, prediction and optimization of equipment and processing for operation of wells, pipelines, industrial plants, tank farms, refineries, petrochemical plants, hydrogen plants, geothermal facilities, carbon capture plants and injection sites; downloadable computer software for use in the field of oil, gas, and natural resource exploration and production for simulations of equipment and process performance all such software utilizing artificial intelligence, machine learning and digital technology. Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production; Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production for data gathering, design, engineering, operations, monitoring, surveillance, diagnosis, prediction and optimization of equipment and processing for operation of wells, pipelines, industrial plants, tank farms, refineries, petrochemical plants, hydrogen plants, geothermal facilities, carbon capture plants and injection sites; Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production for simulations of equipment and process performance all such PaaS and SaaS services utilizing artificial intelligence, machine learning and digital technology; technical consulting and support services for PaaS and SaaS; technical consulting in the field of energy and natural resources production.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for use in the field of oil, gas, and natural resource exploration and production; Downloadable computer software for use in the field of oil, gas, and natural resource exploration and production for analysis, design, engineering, efficiency, monitoring, surveillance, prediction, optimization, risk mitigation, and problem diagnosis using data gathered related to fluid flow in wells and well-related equipment including tubular structures allowing fluid flow, including oil and gas, gathering networks, pipe lines and trunk lines; downloadable computer software for use in the field of oil, gas, and natural resource exploration and production for fluid flow simulations all such software utilizing artificial intelligence, machine learning and digital technology. Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production; Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production for analysis, design, engineering, efficiency, monitoring, surveillance, prediction, optimization, risk mitigation, and problem diagnosis using data gathered related to fluid flow in wells and well-related equipment including tubular structures allowing fluid flow, including oil and gas, gathering networks, pipe lines and trunk lines; Platform as a Service (PaaS) and Software as a Service (SaaS) featuring computer software platforms and software for use in the field of oil, gas, and natural resource exploration and production for fluid flow simulations all such PaaS and SaaS services utilizing artificial intelligence, machine learning and digital technology; technical consulting and support services for PaaS and SaaS; technical consulting in the field of energy and natural resources production.
A method implements quantum computing enabled construction planning. The method includes applying an assignment model to project data using a quantum computing system to create an assignment schedule to assign a machine to a location at a time. The method further includes applying a scheduling model to the project data and the assignment schedule using a classic computing system to create an operation schedule to assign an operation to the location during the time with the machine. The method further includes performing an action responsive to the operation schedule.
A method can include formulating an optimization problem for an operation of an industrial workflow using a geometrical construct that includes nodes and edges and an associated probabilistic solution space; determining an optimal solution for the optimization within the probabilistic solution space using a quantum computer; and, using the optimal solution, performing a subsequent operation of the industrial workflow.
The present disclosure describes techniques including sampling a multidimensional physical space to determine representative combinations of pipeline segment parameters, and executing a flow simulator to estimate pressure drop values or pressure gradient values for pipeline segments having the representative combinations of pipeline segment parameters. The techniques also include training a ML model using the pressure drop values or the pressure gradient values estimated by the flow simulator.. The ML model may output a predicted pressure drop value or pressure gradient value for a pipeline segment based on input values representing pipeline segment parameters of the pipeline segment. The techniques can also include upscaling a network model before executing a ML-based network solver to estimate a pressure drop value, a flow rate value, and node pressure values for the network model using the trained ML model.
G06F 30/18 - Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
G06F 30/20 - Design optimisation, verification or simulation
Computing systems, computer-readable media, and methods for providing an integrated platform. The method includes obtaining data from at least one source, wherein the data is in multiple formats and is related to one of an energy exploration stage, an energy development stage, and an operations stage. At least one data item from the at least one source is specified for visualization. The data is processed, wherein the processing includes parsing, extracting, and ingesting the data, the data including the at least one specified data item. Machine learning is leveraged to obtain an optimum forecasting model, the leveraging including using at least one of autoregressive integrated moving average modelling and temporal fusion transformers. The specified at least one data item is visualized. A forecasting summary is provided based on the optimum forecasting model.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present disclosure relates to various systems and methods for utilizing these T1-T2 maps for enhanced formation evaluation. For example, as described in greater detail herein, these techniques include: (1) Tikhonov regularization used in an NMR inversion and resultant bias, (2) variable depth averaging of NMR two-dimensional (2D) maps on per T1-T2 basis, and (3) NMR kernel correction.
G01V 3/32 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging operating with electron or nuclear magnetic resonance
G01V 3/14 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation operating with electron or nuclear magnetic resonance
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
A method may include receiving data from field equipment during performance of a liner hanger job at a wellsite; generating an inference as to an occurrence of an event associated with the performance of the liner hanger job based on at least a portion of the data using one or more machine learning models; and controlling the performance of the liner hanger job based at least in part on the inference.
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
E21B 33/04 - Casing headsSuspending casings or tubings in well heads
A method can include receiving digital well plan data for direction drilling of a borehole for a well in a subsurface geologic environment (1510); generating candidate trajectories for the borehole using the digital well plan data (1520); generating a ranking of the candidate trajectories using values derived from inverse reinforcement learning using historical observations for one or more other boreholes(1530); and outputting, based on the ranking, one or more of the candidate trajectories for directional drilling of the borehole (1540).
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systemsSystems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 47/022 - Determining slope or direction of the borehole, e.g. using geomagnetism
Techniques for carbon sequestration and storage site selection are presented. The techniques include: obtaining range estimations of criteria for potential carbon sequestration and storage sites; selecting random criteria values in respective range estimations; forming a decision matrix from the random criteria values; weighing the decision matrix to obtain representative vectors; scoring at least some the potential carbon sequestration and storage sites according to similarities of respective potential carbon sequestration and storage site representative vectors to a best vector and to a worst vector; ranking the potential carbon sequestration and storage sites according to the scores; repeating the selecting, forming, weighing, scoring, and ranking a plurality of times to obtain a plurality of sets of scores and a plurality of rankings; displaying a visualization of the plurality of rankings; and selecting a carbon sequestration and storage site from among the potential carbon sequestration and storage sites based on the visualization.
A method for carbon sequestration includes receiving geophysical survey data. The method also includes building a three-dimensional (3D) model based upon the geophysical survey data. The method also includes calibrating the 3D model to produce a calibrated 3D model. The method also includes extracting a soil layer from the calibrated 3D model to produce an extracted soil layer. The method also includes designing a soil-sampling campaign based upon the extracted soil layer. The soil-sampling campaign includes a plurality of cores. The method also includes determining characteristics of the cores. The method also includes propagating the characteristics through the calibrated 3D model to produce a 3D property model.
G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
This disclosure is directed to methods and systems for generating ontological datasets using cloud data for energy development operations. According to one embodiment, a data processing engine stored in a memory device may receive cloud data from a plurality of sources and generate an ontology dataset based on parsing the cloud data. The data processing engine may initiate provisioning of an electronic dashboard on a display device based on a first user input. The electronic dashboard may include one or more display elements associated with the ontology dataset. Moreover, the one or more display elements of the electronic dashboard are activatable to load a computing resource associated with the cloud data. Furthermore, the one or more display elements of the electronic dashboard: are electronically linked to the computing resource; and may comprise picture data, video data, audio data, or textual data.
A method can include selecting a subsurface ancient water bottom location that has corresponding three-dimensional seismic data; associating the subsurface ancient water bottom location with a modern water bottom location based at least in part on one or more structural features of the subsurface ancient water bottom location represented in the three-dimensional seismic data and one or more structural features of the modern water bottom location; and determining one or more ancient flow properties of the subsurface ancient water bottom location based at least in part on one or more modern flow properties of the modern water bottom location.
The present disclosure describes techniques including receiving seismic data corresponding to a subsurface region. The techniques also include filtering the seismic data. The filtered seismic data corresponds to one or more depth ranges within the subsurface region. Further, the techniques include applying a first horizon model to the filtered seismic data. The first horizon model outputs a first set of horizon data having a first resolution indicating an expected location of a horizon within the one or more depth ranges. Even further, the techniques include applying a second horizon model to a portion of the seismic data centered based on the first set of horizon data. Further still, the techniques include generating a second set of horizon data based on the portion of seismic data, the first set of horizon data, and the second horizon model. The second set of horizon data has a higher resolution than the first resolution.
A document analysis and processing (DAP) system is disclosed that includes at least one memory configured to store a corpus of documents and a topic classifier having a first trained artificial intelligence (AI) model and at least one processor configured to execute stored instructions to perform actions. The actions include, for each document of the corpus of documents: using the first trained AI model of the topic classifier to identify topics of each page of the document; mapping each of the identified topics of each page of the document to respective topic colors; combining the respective topic colors of each page of the document to yield a respective page color code for each page of the document; and combining the respective page color code of each page of the document to yield a respective document color code of the document.
Systems and methods of the present disclosure provide a bidding proposal system for bidding proposal preparation. The bidding proposal system includes an artificial intelligence (AI)-assisted system, which generates a predicted bidding proposal based on a received request. In the system, a natural language processing technique is applied to automatically generate potential answers to the questions asked by the purchaser. The systems and methods described herein enable a computing system to understand natural language of a user by identifying the user intent and providing information to generate an answer based on the user intent.
Systems and methods of the present disclosure provide acquiring images corresponding to multiple perspectives of a piece of equipment to be modeled. A region of interest in a first image of the images determined to be less blurry than a blur threshold and a brightness of the first image is above a brightness threshold. Based on these threshold relationships a three-dimensional model of the piece of equipment is generated based at least in part on a subset of the images that include the first image.
A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.
G01M 3/38 - Investigating fluid tightness of structures by using light
G01M 3/04 - Investigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
G01N 21/3504 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing gases, e.g. multi-gas analysis
Systems and methods for generating digital gamma-ray logs for target wells based on combined physics and machine learning model using real-time information (e.g., drilling parameters, survey data, gamma-ray logs, and so forth) obtained from offset wells analogous to the subject well in terms of gamma-ray readings. The systems and methods may provide solutions that may lower the cost of Measuring While Drilling (MWD) and/or Logging While Drilling (LWD) process and facilitate the users (e.g., drillers, geoscientists, and so forth) to make enhanced data driven decisions.
G01V 5/12 - Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays using gamma- or X-ray sources
A method may include receiving, via one or more processors, a first dataset associated with a plurality of projects that corresponds to performing hydrocarbon exploration or processing operations. The method may then involve receiving a second dataset associated with a plurality of assets, such that at least a portion of the plurality of assets is used to complete at least a portion of the plurality of projects. The method may also involve determining a plurality of schedules for deploying the at least a portion of the plurality of assets for use with the at least a portion of the plurality of projects, identifying a portion of the plurality of schedules based on an optimization algorithm with respect to revenue, and sending a notification indicative of the portion of the plurality of schedules to a computing device, wherein the computing device is configured to display the notification via an electronic display.
G01V 3/32 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging operating with electron or nuclear magnetic resonance
G01V 3/38 - Processing data, e.g. for analysis, for interpretation or for correction
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
A method can include initializing a visualization of volumetric seismic data using a computational framework, where the visualization includes a cuboid in a three-dimensional space for rendering as pixels in two-dimensions to a display according to a viewpoint, and where the visualization includes an opacity map; determining a front face of the cuboid and a back face of the cuboid from a perspective of the viewpoint; identifying bricks in a hierarchy of bricks of the volumetric seismic data for generation of a sparse texture for the visualization; and rendering the visualization to the display based on extending rays between the front face and the back face, sampling points on the rays, and associating the points with the sparse texture to provide values for the pixels of the visualization thresholded by the opacity map.
The disclosed methods and systems are directed to water flooding optimizations at a resource site for increased production of hydrocarbons. According to some implementations, the methods include receiving at least one of: fluid production rate data and fluid injection rate data using one or more sensors at a resource site. The methods may also include generating a forecasting model based on one or more of the fluid production rate data and the fluid injection rate data. The disclosed methods further comprise executing, using the forecasting model, one or more sensitivity tests to generate a production forecast report.
C09K 8/58 - Compositions for enhanced recovery methods for obtaining hydrocarbons, i.e. for improving the mobility of the oil, e.g. displacing fluids
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
99.
INFERENCE AS A SERVICE UTILIZING EDGE COMPUTING TECHNIQUES
Systems and methods of the present disclosure include a method for providing machine learning inferences as a service. The method includes receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device. The method also includes forwarding, via the API, the request to one or more edge computing resources. The one or more edge computing resources include one or more edge computing resource nodes, and the API may interface with one or more hardware devices of the one or more edge computing resource nodes based on the request. The method further includes generating, via the one or more edge computing resource nodes, the machine learning inference. In addition, the method includes sending, via the API, the machine learning inference to the client device.
A method can include receiving seismic data from a seismic survey of a subsurface geologic environment that includes one or more reflectors; performing a model-based iterative least squares inversion of the seismic data; for at least one iteration of the model-based iterative least squares inversion, determining a value of a regularization parameter by approximating a first function representative of a residuals norm and a second function representative of a solution norm, where the first function and the second function depend on the regularization parameter; and performing a subsequent model-based iterative least squares inversion of the seismic data using the regularization parameter to determine a position of at least one of the one or more reflectors.
