System and methods of random noise attenuation are provided. A first model may be trained to extract random noise from seismic datasets. A second model may be trained to reconstruct leaked signals from the random noise extracted by the first model. A seismic dataset corresponding to a subsurface reservoir formation and including random noise may be obtained. Using the trained first model, at least a portion of the random noise may be extracted from the first seismic dataset. Using the trained second model, a leaked signal, which includes a portion of the seismic dataset, may be reconstructed from the extracted random noise. A cleaned seismic dataset is generated based on the reconstructed leaked signal and the extracted random noise. The cleaned seismic dataset may include a quantity of random noise that is less than that of the original seismic dataset.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
G01V 1/28 - Processing seismic data, e.g. for interpretation or for event detection
A method comprises obtaining well logs for at least one measurable subsurface formation attribute of a subsurface formation into which a wellbore is formed and performing a multi-stage operation to predict a pay probability for a zone in the subsurface formation that defines a likelihood that the zone includes hydrocarbons that are commercially viable for recovery. Performing the multi-stage operation comprises predicting, via at least one interpreted log machine-learning model using the well logs as inputs, interpreted logs of at least one interpreted subsurface attribute at corresponding depths of the wellbore; and predicting, via a pay zone probability machine-learning model using the interpreted logs as inputs, the pay probability for the zone.
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 43/12 - Methods or apparatus for controlling the flow of the obtained fluid to or in wells
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 comprises obtaining well logs for at least one measurable subsurface formation attribute of a subsurface formation into which a wellbore is formed and performing a multi-stage operation to predict a pay probability for a zone in the subsurface formation that defines a likelihood that the zone includes hydrocarbons that are commercially viable for recovery. Performing the multi-stage operation comprises predicting, via at least one interpreted log machine-learning model using the well logs as inputs, interpreted logs of at least one interpreted subsurface attribute at corresponding depths of the wellbore; and predicting, via a pay zone probability machine-learning model using the interpreted logs as inputs, the pay probability for the zone.
G06Q 30/0202 - Market predictions or forecasting for commercial activities
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
A computer-implemented method, computer-readable medium, and system for digitizing well log data. The method comprises at least obtaining well log data related to one or more geological formations within the Earth's subsurface, wherein the well log data is contained in an image format file; processing the image format file using contour detection to extract bounding boxes for at least one data curve, the contour detection steps including extracting a plot region and detecting a header for the at least one data curve; extracting scale and depth values for at least one plot region from the bounding boxes; processing the header through visual question answering to extract curve properties of the at least one data curve from the header; processing the extracted curves and the plot region through text-guided segmentation, the text-guided segmentation producing masked patches of the at least one data curve; and digitizing the data curve.
G01V 11/00 - Prospecting or detecting by methods combining techniques covered by two or more of main groups
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 computer-implemented method, computer-readable medium, and system for digitizing well log data. The method comprises at least obtaining well log data related to one or more geological formations within the Earth’s subsurface, wherein the well log data is contained in an image format file; processing the image format file using contour detection to extract bounding boxes for at least one data curve, the contour detection steps including extracting a plot region and detecting a header for the at least one data curve; extracting scale and depth values for at least one plot region from the bounding boxes; processing the header through visual question answering to extract curve properties of the at least one data curve from the header; processing the extracted curves and the plot region through text-guided segmentation, the text-guided segmentation producing masked patches of the at least one data curve; and digitizing the data curve.
A method for managing integrity of a tubular comprises obtaining fluid transportation system data, wherein the tubular is a component within a fluid transportation system. The method comprises determining, via a mechanistic model, a mechanistic corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a learning machine, a residual corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a hybrid model, a final corrosion rate of the tubular based on the mechanistic corrosion rate and the residual corrosion rate.
A method for managing integrity of a tubular comprises obtaining fluid transportation system data, wherein the tubular is a component within a fluid transportation system. The method comprises determining, via a mechanistic model, a mechanistic corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a learning machine, a residual corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a hybrid model, a final corrosion rate of the tubular based on the mechanistic corrosion rate and the residual corrosion rate.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
Some implementations include a method which comprises determining an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within a wellbore, the wellbore formed in one or more subsurface formations.
Some implementations include a method which comprises determining an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within a wellbore, the wellbore formed in one or more subsurface formations.
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 comprises obtaining subsurface formation properties of the subsurface formation and obtaining, via one or more sensors on the drill string assembly, wellbore properties corresponding to a first depth interval of the wellbore, wherein the first depth interval comprises one or more axial layers. The method comprises selecting, via a hole profile generator, a failure criteria of the subsurface formation corresponding to the first depth interval. The method comprises determining, via the hole profile generator, a layer failure volume for each axial layer of the first depth interval based on the wellbore properties and the failure criteria. The method comprises determining, via the hole profile generator, a radial distance of the wellbore for the first depth interval based on the layer failure volumes. The method comprises identifying, via the hole profile generator, a hole profile type for the wellbore based on the radial distance of the first depth interval.
A method comprises obtaining subsurface formation properties of the subsurface formation and obtaining, via one or more sensors on the drill string assembly, wellbore properties corresponding to a first depth interval of the wellbore, wherein the first depth interval comprises one or more axial layers. The method comprises selecting, via a hole profile generator, a failure criteria of the subsurface formation corresponding to the first depth interval. The method comprises determining, via the hole profile generator, a layer failure volume for each axial layer of the first depth interval based on the wellbore properties and the failure criteria. The method comprises determining, via the hole profile 10 generator, a radial distance of the wellbore for the first depth interval based on the layer failure volumes. The method comprises identifying, via the hole profile generator, a hole profile type for the wellbore based on the radial distance of the first depth interval.
A system can determine, based on an accumulation of geological material in a wellbore, a pack off indicator indicating a likelihood of a downhole tool being immobile in the wellbore. The accumulation of geological material in the wellbore comprises cuttings resulting from an action of the downhole tool positioned in the wellbore and settled cavings resulting from instability in the wellbore. Determining the pack off indicator can include estimating the accumulation of geological material in the wellbore. Determining the pack off indicator additionally can include dividing the estimated accumulation by a volume of a wellbore annulus defined by a first set of wellbore piping and a second set of wellbore piping positioned in the wellbore. The system can automatically adjust a wellbore operation using the pack off indicator by controlling a flow of circulating fluid in response to the pack off indicator exceeding a predetermined threshold.
A caving volume or caving probability can be determined by using received user inputs and received subterranean formation characteristics. The portion of the subterranean formation characteristics that represent the rock stresses can be transformed to a coordinate system, such as a cylindrical system. Subterranean formation parameters can be calculated from the transformed characteristics. A lithology-specific algorithm can be applied to the subterranean formation parameters to generate a failure criterion. The caving analysis can then be performed using the subterranean formation parameters. The caving analysis can be performed at incremental radial distance layers into the subterrane formation from a borehole wall. The caving analysis can be performed at various measured depth layers within a depth interval of the borehole where the total caving volume is the total of the individual calculated caving volumes at each measured depth layer.
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
G01V 3/18 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging
A borehole stability index can be determined by using received user inputs and received subterranean formation characteristics. The portion of the subterranean formation characteristics that represent the rock stresses can be transformed to a coordinate system, such as a cylindrical system. Subterranean formation parameters can be calculated from the transformed characteristics. A lithology-specific algorithm can be applied to the subterranean formation parameters to generate a borehole stability index. The borehole stability analysis can then be performed using the subterranean formation parameters. The borehole stability analysis can be performed at incremental radial distance layers into the subterrane formation from a borehole wall. The borehole stability analysis can be performed at various measured depth layers within a measured depth interval of the borehole where the borehole stability index is the algorithmic combination of the individual calculated borehole stability indexes at each measured depth layer.
A caving volume or caving probability can be determined by using received user inputs and received subterranean formation characteristics. The portion of the subterranean formation characteristics that represent the rock stresses can be transformed to a coordinate system, such as a cylindrical system. Subterranean formation parameters can be calculated from the transformed characteristics. A lithology-specific algorithm can be applied to the subterranean formation parameters to generate a failure criterion. The caving analysis can then be performed using the subterranean formation parameters. The caving analysis can be performed at incremental radial distance layers into the subterrane formation from a borehole wall. The caving analysis can be performed at various measured depth layers within a depth interval of the borehole where the total caving volume is the total of the individual calculated caving volumes at each measured depth layer.
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 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
16.
Calculating borehole stability index for drilling operations
A borehole stability index can be determined by using received user inputs and received subterranean formation characteristics. The portion of the subterranean formation characteristics that represent the rock stresses can be transformed to a coordinate system, such as a cylindrical system. Subterranean formation parameters can be calculated from the transformed characteristics. A lithology-specific algorithm can be applied to the subterranean formation parameters to generate a borehole stability index. The borehole stability analysis can then be performed using the subterranean formation parameters. The borehole stability analysis can be performed at incremental radial distance layers into the subterrane formation from a borehole wall. The borehole stability analysis can be performed at various measured depth layers within a measured depth interval of the borehole where the borehole stability index is the algorithmic combination of the individual calculated borehole stability indexes at each measured depth layer.
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
IMPROVING THE ACCURACY OF RESERVOIR FACIES AND PETROPHYSICAL PROPERTY MODELS USING MULTIPLE INFORMATION SOURCES THROUGH QUANTILE MACHINE LEARNING TECHNIQUES
Some implementations relate to a method for generating, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of a reservoir using a plurality of external data sources different than the formation property.
G01V 1/40 - SeismologySeismic or acoustic prospecting or detecting specially adapted for well-logging
G01V 99/00 - Subject matter not provided for in other groups of this subclass
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
Some implementations relate to a method for generating, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of a reservoir using a plurality of external data sources different than the formation property.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
19.
METHOD FOR DIFFERENTIATING CARBONATES AND VOLCANOES IN SEISMIC DATA
A method for analyzing seismic data of a subterranean formation includes obtaining the seismic data and identifying one or more potential carbonate buildups in the seismic data. Further, historical paleoclimate data for the formation of the one or more potential carbonate buildups is obtained, and the seismic data and the historical paleoclimate data are processed to generate a plurality of parameter scores for a plurality of characteristics of the formation; A weighted sum calculating scores is calculated using a plurality of parameter weights.
A method for analyzing seismic data of a subterranean formation includes obtaining the seismic data and identifying one or more potential carbonate buildups in the seismic data. Further, historical paleoclimate data for the formation of the one or more potential carbonate buildups is obtained, and the seismic data and the historical paleoclimate data are processed to generate a plurality of parameter scores for a plurality of characteristics of the formation; A weighted sum calculating scores is calculated using a plurality of parameter weights.
A method comprises retrieving attributes of a blowout well and a relief well and simulating a dynamic kill of a blowout from the blowout well via a kill mud pumped down through the relief well, wherein the simulating comprises determining a flowing bottom hole pressure for the blowout well and the relief well based on an intersection of a reservoir inflow performance relationship (IPR) curve and a wellbore vertical lift (VLP) curve.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
A method comprises retrieving attributes of a blowout well and a relief well and simulating a dynamic kill of a blowout from the blowout well via a kill mud pumped down through the relief well, wherein the simulating comprises determining a flowing bottom hole pressure for the blowout well and the relief well based on an intersection of a reservoir inflow performance relationship (IPR) curve and a wellbore vertical lift (VLP) curve.
The disclosure provides an automated process for determining the wear condition of a downhole tool that removes the subjectivity associated with manual observation. The automated process can advantageously evaluate a wear condition of a downhole tool using visual analytics and real-time analysis after the downhole tool has been extracted from the wellbore. An example of a method includes: (1) securing a downhole tool in a rig assembly, (2) obtaining, using sensors, surround tool data of the downhole tool in the rig assembly, wherein the surround tool data includes a first set of surround tool data obtained before a downhole operation by the downhole tool and a second set of surround tool data obtained after the downhole operation, and (3) automatically determining a wear condition of the downhole tool in real time by comparing the second set of surround tool data to the first set of surround tool data.
In some implementations, a method comprises generating a training dataset including a plurality of sample systems tracts each associated with a respective sample rate of change of subsidence and a respective sediment supply. The method also may comprise training a learning machine to indicate predicted systems tracts for wells based on the plurality of sample system tracts and their respective sample rate of change of subsidence and respective sample sediment supplies.
In some implementations, a method comprises generating a training dataset including a plurality of sample systems tracts each associated with a respective sample rate of change of subsidence and a respective sediment supply. The method also may comprise training a learning machine to indicate predicted systems tracts for wells based on the plurality of sample system tracts and their respective sample rate of change of subsidence and respective sample sediment supplies.
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
The disclosure provides automated analysis of oil and gas textual data that uses one or more LLMs and designed prompts or prompt chains. The prompts, referred to a curated domain prompts, use oil and gas domain knowledge to simulate human thinking and analysis. The curated domain prompts are pre-configured such that users do not need to create prompts for analyzing the textual data. In one example, a method of automatically analyzing oil and gas textual data, includes: (1) obtaining a curated domain prompt that identifies an oil and gas operation event and a parameter associated with the oil and gas operation event, (2) automatically extracting, using a large language model (LLM), event data from oil and gas textual data based on the oil and gas operation event and the parameter, and (3) automatically generating an event summary that correlates the oil and gas operation event and the event data.
The disclosure provides automated analysis of oil and gas textual data that uses one or more LLMs and designed prompts or prompt chains. The prompts, referred to a curated domain prompts, use oil and gas domain knowledge to simulate human thinking and analysis. The curated domain prompts are pre-configured such that users do not need to create prompts for analyzing the textual data. In one example, a method of automatically analyzing oil and gas textual data, includes: (1) obtaining a curated domain prompt that identifies an oil and gas operation event and a parameter associated with the oil and gas operation event, (2) automatically extracting, using a large language model (LLM), event data from oil and gas textual data based on the oil and gas operation event and the parameter, and (3) automatically generating an event summary that correlates the oil and gas operation event and the event data.
Determining the location, size, and orientation of features within a subterranean formation can be determined by using more than one set of azimuthal data collected along at least two different angle ranges of seismic detection. The azimuthal data collected along one azimuthal range can be stacked and combined. A feature probability map can be generated for each azimuthal data collection using a machine learning system. Feature probability maps generated using azimuthal data collected along different azimuthal angle ranges can be used to optimize a machine learning estimator to generate ensemble azimuthal datasets. More than one estimator can be used thereby generating more than one ensemble azimuthal dataset. These results can be combined using a weighting algorithm applied using a machine learning model resulting in a combined feature probability map that can reduce the uncertainty of the characteristics of the feature of the subterranean formation.
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
Determining the location, size, and orientation of features within a subterranean formation can be determined by using more than one set of azimuthal data collected along at least two different angle ranges of seismic detection. The azimuthal data collected along one azimuthal range can be stacked and combined. A feature probability map can be generated for each azimuthal data collection using a machine learning system. Feature probability maps generated using azimuthal data collected along different azimuthal angle ranges can be used to optimize a machine learning estimator to generate ensemble azimuthal datasets. More than one estimator can be used thereby generating more than one ensemble azimuthal dataset. These results can be combined using a weighting algorithm applied using a machine learning model resulting in a combined feature probability map that can reduce the uncertainty of the characteristics of the feature of the subterranean formation.
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
Some implementations include a method for detecting leaks across a geological fault. The method may include determining probabilities of smears and smear breaches along a fault plane of the geological fault; and determining, based on the probabilities, one or more leak points through which hydrocarbon fluid leaks from a subsurface reservoir.
E21B 47/10 - Locating fluid leaks, intrusions or movements
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
31.
SYSTEM FOR PROBABILISTICLY DETERMINING SHALE SMEAR OCCURANCE FOR FAULT SEAL ANALYSIS
Some implementations include a method for detecting leaks across a geological fault. The method may include determining probabilities of smears and smear breaches along a fault plane of the geological fault; and determining, based on the probabilities, one or more leak points through which hydrocarbon fluid leaks from a subsurface reservoir.
E21B 47/113 - Locating fluid leaks, intrusions or movements using electrical indicationsLocating fluid leaks, intrusions or movements using light radiation
32.
HYBRID ESP FAILURE PREDICTION USING FUZZY LOGIC FOR DATA IMPROVEMENT AND AUGMENTATION
Systems and methods are described using a trained ML model to monitor, detect failure within, and schedule a remediation procedure (RP) for an operating ESP within a well. ESP status data including a time series comprising ESP input variables representing ESP state are collected from a sensor. Using fuzzy logic, the ESP status data is cleaned to remove abnormal data and used to generate fuzzy logic-based labels, each representing an ESP condition associated with ESP state. The fuzzy logic-based labels are segregated into processed labels used to populate each ML model feature. A selected, trained ML model with improved accuracy for ESP monitoring, failure detection, and RP scheduling for the ESP (based on specific ML model, well, and ESP), accepts the ML model features as input. An ESP failure alert is generated by the ML model based on the ESP status data. The RP is scheduled before ESP catastrophic failure.
Systems and methods are described using a trained ML model to monitor, detect failure within, and schedule a remediation procedure (RP) for an operating ESP within a well. ESP status data including a time series comprising ESP input variables representing ESP state are collected from a sensor. Using fuzzy logic, the ESP status data is cleaned to remove abnormal data and used to generate fuzzy logic-based labels, each representing an ESP condition associated with ESP state. The fuzzy logic-based labels are segregated into processed labels used to populate each ML model feature. A selected, trained ML model with improved accuracy for ESP monitoring, failure detection, and RP scheduling for the ESP (based on specific ML model, well, and ESP), accepts the ML model features as input. An ESP failure alert is generated by the ML model based on the ESP status data. The RP is scheduled before ESP catastrophic failure.
In general, in one aspect, embodiments relate to a method that includes selecting one or more stratigraphic forward models from a digital analogue library, generating one or more k-layers based at least in part on the one or more selected stratigraphic forward models and one or more generative machine learning models, and predicting thicknesses of one or more geological properties based at least in part on the one or more k-layers.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
35.
A SYSTEM FOR DEVELOPING GEOLOGICAL SUBSURFACE MODELS USING MACHINE LEARNING
In general, in one aspect, embodiments relate to a method that includes selecting one or more stratigraphic forward models from a digital analogue library, generating one or more k-layers based at least in part on the one or more selected stratigraphic forward models and one or more generative machine learning models, and predicting thicknesses of one or more geological properties based at least in part on the one or more k-layers.
A method for creating a generated signal which includes training a signal generator using a physics informed constraint, providing, to the signal generator, raw data that includes a signal and noise, where the signal generator processes the raw data to create the generated signal, and obtaining the generated signal from the signal generator.
A method for analyzing an unproduced reservoir that includes obtaining a new reservoir dataset, for the unproduced reservoir, that includes a plurality of desired reservoir data types, identifying a plurality of existing reservoir datasets, in a historical reservoir database, where each reservoir dataset, of the plurality of existing reservoir datasets, includes a desired reservoir data type of the plurality of desired reservoir data types, training a plurality of finalist machine learning models using the plurality of existing reservoir datasets, identifying a best finalist machine learning model of the plurality of finalist machine learning models, and processing the new reservoir dataset, using best finalist machine learning model, to generate analysis data for the unproduced reservoir.
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 for analyzing an unproduced reservoir that includes obtaining a new reservoir dataset, for the unproduced reservoir, that includes a plurality of desired reservoir data types, identifying a plurality of existing reservoir datasets, in a historical reservoir database, where each reservoir dataset, of the plurality of existing reservoir datasets, includes a desired reservoir data type of the plurality of desired reservoir data types, training a plurality of finalist machine learning models using the plurality of existing reservoir datasets, identifying a best finalist machine learning model of the plurality of finalist machine learning models, and processing the new reservoir dataset, using best finalist machine learning model, to generate analysis data for the unproduced reservoir.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
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
39.
Unsupervised Framework For Decoupling Signals Or Noise From Data
A method for creating a generated signal which includes training a signal generator using a physics informed constraint, providing, to the signal generator, raw data that includes a signal and noise, where the signal generator processes the raw data to create the generated signal, and obtaining the generated signal from the signal generator.
A system can be used to analyze a wellbore operation using symbolic dynamics. The system can receive baseline data and subsequent data about a downhole tool. The system can transform the baseline data and the subsequent data into symbolic representations thereof. The system can determine, using symbolic dynamics to compare the symbolic representations, a degree of difference between the symbolic representations. The system can provide the degree of difference between the symbolic representations via an output device. The degree of difference can be used to control the wellbore operation.
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 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
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
A system can be used to analyze a wellbore operation using symbolic dynamics. The system can receive baseline data and subsequent data about a downhole tool. The system can transform the baseline data and the subsequent data into symbolic representations thereof. The system can determine, using symbolic dynamics to compare the symbolic representations, a degree of difference between the symbolic representations. The system can provide the degree of difference between the symbolic representations via an output device. The degree of difference can be used to control the wellbore operation.
Some implementations may include a method for detecting, by a learning machine, a geobody in a seismic volume. The method may include receiving a first seismic input tile representing first seismic data from the seismic volume; receiving a first guide input tile including first labels that indicate presence of the geobody in a respective region in the seismic volume or absence of the geobody in the respective region, and one or more unlabeled regions that make no indication about presence or absence of the geobody; and determining, based on the first seismic input tile and the first guide input tile, a first prediction about geobody presence or absence in the seismic volume.
Some implementations may include a method for detecting, by a learning machine, a geobody in a seismic volume. The method may include receiving a first seismic input tile representing first seismic data from the seismic volume; receiving a first guide input tile including first labels that indicate presence of the geobody in a respective region in the seismic volume or absence of the geobody in the respective region, and one or more unlabeled regions that make no indication about presence or absence of the geobody; and determining, based on the first seismic input tile and the first guide input tile, a first prediction about geobody presence or absence in the seismic volume.
Some implementations include a method for predicting closure of a subsurface safety valve (SCSSV) configured to shut-in a well without any sensors on the SCSSV. The method may include obtaining, by a learning machine, sensor readings indicating downhole conditions in the well. The method may include predicting, by the learning machine, closure of the SCSSV based on the sensor readings indicating downhole conditions in the well. The method may include transmitting a communication predicting closure of the SCSSV.
Some implementations include a method for predicting closure of a subsurface safety valve (SCSSV) configured to shut-in a well without any sensors on the SCSSV. The method may include obtaining, by a learning machine, sensor readings indicating downhole conditions in the well. The method may include predicting, by the learning machine, closure of the SCSSV based on the sensor readings indicating downhole conditions in the well. The method may include transmitting a communication predicting closure of the SCSSV.
E21B 34/10 - Valve arrangements for boreholes or wells in wells operated by control fluid supplied from outside the borehole
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 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
A method for generating a merged survey dataset, that includes obtaining a plurality of survey datasets, calculating edges between survey datasets of the plurality of survey datasets, modifying the plurality of survey datasets, based on the edges, to obtain a plurality of modified survey datasets, and generating the merged survey dataset using the plurality of modified survey datasets.
A method for generating a merged survey dataset, that includes obtaining a plurality of survey datasets, calculating edges between survey datasets of the plurality of survey datasets, modifying the plurality of survey datasets, based on the edges, to obtain a plurality of modified survey datasets, and generating the merged survey dataset using the plurality of modified survey datasets.
A method comprises obtaining geology data of a subsurface formation and generating a subsurface model of the subsurface formation, the subsurface model including one or more age-attributed geometries of a first age scheme. The method comprises obtaining a first contextual information dataset of a target age scheme and converting each of the one or more age-attributed geometries to a target age-attributed geometry based on the target age scheme. The method comprises integrating the first contextual information dataset into the subsurface model, via the one or more target age-attributed geometries, to generate a context volume. The method comprises performing a subsurface operation based on the context volume.
A method comprises obtaining geology data of a subsurface formation and generating a subsurface model of the subsurface formation, the subsurface model including one or more age-attributed geometries of a first age scheme. The method comprises obtaining a first contextual information dataset of a target age scheme and converting each of the one or more age-attributed geometries to a target age-attributed geometry based on the target age scheme. The method comprises integrating the first contextual information dataset into the subsurface model, via the one or more target age-attributed geometries, to generate a context volume. The method comprises performing a subsurface operation based on the context volume.
A method for determining an emissions associated with hydrocarbon recovery of a hydrocarbon site within a geographic region, the method comprises selecting the hydrocarbon site for which to determine the emissions. The method comprises determining current values of hydrocarbon related attributes that affect emissions at the hydrocarbon site for a current time frame. The method comprises inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site.
A method for determining an emissions associated with hydrocarbon recovery of a hydrocarbon site within a geographic region, the method comprises selecting the hydrocarbon site for which to determine the emissions. The method comprises determining current values of hydrocarbon related attributes that affect emissions at the hydrocarbon site for a current time frame. The method comprises inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site.
In some embodiments, a method comprises quantifying a condition of a wellbore into which a string is to be deployed, wherein quantifying the condition of the wellbore comprises, determining at least one geometrical parameter of the wellbore; and determining at least one mechanical parameter of the string that is caused by deploying the string downhole into the wellbore. The method also comprises determining a string runnability index for running the string into the wellbore based on the quantified condition of the wellbore.
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
53.
WELLBORE STRING RUNNABILITY FOR DOWNHOLE OPERATIONS
In some embodiments, a method comprises quantifying a condition of a wellbore into which a string is to be deployed, wherein quantifying the condition of the wellbore comprises, determining at least one geometrical parameter of the wellbore; and determining at least one mechanical parameter of the string that is caused by deploying the string downhole into the wellbore. The method also comprises determining a string runnability index for running the string into the wellbore based on the quantified condition of the wellbore.
E21B 43/10 - Setting of casings, screens or liners in wells
E21B 23/04 - Apparatus for displacing, setting, locking, releasing or removing tools, packers or the like in boreholes or wells operated by fluid means, e.g. actuated by explosion
E21B 23/02 - Apparatus for displacing, setting, locking, releasing or removing tools, packers or the like in boreholes or wells for locking the tools or the like in landing nipples or in recesses between adjacent sections of tubing
54.
Diffusion modeling based subsurface formation evaluation
Some implementations include a method for controlling a computer to geologically characterize a space relative to a borehole. The method may include configuring a diffusion process applied to information and data about samples of reservoir parameters. The method also may include determining, via the diffusion process, a probability distribution of the reservoir parameters in the space relative to the borehole.
Some implementations include a method for controlling a computer to geologically characterize a space relative to a borehole. The method may include configuring a diffusion process applied to information and data about samples of reservoir parameters. The method also may include determining, via the diffusion process, a probability distribution of the reservoir parameters in the space relative to the borehole.
A method comprising obtaining a thickness for each of one or more sediment packages of a subsurface formation. The method comprises generating a thickness profile of each of the one or more sediment packages based on the thickness. The method comprises obtaining one or more properties of each of the one or more sediment packages based on the thickness profile. The method comprises generating, via a learning machine, one or more sediment package classifications based on the one or more properties. The method comprises and performing a subsurface operation based on the one or more sediment package classifications.
G06F 18/2411 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
A method comprising obtaining a thickness for each of one or more sediment packages of a subsurface formation. The method comprises generating a thickness profile of each of the one or more sediment packages based on the thickness. The method comprises obtaining one or more properties of each of the one or more sediment packages based on the thickness profile. The method comprises generating, via a learning machine, one or more sediment package classifications based on the one or more properties. The method comprises and performing a subsurface operation based on the one or more sediment package classifications.
A system for drilling a borehole at a wellsite, the system including a BHA with a drill bit and operable to drill the borehole as part of a borehole drilling operation using a modeled operational parameter. The system also includes a sensor located in the borehole and operable to measure an actual operational parameter in real-time. A controller including a processor performs operations that include: determining a modeled result for the borehole drilling operation using a well engineering model; receiving the measurement of the actual operational parameter from the sensor; determining an actual result for the borehole drilling operation using the well engineering model; automatically calibrating the well engineering model using the modeled result and the actual result to produce a calibrated well engineering model; and adjusting the modeled operational parameter of the drilling operation based on the calibrated well engineering model.
E21B 47/01 - Devices for supporting measuring instruments on drill bits, pipes, rods or wirelinesProtecting measuring instruments in boreholes against heat, shock, pressure or the like
A system can receive data relating to a tubular of a well system. The system can execute a first module to determine first outputs. The system can execute a second module to determine second outputs based on the first outputs. The system can execute a third module to determine third outputs based on the first outputs. The second outputs can include a crack-initiation fracture pressure, and the third outputs can include a crack-propagation fracture pressure. The system can identify a brittle-burst strength of the tubular from among the second outputs, the third outputs, and a standard burst strength of the tubular. The system can provide the brittle-burst strength of the tubular to facilitate an adjustment to the tubular to optimize a wellbore operation associated with the well system.
A system for drilling a borehole at a wellsite, the system including a BHA with a drill bit and operable to drill the borehole as part of a borehole drilling operation using a modeled operational parameter. The system also includes a sensor located in the borehole and operable to measure an actual operational parameter in real-time. A controller including a processor performs operations that include: determining a modeled result for the borehole drilling operation using a well engineering model; receiving the measurement of the actual operational parameter from the sensor; determining an actual result for the borehole drilling operation using the well engineering model; automatically calibrating the well engineering model using the modeled result and the actual result to produce a calibrated well engineering model; and adjusting the modeled operational parameter of the drilling operation based on the calibrated well engineering model.
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
61.
Adjusting fluid injection into a wellbore based on relative permeability of a formation
A fluid injection process for injecting fluid into a wellbore can be adjusted based on relative permeability. An actual trapped gas saturation of a formation can be determined from a maximum gas saturation, a maximum trapped gas saturation, and an actual gas saturation. A pseudo-maximum gas saturation can be determined from the actual trapped gas saturation, the maximum trapped gas saturation, and actual gas saturation. A relative permeability of the formation can be determined by mapping the pseudo-maximum gas saturation along a drainage curve. The fluid injection process can be adjusted based on the relative permeability.
A fluid injection process for injecting fluid into a wellbore can be adjusted based on relative permeability. An actual trapped gas saturation of a formation can be determined from a maximum gas saturation, a maximum trapped gas saturation, and an actual gas saturation. A pseudo-maximum gas saturation can be determined from the actual trapped gas saturation, the maximum trapped gas saturation, and actual gas saturation. A relative permeability of the formation can be determined by mapping the pseudo-maximum gas saturation along a drainage curve. The fluid injection process can be adjusted based on the relative permeability.
In some embodiments, a method for computing, by a volume data processor, volumetrics of a subsurface region without gridlines associated with the subsurface region comprises creating, in the volume data processor, a geometry representing the subsurface region and first bounding box about the geometry, computing a first probability that a group of sampled points inside the first bounding box are inside the geometry, and computing a gross rock volume (GRV) of the geometry by multiplying the first probability by a volume of the first bounding box.
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
In some embodiments, a method for computing, by a volume data processor, volumetrics of a subsurface region without gridlines associated with the subsurface region comprises creating, in the volume data processor, a geometry representing the subsurface region and first bounding box about the geometry, computing a first probability that a group of sampled points inside the first bounding box are inside the geometry, and computing a gross rock volume (GRV) of the geometry by multiplying the first probability by a volume of the first bounding box.
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
A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.
A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.
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
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable and recorded computer software for use in the exploration and production of hydrocarbons in the oil and gas industry providing online non-downloadable computer software for use in the exploration and production of hydrocarbons in the oil and gas industry
68.
BRITTLE-BURST STRENGTH FOR WELL SYSTEM TUBULAR INTEGRITY
A system can receive data relating to a tubular of a well system. The system can execute a first module to determine first outputs. The system can execute a second module to determine second outputs based on the first outputs. The system can execute a third module to determine third outputs based on the first outputs. The second outputs can include a crack-initiation fracture pressure, and the third outputs can include a crack-propagation fracture pressure. The system can identify a brittle-burst strength of the tubular from among the second outputs, the third outputs, and a standard burst strength of the tubular. The system can provide the brittle-burst strength of the tubular to facilitate an adjustment to the tubular to optimize a wellbore operation associated with the well system.
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 43/267 - Methods for stimulating production by forming crevices or fractures reinforcing fractures by propping
Disclosed herein are embodiments of a method, a non-transitory computer readable medium, and an apparatus for faulted seismic horizon mapping. In one example, a method comprises: obtaining seismic data for a seismic volume that corresponds to a subsurface formation; generating a map of at least one horizon in the subsurface formation based on the seismic volume; identifying at least one fault intersecting the at least one horizon; determining a throw of the at least one fault; and updating the map of the at least one horizon to incorporate the at least one fault based on the throw of the at least one fault.
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
A system can generate, via a software model, downhole pressure estimations and downhole debris estimations using caving parameters. Additionally, the system can generate, via the software model, settled caving volume percent estimations using the caving parameters. The system can determine a pack off volume percent using the downhole pressure estimations, the downhole debris estimations, and the settled caving volume percent estimations. The system can output, via a user interface, the pack off indicator and a subset of the caving parameters for use in adjusting a wellbore operation. The user interface can provide a plot of the pack off volume percent horizontally offset with respect to a plot of the subset of the caving parameters and a depth of the wellbore.
A method for processing seismic data by a seismic data system. The method comprises acquiring a plurality of first traces each corresponding to a respective first trace location. The method comprises expressing the first traces as first vertices in a first graph in which first edges connect the first vertices, wherein the first edges indicate positioning of the first vertices.
A system can generate, via a software model, downhole pressure estimations and downhole debris estimations using caving parameters. Additionally, the system can generate, via the software model, settled caving volume percent estimations using the caving parameters. The system can determine a pack off volume percent using the downhole pressure estimations, the downhole debris estimations, and the settled caving volume percent estimations. The system can output, via a user interface, the pack off indicator and a subset of the caving parameters for use in adjusting a wellbore operation. The user interface can provide a plot of the pack off volume percent horizontally offset with respect to a plot of the subset of the caving parameters and a depth of the wellbore.
Disclosed herein are embodiments of a method, a non-transitory computer readable medium, and an apparatus for faulted seismic horizon mapping. In one example, a method comprises: obtaining seismic data for a seismic volume that corresponds to a subsurface formation; generating a map of at least one horizon in the subsurface formation based on the seismic volume; identifying at least one fault intersecting the at least one horizon; determining a throw of the at least one fault; and updating the map of the at least one horizon to incorporate the at least one fault based on the throw of the at least one fault.
A method for processing seismic data by a seismic data system. The method comprises acquiring a plurality of first traces each corresponding to a respective first trace location. The method comprises expressing the first traces as first vertices in a first graph in which first edges connect the first vertices, wherein the first edges indicate positioning of the first vertices.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spreadCorrelating seismic signalsEliminating effects of unwanted energy
75.
METHOD AND SYSTEM FOR PREDICTION AND CLASSIFICATION OF INTEGRATED VIRTUAL AND PHYSICAL SENSOR DATA
The present disclosure is related to improvements in methods for evaluating and predicting responses of virtual sensors to determine formation and fluid properties as well as classifying the predicted as plausible or outlier responses that can indicate the need for maintenance of downhole physical sensors. In one aspect, a method includes detecting a change to a system of operating a wellbore to yield a determination, the system including a virtual sensor, the virtual sensor including a physical sensor placed in the wellbore for collecting one or more physical properties inside the wellbore; and based on the determination, performing one of retraining a machine learning model for predicting an output of the virtual sensor or predicting an output of the virtual sensor using the machine learning mode, the predicted output being indicative of at least one of sub-surface formation or fluid properties inside the wellbore.
A method for controlling computerized operations related to a wellbore comprises drilling the wellbore in a subsurface formation with a drill string including a drill bit. The method comprises acquiring a plurality of drilling parameters while drilling the wellbore. The method comprises determining, based on the plurality of drilling parameters, solids properties for solids forming a cutting plug up hole of the drill bit. The method comprises determining a length of the cutting plug based on the solids properties. The method comprises determining a cutting plug friction force based on the cutting plug length and a pressure differential across the cutting plug. The method comprises performing a drilling operation based on the cutting plug friction force.
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 29/00 - Cutting or destroying pipes, packers, plugs or wire lines, located in boreholes or wells, e.g. cutting of damaged pipes, of windowsDeforming of pipes in boreholes or wellsReconditioning of well casings while in the ground
A method for controlling computerized operations related to a wellbore comprises drilling the wellbore in a subsurface formation with a drill string including a drill bit. The method comprises acquiring a plurality of drilling parameters while drilling the wellbore. The method comprises determining, based on the plurality of drilling parameters, solids properties for solids forming a cutting plug up hole of the drill bit. The method comprises determining a length of the cutting plug based on the solids properties. The method comprises determining a cutting plug friction force based on the cutting plug length and a pressure differential across the cutting plug. The method comprises performing a drilling operation based on the cutting plug friction force.
The disclosure relates to determining rock properties of subterranean formations and learning the distribution of hydrocarbons in the formations. A geometrical element spread function is disclosed that quantifies distortion of the geology as seen by the geophysicists who process seismic images of the subterranean formations. A method of determining the rock properties using the seismic images and synthetic images is provided. In one example, the method includes: (1) obtaining seismic data from a subterranean formation using a seismic acquisition system, (2) generating one or more seismic images of the subterranean formation using the seismic data, (3) creating one or more synthetic images from the one or more seismic images, and (4) determining rock properties of the subterranean formation based on the one or more seismic images and the one or more synthetic images.
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
A geoscience knowledge system can be obtained, where the geoscience knowledge system can include one or more of publicly available information, industry information, proprietary information, or task specific information. The geoscience knowledge system can be represented as a graph, graph data, network nodes, image data, tokenized data, or textualized data. Subsurface information can be obtained such as from seismic images or other types of sensor data. The subsurface information can be transformed or pre-processed, such as denoising, to make it suitable for use by the geoscience knowledge system. Then subsurface knowledge can be inferred from the subsurface information using the geoscience knowledge system. The subsurface knowledge can provided estimates, approximations, or value of the subterranean formation of interest in order to calculate an economic model parameter, such as a hydrocarbon distribution proximate the subterranean formation of interest.
A geoscience knowledge system can be obtained, where the geoscience knowledge system can include one or more of publicly available information, industry information, proprietary information, or task specific information. The geoscience knowledge system can be represented as a graph, graph data, network nodes, image data, tokenized data, or textualized data. Subsurface information can be obtained such as from seismic images or other types of sensor data. The subsurface information can be transformed or pre-processed, such as denoising, to make it suitable for use by the geoscience knowledge system. Then subsurface knowledge can be inferred from the subsurface information using the geoscience knowledge system. The subsurface knowledge can provided estimates, approximations, or value of the subterranean formation of interest in order to calculate an economic model parameter, such as a hydrocarbon distribution proximate the subterranean formation of interest.
A system can be used for optimizing a wellbore operation via a metaverse space that can include one or more avatars. The system can provide access to the metaverse space for an entity. The metaverse space can be a computer-generated representation of a location relating to a wellbore operation. The system can receive, via an avatar in the metaverse space, a query from the entity relating to the wellbore operation. The avatar can include software applications for performing tasks in the metaverse space. The system can execute, via the avatar, a request to a micro-service for at least one solution parameter based on the query. The request can cause the micro-service to generate the at least one solution parameter. The system can receive the at least one solution parameter from the micro-service. The system can output the at least one solution parameter for adjusting the wellbore operation.
Processes to receive user input parameters and system input parameters associated with a borehole undergoing active drilling operations to continually update drilling directions with wholistically applied optimizations to bring the actual borehole trajectory closer to the planned borehole trajectory. The processes can project ahead of the drilling assembly to determine the actual trajectory of the borehole and generate corrections to reduce the gap between the actual and planned trajectory paths. Various optimizations can be applied to the corrections to avoid overstressing systems or reducing the borehole productivity. Conflicts between optimizations can be resolved using a weighting or ranking system. More than one set of corrections can be determined and a user or a machine learning system can be used to select the one set of corrections to use as the results to be communicated and applied to the drilling operation plan or a borehole system, such as a geo-steering system.
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
Processes to receive user input parameters and system input parameters associated with a borehole undergoing active drilling operations to continually update drilling directions with wholistically applied optimizations to bring the actual borehole trajectory closer to the planned borehole trajectory. The processes can project ahead of the drilling assembly to determine the actual trajectory of the borehole and generate corrections to reduce the gap between the actual and planned trajectory paths. Various optimizations can be applied to the corrections to avoid overstressing systems or reducing the borehole productivity. Conflicts between optimizations can be resolved using a weighting or ranking system. More than one set of corrections can be determined and a user or a machine learning system can be used to select the one set of corrections to use as the results to be communicated and applied to the drilling operation plan or a borehole system, such as a geo-steering system.
E21B 41/00 - Equipment or details not covered by groups
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
84.
LEARNING HYDROCARBON DISTRIBUTION FROM SEISMIC IMAGE
The disclosure relates to determining rock properties of subterranean formations and learning the distribution of hydrocarbons in the formations. A geometrical element spread function is disclosed that quantifies distortion of the geology as seen by the geophysicists who process seismic images of the subterranean formations. A method of determining the rock properties using the seismic images and synthetic images is provided. In one example, the method includes: (1) obtaining seismic data from a subterranean formation using a seismic acquisition system, (2) generating one or more seismic images of the subterranean formation using the seismic data, (3) creating one or more synthetic images from the one or more seismic images, and (4) determining rock properties of the subterranean formation based on the one or more seismic images and the one or more synthetic images.
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
85.
REAL-TIME DRILLING OPTIMIZATION IN A METAVERSE SPACE
A system can be used for optimizing a wellbore operation via a metaverse space that can include one or more avatars. The system can provide access to the metaverse space for an entity. The metaverse space can be a computer-generated representation of a location relating to a wellbore operation. The system can receive, via an avatar in the metaverse space, a query from the entity relating to the wellbore operation. The avatar can include software applications for performing tasks in the metaverse space. The system can execute, via the avatar, a request to a micro-service for at least one solution parameter based on the query. The request can cause the micro-service to generate the at least one solution parameter. The system can receive the at least one solution parameter from the micro-service. The system can output the at least one solution parameter for adjusting the wellbore operation.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06F 21/30 - Authentication, i.e. establishing the identity or authorisation of security principals
E21B 41/00 - Equipment or details not covered by groups
86.
TRIP MAP FOR ADJUSTING A TRIPPING OPERATION IN A WELLBORE
A system can generate a trip map for adjusting a tripping operation in a wellbore. The system can receive input data from a downhole tool in a wellbore. The system can determine parameters for the tripping operation. The system can determine an overall condition for an interval of the wellbore based on the parameters. The system can determine a status for the parameters and for the overall condition based on a difference between the parameters or the overall condition and a corresponding optimized value. The system can generate a trip map using the parameters and the overall condition. The trip map can include a background shape and a polygon that can be positioned on the background shape. The polygon can include corners corresponding to the parameters and overall condition that are positioned angularly around the background. The trip map can be output to adjust the tripping operation.
G01V 99/00 - Subject matter not provided for in other groups of this subclass
G01V 3/18 - Electric or magnetic prospecting or detectingMeasuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging
E21B 41/00 - Equipment or details not covered by groups
87.
ANALYZING BOREHOLE PATHS USING STRATIGRAPHIC TURNING POINTS
The disclosure presents processes to determine turning points in stratigraphy (TPS) which can be used to improve the representation of the borehole path in relation to layers of the subterranean formation. The TPS can be determined by analyzing each directional survey point in relation to the nearest layer of the subterranean formation. In determining which layer is the nearest layer, the process can analyze the layer type, such as conformable or unconformable, whether a fault intersects the borehole, the angle of the layer in relation to the borehole path, or whether the true stratigraphic thickness (TST) changes from one of a positive parameter or negative parameter to the other. The generated TPS can be used by a system as input or can be displayed for a user where the segmented borehole path can be aligned using the calculated TST to improve the ability of the user to analyze the representation.
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
A system can generate a trip map for adjusting a tripping operation in a wellbore. The system can receive input data from a downhole tool in a wellbore. The system can determine parameters for the tripping operation. The system can determine an overall condition for an interval of the wellbore based on the parameters. The system can determine a status for the parameters and for the overall condition based on a difference between the parameters or the overall condition and a corresponding optimized value. The system can generate a trip map using the parameters and the overall condition. The trip map can include a background shape and a polygon that can be positioned on the background shape. The polygon can include corners corresponding to the parameters and overall condition that are positioned angularly around the background. The trip map can be output to adjust the tripping operation.
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
89.
OPTIMIZING DRILLING PARAMETERS FOR CONTROLLING A WELLBORE DRILLING OPERATION
A system can receive input data indicating a current state of a wellbore drilling operation. The system can determine, by a set of software applications, constraints associated with the wellbore drilling operation. The system can optimize, by an optimization model and using the input data, a drilling parameter subject to the constraints associated with the wellbore drilling operation. The system can output the optimized drilling parameter for controlling the wellbore drilling operation.
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 43/30 - Specific pattern of wells, e.g. optimising the spacing of wells
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 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
90.
DETERMINING CELL PROPERTIES FOR A GRID GENERATED FROM A GRID-LESS MODEL OF A RESERVOIR OF AN OILFIELD
A system can receive a grid-less point cloud model of a geological formation, the grid-less cloud point model that includes data points. The system can determine, by a machine-learning model for clustering data points, clusters for the data points according to a heterogeneity index. The system can determine an outline for each cluster. The system can generate a grid corresponding to the geological formation, the grid comprising a plurality of cells for each cluster of the plurality of clusters, each cluster having cell properties. The system can output the grid for the geological formation to a graphical user interface, the grid usable for executing a flow simulation at the graphical user interface.
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
91.
DETERMINING CELL PROPERTIES FOR A GRID GENERATED FROM A GRID-LESS MODEL OF A RESERVOIR OF AN OILFIELD
A system can receive a grid-less point cloud model of a geological formation, the grid-less cloud point model that includes data points. The system can determine, by a machine-learning model for clustering data points, clusters for the data points according to a heterogeneity index. The system can determine an outline for each cluster. The system can generate a grid corresponding to the geological formation, the grid comprising a plurality of cells for each cluster of the plurality of clusters, each cluster having cell properties. The system can output the grid for the geological formation to a graphical user interface, the grid usable for executing a flow simulation at the graphical user interface.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
92.
Optimizing drilling parameters for controlling a wellbore drilling operation
A system can receive input data indicating a current state of a wellbore drilling operation. The system can determine, by a set of software applications, constraints associated with the wellbore drilling operation. The system can optimize, by an optimization model and using the input data, a drilling parameter subject to the constraints associated with the wellbore drilling operation. The system can output the optimized drilling parameter for controlling the wellbore drilling operation.
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
93.
EMISSIONS ESTIMATIONS AT A HYDROCARBON OPERATION LOCATION USING A DATA-DRIVEN APPROACH
A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.
A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.
The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.
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 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
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 30/20 - Design optimisation, verification or simulation
The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.
A system can receive a sustainability target for a level of assessment for a hydrocarbon operation. The system can receive actual data for an activity associated with the hydrocarbon operation. The system can generate a sustainability metric based on the actual data and one or more parameters of the activity. The system can generate, by at least one algorithm, a predicted sustainability state for the level of assessment at a subsequent point in time based on the sustainability metric, the actual data, and the one or more parameters of the activity. The system can generate a recommendation for at least one action based on the predicted sustainability state and the sustainability target for the hydrocarbon operation. The system can output the recommendation for the at least one action for adjusting the activity of the hydrocarbon operation.
The disclosure provides an automated process for determining the wear condition of a downhole tool that removes the subjectivity associated with manual observation. The automated process can advantageously evaluate a wear condition of a downhole tool using visual analytics and real-time analysis after the downhole tool has been extracted from the wellbore. An example of a method includes: (1) securing a downhole tool in a rig assembly, (2) obtaining, using sensors, surround tool data of the downhole tool in the rig assembly, wherein the surround tool data includes a first set of surround tool data obtained before a downhole operation by the downhole tool and a second set of surround tool data obtained after the downhole operation, and (3) automatically determining a wear condition of the downhole tool in real time by comparing the second set of surround tool data to the first set of surround tool data.
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
Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.
