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
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 spread; Correlating seismic signals; Eliminating effects of unwanted energy
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 systems; Systems 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 windows; Deforming of pipes in boreholes or wells; Reconditioning of well casings while in the ground
6.
INFERRING SUBSURFACE KNOWLEDGE FROM SUBSURFACE INFORMATION
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.
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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
8.
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
9.
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
10.
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 detecting; Measuring 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
11.
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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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 walls; Formation testing; Methods 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. optimizing 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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
13.
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
14.
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"
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.
A system can determine a heterogeneity and a score for a reservoir for optimizing a drilling location. The system can receive a wireline log associated with a well that is positioned in a subterranean formation that includes a reservoir. The system can determine, using the wireline log, at least one statistical parameter for an interval of the well. The system can determine, using the at least one statistical parameter, a vertical heterogeneity of the reservoir. The system can determine, using the vertical heterogeneity, a score associated with the reservoir. The score can indicate an extraction difficulty and a carbon intensity of the reservoir. The system can output the score for optimizing a drilling location.
E21B 43/30 - Specific pattern of wells, e.g. optimizing 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 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
E21B 41/00 - Equipment or details not covered by groups
20.
MODELING A KARST FORMATION FOR A WELLBORE OPERATION
A system can model a karst formation for controlling a wellbore operation. The system can receive first input data that includes a set of fracture properties in a fracture network of a subterranean formation. The system can receive second input data that includes a set of point sets from a fracture geometry of the fracture network. The system can generate a set of fracture skeletons from the first input data and the second input data. The system can model a karst feature based on the plurality of fracture skeletons. The system can output the karst feature for controlling a wellbore operation.
The disclosure addresses the existing gap in tubular designs and monitoring of tubulars in wellbores by considering high temperature, cyclic thermal loading effects. An example method of designing tubular for use in a well is provided that includes: (1) receiving a well configuration for a well and at least one type of well operation for the well, (2) receiving a selection of a tubular for use in the well, (3) generating a temperature history and a pressure history for the well using the well configuration, the selection of the tubular, the at least one type of well operation, and one or more simulators, and (4) determining, using the temperature history and the pressure history, a derated strength of the tubular based on one or more effects of high temperature, cyclic thermal loadings on the tubular.
Some implementations relate to a method for parallelizing, by a geological data system, operations of a geostatistical simulation for a well data set via a plurality of processing elements (PEs). The method may include determining a reservoir area for the well data set. The method may include determining a set of turning band lines for the reservoir area. The method may include dividing the reservoir area into a plurality of tiles, each tile including a respective subset of the set of turning band lines. The method may include assigning at least one of the tiles to each of the PEs. The method may include determining, in parallel for each tile, intermediate results with respect to each respective subset of turning band lines. The method may include aggregating the intermediate results to form a final result of the geostatistical simulation.
Systems and methods for machine learning (ML) assisted parameter matching are disclosed. Wellsite data is acquired for one or more existing production wells in a hydrocarbon producing field. The wellsite data is transformed into one or more model data sets for predictive modeling. A first ML model is trained to predict well logs for the existing production well(s), based on the model data set(s). A first well model is generated to estimate production of the existing production well(s) based on the predicted well logs. Parameters of the first well model are tuned based on a comparison between the estimated and an actual production of the existing production well(s). A second ML model is trained to predict parameters of a second well model for a new production well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model.
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 41/00 - Equipment or details not covered by groups
System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.
Systems and methods for completion design are disclosed. Wellsite data is acquired for one or more existing production wells. The wellsite data is transformed into model data sets for training a first machine learning (ML) model to predict well logs. A first well model uses the well logs to estimate production of the existing well(s). Parameters of the first well model are tuned based on a comparison between the estimated and actual production of the existing well(s). A second ML model is trained to predict parameters of a second well model for a new well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model. Completion costs for the new well are estimated based on the well's completion design parameters and the forecasted production. Completion design parameters are adjusted, based on the estimated completion costs and the forecasted production.
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 47/008 - Monitoring of down-hole pump systems, e.g. for the detection of "pumped-off" conditions
E21B 43/16 - Enhanced recovery methods for obtaining hydrocarbons
Drilling parameters for a wellbore operation can be determined based on resonance speeds. For example, a system can receive real-time data for a drilling operation that is concurrently occurring with receiving the real-time data. The system can determine, for a drilling depth, a rotations-per-minute (RPM) value corresponding to a resonance speed based on a weight-on-bit (WOB) value and the real-time data. The system can generate a plot of the WOB value and the RPM value corresponding to the resonance speed. The system can determine drilling parameters for the drilling operation based on the plot. The drilling parameters can exclude, for the WOB value, the RPM value corresponding to the resonance speed.
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
E21B 44/04 - Automatic control of the tool feed in response to the torque of the drive
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
The disclosure presents processes to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS, e.g., seismic data-CRS match, can be used as input to a user system or a computing system, such as a borehole operation system.
The disclosure presents processes for evaluating a borehole design against one or more identified risks. The processes can determine borehole design concepts for the borehole design. Each borehole design concept can have multiple risks assigned, which can be selected from a library of risks, a risk matrix or template, a risk model, or user entered risks. The risks can be scored using one or more statistics-based algorithms, such as a sum, an average, a mean, or other algorithms. The risks can be grouped by a risk level, forming a sub-risk score for each risk level for each borehole design concept. A final risk score can be generated using the sub-risk scores for the borehole design. More than one borehole design can be evaluated using a risk tolerance parameter and the borehole design that satisfies the risk tolerance parameter can be selected as the recommended borehole design.
E21B 43/30 - Specific pattern of wells, e.g. optimizing 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 41/00 - Equipment or details not covered by groups
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 spread; Correlating seismic signals; Eliminating effects of unwanted energy
A system can be provided for applying a dynamic filter to a velocity model for converting the domain of seismic data. The system can receive a velocity model for a geological area of interest. The system can apply a dynamic filter to the velocity model for smoothing an anomaly included in the velocity model. The system can apply the velocity model with the smoothed anomaly to seismic data associated with the geological area of interest for converting the domain of the seismic data.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
31.
DETERMINING FAULT SURFACES FROM FAULT ATTRIBUTE VOLUMES
Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
G01V 1/40 - Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
32.
DETERMINING CHARACTERISTICS OF FLUID LOSS IN A WELLBORE
A system can provide for determining characteristics loss in a wellbore. The system can include a processor and a non-transitory memory with instructions that are executable by the processor for causing the processor to execute operations. The operations can include receiving, from sensors in a wellbore, data corresponding to loss indicators. The operations can include determining a loss probability for each loss indicator. The operations can include determining a total loss probability of fluid loss in the wellbore based on the loss probabilities. The operations can include outputting the total loss probability to be used in a drilling operation in the wellbore.
E21B 47/10 - Locating fluid leaks, intrusions or movements
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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 43/26 - Methods for stimulating production by forming crevices or fractures
A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
34.
CONTEXTUALIZATION OF GEOSCIENTIFIC DATA USING GEOLOGICAL AGE FRAMEWORK
The disclosure presents processes to improve the ability to analyze geological information for an area or region of interest. A user can specify one or more input files, such as from public, private, or proprietary sources. The user can specify a geological or geographic framework to utilize. The process can then perform a matching between the data in the input files and the data in the framework. The matching process can utilize a geological matching using a specified range of depths or a geographical matching followed by the geological matching. Other parameters can be utilized such as a radius to define an area of interest around a central location of interest. Matched data elements can have geological attributes from the geological framework data linked to data elements in the input files. The input files can be downloaded, displayed, printed, or communicated to another computing system or program for further analysis
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
E21B 41/00 - Equipment or details not covered by groups
A location of a cut and an amount of force to be used in a pull operation for a plug & abandonment (P&A) operation can be determined. Measurements of at least one characteristic of fluids and solids disposed in an annulus defined between a casing and a wall of a wellbore can be received. A total fluid and solid friction force drag can be determined using hydrostatic force that is determined from the measurements. A mechanical friction force drag can be determined based on a weight of the casing. The mechanical friction force drag and the total fluid and solid friction force drag can be used to determine a friction factor. The friction factor can be used to determine a depth location at which to cut the casing and a pull force for pulling the casing from the wellbore in the P&A 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
A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.
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
37.
CALIBRATION OF DRILLSTRING WEIGHT FOR FRICTION FACTOR ESTIMATION
A method comprises determining a value of at least one oppositional force for a drillstring at multiple depths in the wellbore, determining a value of hook load for the drillstring at the multiple depths based on the value of the at least one opposition force at the multiple depths, and determining a calibrated drillstring weight based on a change in the value of the hook load over the multiple depths of the wellbore. The change in the value of the hook load can be determined based on a change in a measured hook load and/or a change in an estimated hook load. From the calibrated drillstring weight, an adjusted estimated hook load can be determined.
A method includes acquiring historical well construction data associated with a set of historical wells. The method also includes developing a well construction model using the corpus of historical well construction data. Additionally, the method includes acquiring real-time well construction data during a well construction operation and applying the well construction model to the real-time well construction data to identify changes to a well construction parameter. Further, the method includes outputting a command to update the well construction operation using the changes to the well construction parameter.
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
G01V 1/40 - Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
39.
CALIBRATION OF DRILLSTRING WEIGHT WITH DRAG FOR FRICTION FACTOR ESTIMATION
A method comprises determining a value of at least one oppositional force for a drillstring at multiple depths in the wellbore, determining a value of a drag force for the drillstring at the multiple depths, determining a value of hook load for the drillstring at the multiple depths based on the value of the at least one opposition force and the value of the drag force at the multiple depths, and determining a calibrated drillstring weight based on a change in the value of the hook load over the multiple depths. From the calibrated drillstring weight, an adjusted estimated hook load can be determined. The drag force can be calculated based on a drag per centralizer and the number of centralizers in the wellbore. A centralizer friction factor can be determined and used to calibrate the value of the drag per centralizer.
The disclosure presents apparatuses and systems to reduce drag and friction forces on a drill string located downhole a borehole. The drill string can have two or more movement isolators to allow a movement sensitive tool to be movement isolated from other portions of the drill string that have powered movement. The other drill string portions can be powered by a surface equipment or by a downhole movement motor attached to the drill string, such as a rotational mud motor, an agitator, a jar motor, or a rotary steerable. Portions of the drill string located further downhole than the movement sensitive tool can utilize a movement motor attached to the drill string to provide movement to reduce drag and friction force where the movement isolators can reduce the movement force experienced by the movement sensitive tool.
A method for performing wellbore correlation across multiple wellbores includes predicting a depth alignment across the wellbores based on a geological feature of the wellbores. Predicting a depth alignment includes selecting a reference wellbore, defining a control point in a reference signal of a reference well log for the reference wellbore, and generating an input tile from the reference signal, the control points, and a number of non-reference well logs corresponding to non-reference wellbores. The well logs include changes in a geological feature over a depth of a wellbore. The input tile is input into a machine-learning model to output a corresponding control point for each non-reference well log. The corresponding control point corresponds to the control point of the reference log. Based on the corresponding control points output from the machine-learning model, the non-reference well logs are aligned with the reference well log to correlate the multiple wellbores.
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 presents solutions for determining a casing wear parameter. Image collecting or capturing devices can be used to capture visual frames of a section of drilling pipe during a trip out operation. The visual frames can be oriented to how the drilling pipe was oriented within the borehole during a drilling operation. The visual frames can be analyzed for wear, e.g., surface changes, of the drilling pipe. The surface changes can be classified as to the type, depth, volume, length, shape, and other characteristics. The section of drilling pipe can be correlated to a depth range where the drilling pipe was located during drilling operations. The surface changes, with the depth range, can be correlated to an estimated casing wear to generate the casing wear parameter. An analysis of multiple sections of drilling pipe can be used to improve the locating of sections of casing where wear is likely.
An ensemble of machine learning models is trained to evaluate seismic and risk -related data in order to evaluate, value, or otherwise rank various prospective hydrocarbon reservoir ("prospects") of a field. A classification machine learning model is trained to classify a prospect or region of a prospect based on the exploration risk level. From the seismic data, a frequency-filtered volume (FFV) for each prospect is calculated, where the FFV is a measure of reservoir volume which takes into account seismic resolution limits. Based on the risk classification and FFV, prospects of the field are ranked based on their economic value which is a combination of the risk associated with drilling and their potential reservoir volume.
The disclosure presents processes and methods for determining an overpull force for a stuck drill string in a borehole system. The fluid composition of a mud in the borehole at a specified depth can be broken down into a percentage of liquid and percentage of solids, as well as adjusting for material sag and settling factors. The fluid composition can be utilized to identify friction factors and drag in respective fluid composition zones. Each friction factor and drag can be summed to determine a total fluid drag on the drill string. In some aspects, the total fluid drag can be adjusted utilizing the relative positioning of casing collars and tool joints. The total fluid drag can be summed with the other force factors, such as a shear force and mechanical drag. The total drag can then be utilized as the overpull force applied to the stuck drill string.
E21B 44/04 - Automatic control of the tool feed in response to the torque of the drive
E21B 47/18 - 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 using acoustic waves through the well fluid
E21B 47/007 - Measuring stresses in a pipe string or casing
Hybrid computing that utilizes a computer processor coupled to one or more graphical processing units (GPUs) is configured to perform computations that generate outputs related to reservoir simulations associated with formations that may include natural gas and oil reservoirs.
A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration 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
In contrast to existing methods wherein derived horizons are interpreted in isolation, the disclosure provides a process that does not interpret patches themselves but determines the relationships between patches, in order to associate and link patches to derive a holistic geological interpretation. Predefined patches, such as from a pre-interpreted suite, are received as inputs to determine the relationships and derive an interpretation for a complete volume. In one aspect the disclosure provides an automated method of generating a geological age model for a subterranean area. In one example, the automated method includes: (1) abstracting seismic data of a subsurface into a limited number of patches, (2) abstracting the patches by defining patch-links between the patches, and (3) generating a geological age model of the subsurface by solving for the relative geological age of each of the patches using the patch-links.
A system is disclosed for detecting a problem associated with a drilling operation based on the properties of a formation cutting. The system can include a camera for generating an image of the formation cutting extracted from a subterranean formation. The system can include one or more sensors for detecting one or more characteristics of the subterranean formation or a well tool. The system can provide the image as input to a first model for determining one or more properties of the formation cutting based on the image. The system can provide the one or more properties and the one or more characteristics as input to a second model for detecting a downhole problem associated with the drilling operation. The system can transmit an alert indicating the downhole problem and optionally a recommended solution to a user.
E21B 21/06 - Arrangements for treating drilling fluids outside the borehole
E21B 21/01 - Arrangements for handling drilling fluids or cuttings outside the borehole, e.g. mud boxes
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 47/01 - Devices for supporting measuring instruments on drill bits, pipes, rods or wirelines; Protecting measuring instruments in boreholes against heat, shock, pressure or the like
A system may include a processing device and a memory device that includes instructions to receive real-time data including wellhead pressure, a new sand measurement, and a new erosion rate for a wellbore. A model including an available reference sand rate for the wellbore based on the wellhead pressure and at least one of the new sand measurement or the new erosion rate of the wellbore may be calibrated. The model may be applied to determine a calibrated sand rate is within a pre-determined threshold. A new sand production rate for the wellbore based on the model may be determined.
Process-mining software is disclosed for generating a process flow for forming a wellbore at a wellsite. The process-mining software can receive data from sensors at a wellsite. The process-mining software can determine wellbore operations performed at the wellsite, based on the received data, using a predefined algorithm. The process-mining software can generate an event log based on the determined wellbore operations. The process-mining software can then generate a process flow based on the event log. The process-mining software can output the process flow for use in forming one or more wellbores.
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
i.ei.e., when the drilling bit is drilling in the borehole. The method disclosed herein incorporates both physics based as well as machine learning based aspects to provide existing and forecasted evaluations. In one example a method of evaluating properties of a drilling bit when in a borehole is disclosed that includes: (1) determining formation properties corresponding to a subterranean formation at a location of the drilling bit in the borehole, (2) calculating an existing bit wear condition of the drilling bit based on the formation properties, (3) providing a forecasted bit wear condition of the drilling bit based on the existing bit wear condition and real time parameters, and (4) evaluating performance of the drilling bit based on the forecasted bit wear condition.
A method comprises determining a measure of drilling efficiency, such as a friction factor or mechanical specific energy, of a drill bit used in a drilling operation of a wellbore and performing video analytics of at least one video that includes a substantially complete view of the wear surfaces of a drill bit to determine drill bit wear of the drill bit that is a result of the drilling operation of the wellbore. The method includes determining a cause of the drill bit wear based on the measure of drilling efficiency and the drill bit wear determined by performing video analytics. Based on correlation or modeling of drill bit wear and the measure of drilling efficiency, drill bit wear can be predicted and some types of drilling dysfunction mitigated in subsequent drilling runs.
E21B 41/00 - Equipment or details not covered by 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
The disclosure presents processes and methods for determining a packoff event at a location in a borehole undergoing a drilling operation. The packoff event can be represented by a packoff risk indicator (PRI) that presents, for example, a percentage risk of the packoff event occurring. The PRI can be utilized to initiate a remediation operation prior to the packoff event becoming more severe, such as a stuck drill string. In some aspects, the generation of the PRI can utilize an uncertainty model to provide a range of input parameters and an uncertainty parameter used by other systems to evaluate the risk of the potential packoff event has on borehole operations. In some aspects, the generation of the PRI can utilize machine learning algorithms or deep neural network algorithms to pre-process the input parameters to improve the accuracy of the PRI and of the models used to generate the PRI.
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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
54.
HYBRID COLLAPASE STRENGTH FOR BOREHOLE TUBULAR DESIGN
The disclosure presents processes for improving the design phase of tubular structures to be used downhole in a borehole. A hybrid collapse strength model can be utilized that uses a linear collapse strength model for an initial percentage range based on the initial wall thickness of the tubular structure. A standards collapse strength model can be used once a wall thickness threshold is not satisfied. In some aspects, a transition collapse strength model can be used prior to the standards collapse strength model to avoid discontinuities in the analysis. The hybrid collapse strength model can enable more efficient use of tubular structures, designing a longer operational lifetime, or the use of thinner structures while maintaining a satisfactory operational lifetime. Lower operational costs of the borehole can be achieved through using less expensive tubular structures and through a reduction of costs associated with replacing a section of casing within 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
55.
STRESS ANALYSIS FOR PLASTIC MATERIAL LINED TUBULAR STRUCTURES FOR BOREHOLES
e.ge.g., wall thickness parameters. A thermal model can be applied to the tubular structure to determine pressure and temperature parameters. The strength model and the thermal model outputs can be utilized by a stress analyzer to determine loads, safety factors, and design limit parameters. The plastic material lined tubular structure model can enable more efficient use of tubular structures, designing a longer operational lifetime, such as in acidic environments, or the use of thinner structures while maintaining a satisfactory operational lifetime.
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
56.
ESTIMATING RESERVOIR PRODUCTION RATES USING MACHINE LEARNING MODELS FOR WELLBORE OPERATION CONTROL
A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.
An intelligent data management system leverages heterogeneous database technologies and cloud technology to manage data for reservoir simulations across the lifetime of a corresponding energy asset(s) and facilitates access of that data by various consumers despite changing compute platforms and adoption of open source paradigms. The intelligent data management system identifies the various data units that constitute a reservoir simulation output for storage and organization. The intelligent data management system organizes the constituent data units across a file system and object database based on correspondence with different simulation run attributes: project, study, and model. The intelligent data management system also learns to specify or guide configuration of simulation runs.
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
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The disclosure presents processes and methods for determining an adjusted drag friction factor, where the adjusting utilizes a hole cleaning function. In some aspects, the drag friction factor utilizes viscous drag. In some aspects, the drag friction factor utilizes viscous torque. In some aspects, the drag friction factor can be utilized to determine one or more decomposed friction factors. The decomposed friction factors or the adjusted drag friction factor can be utilized in a friction processor to improve the efficiency of borehole operations. The hole cleaning function can utilize various parameters, for example, a cuttings density, a cuttings load, a cuttings shape, a cuttings size, a deviation, a drill pipe rotation rate, a drill pipe size, a flow regime, a hole size, a mud density, a mud rheology, a mud velocity, a pipe eccentricity, and other parameters. A system is disclosed that is capable of implementing the processes and methods.
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 drilling data analytics engine disclosed herein automatically corrects drilling data with predictive modeling. A drilling data quality analyzer segregates drilling data into good drilling data and bad drilling data that has missing, incomplete, or incorrect entries. For each bad data entry in the bad drilling data, the drilling data analytics engine preprocess drilling data attribute values for the corresponding task not including the drilling data attribute value for the bad data entry and inputs the preprocessed drilling data attribute values into a trained predictive model. The trained predictive model is trained on good drilling data to estimate values for the drilling attribute corresponding to the bad data entry.
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
60.
DRILLING DATA CORRECTION WITH MACHINE LEARNING AND RULES-BASED PREDICTIONS
A drilling data correction system corrects drilling data entries in high-importance drilling data segments using machine learning and rules-based drilling models. A data importance analyzer identifies high-importance data segments in incoming drilling data. The drilling data correction system inputs features of drilling data into machine learning drilling models and rules-based drilling models trained to predict the high-importance data segments. Predictions from the machine learning drilling models and rules-based drilling models are presented to a user based on drilling data prediction criteria. The machine learning drilling data predictions are used to automatically correct the high-importance data segments, or the user chooses between machine learning drilling data predictions and rules-based drilling data predictions to correct the high-importance drilling data segment.
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
61.
RESERVOIR CHARACTERIZATION USING MACHINE-LEARNING TECHNIQUES
A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.
An apparatus for processing seismic data variables comprising a tracking module and an interpretation module. The tracking module selects groupings of subsurface data variables from the seismic data variables, selects a subsurface data variable for each grouping, and determines an isochron variable for each subsurface data variable for each grouping. Each grouping of subsurface data variables has spatial coordinates values. The interpretation module predicts a horizon variable for each grouping using the isochron variable and an algorithmic model or trained algorithmic. The interpretation module predicts a horizon variable using the isochron variable for each grouping and a trained algorithmic model. The tracking module selects the subsurface data variable for each grouping based on a peak, trough or zero-crossing identified in the grouping. The trained algorithmic model uses multivariate classification or multivariate linear regression analysis using the isochron variables and associated seismic data variables against a dataset to predict the horizons.
A system can output a graphical user interface for use in planning or performing a wellbore operation. The system can receive a location of a geological point location of interest for subterranean exploration and a geological time-frame for the geological point location of interest. The system can determine present-day data about the geological point location of interest from the received location. The system can generate a pseudo-well and reconstruct geological-historical parameters in separate time-intervals based on the received location, plate-tectonic models, and paleo-geographic datasets. The system can generate a graphical user interface including present-day data, paleo-geographic data, plate-tectonic data, and plate-interaction data. The system can output the graphical user interface for use in planning or performing a wellbore operation to extract hydrocarbon fluid.
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
G01V 1/40 - Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
64.
AUTOMATED HORIZON LAYER EXTRACTION FROM SEISMIC DATA FOR WELLBORE OPERATION CONTROL
A method includes receiving a seismic data volume comprising seismic information of subterranean formations and receiving a set of seismic traces of the seismic data volume. The method also includes, determining, along each seismic trace of the set of seismic traces, a set of seed points comprising minimum or maximum onsets. Further, the method includes sorting the set of seed points into a sorted set of seed points by absolute amplitude values of the set of seed points. Furthermore, the method includes generating a horizon representation of every seismic event in the seismic data volume by automatically tracking horizons throughout an entirety of the seismic data volume from the sorted set of seed points in an order of the absolute amplitude values of the sorted set of seed points. Additionally, the method includes generating a graphical user interface that includes the horizon representation for display on a display device.
G01V 1/36 - Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
65.
BOUNDARY LINE GENERATION FOR CONTROLLING DRILLING OPERATIONS
A system can generate and output boundary lines for controlling a drilling operation. The system can receive data, including an offset well surveys, measuring instrument information, and a well casing diameter, about offset wells in a subterranean formation. The system can determine reference well values. The system can generate boundary lines for the offset wells based on the received data and the calculated reference well values. The system can adjust the boundary lines, and can output the adjusted boundary lines for controlling a drilling operation.
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 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
66.
GEOLOGICAL PROPERTY MODELING WITH NEURAL NETWORK REPRESENTATIONS
A neural network trainer trains neural networks to estimate secondary data at locations throughout a geological formation where secondary data is unknown. The neural networks are trained to estimate secondary data using locations in the geological formation as input. Subsequently, the secondary data is deleted from memory using the trained neural network as a proxy representation to reduce memory footprint and allow for estimation of secondary data at locations where it is unknown.
Methods and systems for determining wellpath tortuosity are disclosed. In accordance with an embodiment, a tortuosity of a borehole segment is determined and a tortuosity of a casing associated with the borehole segment is determined based, at least in part, on the tortuosity of the borehole segment and a path conformity characteristic of the casing. A tortuosity variation factor is generated based on a value of the tortuosity of the casing relative to a value of the tortuosity of the borehole segment.
E21B 47/007 - Measuring stresses in a pipe string or casing
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods 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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
68.
GEOLOGICAL DATABASE MANAGEMENT USING SIGNATURES FOR HYDROCARBON EXPLORATION
A system can determine an analogue geological feature. The system may generate, by extracting parameter signatures for geological features, a database including parameters about geological features associated with parameter signatures. The system may receive data including parameters and a feature-type about a geological feature of interest. The system may generate a signature including values for a subset of the feature-of-interest parameters selected based on the geological feature of interest for the feature-of-interest using the data. The system may execute a comparison of the feature signature to the parameter signatures included in the database for identifying an analogue geological feature for the feature of interest. The system may output a subset of parameters for the analogue for use in subterranean exploration.
The disclosure presents processes and methods for decomposing friction factors and generating a calibrated friction factor and adjusted input parameters. The calibrated friction factor and adjusted input parameters can be utilized by a borehole system as an input to adjust borehole operations to improve the operational efficiency. The friction factors can be decomposed by type, such as geometrical, geomechanical, mechanical, and fluid. The disclosure also presents processes and methods for identifying an outlier portion of a friction factor, as identified by a deviation threshold that can be used to identify adjustments to borehole operations in that portion of the borehole. A system is disclosed that is capable of implementing the processes and methods in a borehole operation system, such as a downhole system, a surface system, or a distant system, for example, a data center, cloud environment, lab, corporate office, or other location.
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
70.
SUBSURFACE FLUID-TYPE LIKELIHOOD USING EXPLAINABLE MACHINE LEARNING
A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.
The disclosure presents processes and methods for utilizing one or more micro-services to generate a calibration of a factor of a borehole operation or to generate an optimization adjustment to the borehole operation. The micro-services selected for execution can be selected by an optimization workflow, where each type of borehole operation can have its own set of micro-services. The micro-services can be part of one or more computing systems, such as a downhole system, a surface system, a well site controller, a cloud service, a data center service, an edge computing system, other computing systems, or various combinations thereof. Also disclosed is a system for implementing micro-services on one or more computing systems to enable a light weight and fast response, e.g., real-time or near real-time response, to borehole operations.
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
G01V 1/40 - Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
Systems and methods to assess formation data are disclosed. The method includes partitioning a formation containing a plurality of rock types into a plurality of sections. For a section of the plurality of sections, the method also includes determining, for each rock type of the plurality of rock types, a probability that the rock type is present in the section. The method further includes assigning a value to the section of the plurality of sections based on a probability that the section contains one or more rock types of the plurality of rock types. The method further includes analyzing the formation based on the value associated with the section.
Systems and methods are provided for a horizon patch extraction process and in particular, to receiving seismic trace data of a plurality of seismic events of a subterranean volume, selecting a first seismic trace based on the seismic trace data of the plurality of seismic events, the first seismic trace including a plurality of seismic onsets, determining a depth, an amplitude, and a first thickness of a first seismic onset of the first seismic trace, determining a second thickness between the first seismic onset and a second seismic onset, determining a third thickness between the first seismic onset and a third seismic onset, and generating a horizon patch based on the depth, the amplitude, and the first thickness of the first seismic onset, the second thickness between the first seismic onset and the second seismic onset, and the third thickness between the first seismic onset and the third seismic onset.
Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.
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 41/00 - Equipment or details not covered by groups
G01V 1/40 - Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.
A system can receive fault likelihood data about a subterranean environment and apply a binary mask filter using a tuning parameter to convert the fault likelihood data to binary distribution data having a plurality of pixels arranged in a plurality of profiles in at least two directions. The system can perform, for each profile of the plurality of profiles, fault skeletonization on the binary distribution data to form fault skeletonization data with pixels connected that represent part of a fracture. The system can convert the fault skeletonization data to seismic volume data and combine and filter the seismic volume data in the at least two directions to form combined seismic volume data. The system can output the combined seismic volume data as an image for use in detecting objects to plan a wellbore operation.
Disclosed embodiments include methods and systems for classifying test data. In one embodiment a method includes determining one or more variable types in a multivariate test vector within a data set, and for a plurality of machine -learning models, determining a closest match between variable types used by (to train) the machine -learning models and the determined variable types for the test vector. In response to determining a closest match for one machine-learning model, a corresponding machine -learning model is selected and the test vector is classified using the selected model. In response to determining a closest match for multiple machine -learning models, a similarity is determined between a probability distribution for the test data set and the probability distributions for the multiple machine- learning models to generate similarity values for each of the models. In response to one of the similarity values exceeding a threshold value, a machine-learning model is selected that corresponds to the exceeding similarity value and the test vector is classified using the selected model.
Vibrations occurring during downhole drilling operations can be predicted and reduced according to some examples. One particular example includes a system that can receive drilling parameter values associated with a drilling operation involving drilling a wellbore through a subterranean formation using a drill string. The system can receive a depth value associated with the drilling parameter values. The system can provide the drilling parameter values as input to a drill string model to receive a critical speed prediction as output from the drill string model. The system can then generate a speed-depth mapping based on the critical speed prediction and the depth value. The speed-depth mapping can be used to avoid the critical speed at the depth in the wellbore, which may prevent a failure of the drill string resulting from associated vibrations.
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access a plurality of seismic images of a subterranean formation in a first geological area. The instructions are also executable to generate a plurality of fault estimates from each of the plurality of seismic images. Further, the instructions are executable to generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates. Additionally, the instructions are executable to generate a statistical fault uncertainty volume of the first geological area using the processed seismic image. Furthermore, the instructions are executable to control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area.
A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.
A system can access geological data describing a plurality of rock types in a physical rock sample drilled from a reservoir. The system can generate synthetic rock samples and execute single phase upscaling to compute absolute permeabilities for the physical rock sample and the synthetic rock samples. The system can execute a first multiphase upscaling based on the single phase upscaling to determine relative permeabilities for the physical rock sample and the synthetic rock samples. The system can compare the relative permeability of the physical rock sample to the relative permeabilities for the synthetic rock samples and select a synthetic rock sample that varies the least from the physical rock sample. The system can perform at least one additional multiphase upscaling on the physical rock sample and the synthetic rock samples to determine a second multiphase upscaling result and to develop a plan for drilling operations.
E21B 49/02 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells by mechanically taking samples of the soil
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
G01V 3/08 - Electric or magnetic prospecting or detecting; Measuring 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
E21B 41/00 - Equipment or details not covered by groups
A system is described for controlling wellbore drilling operations autonomously using satisficing parameters. The system can determine a wellbore-drilling envelope defining a zone for satisficed values of drilling parameters for a drilling operation. The system can receive real-time data for the drilling parameters and can compare the real-time data to the wellbore-drilling envelope. The system can output a command for automatically controlling the drilling operation in response to comparing the real-time data to the wellbore-drilling envelope.
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 systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
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
83.
DETERMINING GAS-OIL AND OIL-WATER SHUT-IN INTERFACES FOR AN UNDULATING WELL
A system can determine a temperature profile based on a prior production temperature profile and a reference start point pressure for a well. The system can virtually divide the well into a plurality of sections including uphill sections and downhill sections. The system can determine a gas-oil interface depth for each section of the plurality of sections from a water-oil ratio and a gas-oil ratio based on the temperature profile and the reference start point pressure. The system can determine an oil-water interface depth for each section of the plurality of sections from the gas-oil ratio and the water-oil ratio based on the temperature profile and the reference start point pressure.
A system includes equipment for at least one of formation of, stimulation of, or production from a wellbore, a processor, and a non-transitory memory device. The processor is communicatively coupled to the equipment. The non-transitory memory device contains instructions executable by the processor to cause the processor to perform operations comprising training a hybrid deep generative physics neural network (HDGPNN), iteratively computing a plurality of projected values for wellbore variables using the HDGPNN, comparing the projected values to measured values, adjusting the projected values using the HDGPNN until the projected values match the measured values within a convergence criteria to produce an output value for at least one controllable parameter, and controlling the equipment by applying the output value for the at least one controllable parameter.
E21B 41/00 - Equipment or details not covered by groups
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
E21B 47/26 - Storing data down-hole, e.g. in a memory or on a record carrier
85.
RESERVOIR FLUID PROPERTY MODELING USING MACHINE LEARNING
System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.
A system can assign a value to one or more sustainability factors for a wellbore operation based on historical data. The system can determine, for each of the one or more sustainability factors, a weight. The system can determine a sustainability index corresponding to a predicted carbon footprint for the wellbore operation based on the weight and the value for each of the one or more sustainability factors. The system can output a command for adjusting the wellbore operation based on the sustainability index.
Aspects and features of a system for providing parameters for shale field configuration include a processor, and instructions that are executable by the processor. The system, using the processor, can receive resource supply data associated with a shale field to be penetrated by a wellbore or wellbores and simulate production from the shale field using the resource supply data to determine constraints and decision variables. The system can optimize a multi-objective function of the decision variables subject to the constraints to produce controllable parameters for operating the shale field. As examples, these parameters may be related to formation or stimulation of the wellbore or wellbores at the shale field site.
A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.
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 41/00 - Equipment or details not covered by groups
Aspects and features of a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus. The message bus can receive current data from a wellbore. The computing device can generate and use a feature-extraction model to provide revised data values that include those for missing data, statistical outliers, or both. The model can be used to produce controllable drilling parameters using highly accurate data to provide optimal control of the drilling tool. The real-time message bus can be used to apply the controllable drilling parameters to the drilling tool.
E21B 49/00 - Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
90.
FACILITATING HYDROCARBON EXPLORATION FROM EARTH SYSTEM MODELS
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access training data of a modern feature of interest from direct observations, remotely determined data, or a combination thereof. The instructions are also executable to compile parameter data from at least one model simulation that impacts the modern feature of interest. The instructions are executable to train a machine-learning model to generate a predictive model that matches the training data of the modern feature of interest using the compiled parameter data as input. Furthermore, the instructions are executable to predict a feature of interest in a past time period using the predictive model and at least one historical model simulation that impacts the feature of interest. Additionally, the instructions are executable to execute a processing operation for facilitating hydrocarbon exploration based on the predicted feature of interest from the predictive model.
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to cause the processor to receive basin data of a target basin including an area of the target basin, a number of exploration wells in the target basin, and a number of discovery wells in the target basin. Additionally, the instructions are executable to cause the processor to provide the basin data as input to a trained machine-learning model to determine a predicted trajectory of exploration efficiency of the target basin. Further, the instructions are executable to cause the processor to, in response to providing the basin data as input to the trained machine-learning model, receive an output from the trained machine-learning model indicating the predicted trajectory of exploration efficiency in the target basin.
A system for analysis and display of hydrocarbon play information according to some aspects determines a probability of source rock occurrence according to source rock age based on a proven play concept. The system can also determine a relative probability of migration for hydrocarbons from a source rock of a proposed petroleum play concept to a reservoir. A relative probability of wellbore success for the proposed play concept can be determined at least in part based on these probabilities. The system can display the relative probability of wellbore success for the proposed play concept, either alone as part of a displayed inventory of proposed hydrocarbon play concepts. The system can produce accurate results that facilitate rapid play concept investigations for hydrocarbon exploration.
G01V 3/38 - Processing data, e.g. for analysis, for interpretation or for correction
G01V 3/18 - Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination or deviation specially adapted for well-logging
G01V 99/00 - Subject matter not provided for in other groups of this subclass
93.
DETERMINING EXPLORATION POTENTIAL RANKING FOR PETROLEUM PLAYS
A system for determining exploration potential ranking for petroleum plays according to some aspects receives geological survey data of a geographical area to be ranked for a future petroleum play. The system generates predicted values based on the geological survey data, each predicted value indicating a probability that a portion of the basin includes a first characteristic. A set of polygons that represent the basin may be generated based on the predicted values. Each polygon represents a contiguous portion of the basin that has a same predicted value. A basin is score is generated by: generating a score for each polygon using the predicted value; and aggregating the score of each polygon of the set of polygons into the basin score. The basin score is displayed for use displaying for use in determining an area in which drilling a wellbore would have a greater probability of success.
System for optimizing operation of an oil and gas well employs multi-objective Bayesian optimization of wellbore parameters to minimize scaling and corrosion. The system may contain instrumentation for measuring temperature, pressure, at least one production parameter and at least one ion concentration of the fluid in the wellbore. The system may also have a processor for performing a calculation procedure to determine an anticipated corrosion rate ("Vbase") and a scaling index ("Is") reflecting a tendency of scale to form in the wellbore based on the measurements provided by the instrumentation, where Vbase and Is are calculated along the length of the wellbore. Based on a selected set of optimization points taken from the calculations of Vbase and Is, the system may control the alkalinity and flow rate of the fluid based on the multi-objective optimization to simultaneously optimize scaling and corrosion.
Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data, and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.
A method for designing a borehole tubular for use in a borehole. The method may include defining tubular sections that make up the borehole tubular, defining a downhole operation that will be conducted using the borehole tubular at a first timestamp, determining loads that will be applied to each of the tubular sections at respective specific depths along the borehole during the downhole operation at the first timestamp, determining a design limit envelope for each of the tubular sections at the first timestamp based on design parameters of the tubular section and the specific depth of the tubular section at the first timestamp, and displaying a three-dimensional (3D) plot of the design limit envelopes of the tubular sections and the loads applied to the tubular sections as a function of depth within the borehole on a display.
Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.
E21B 41/00 - Equipment or details not covered by 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
E21B 47/10 - Locating fluid leaks, intrusions or movements
A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling 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
This disclosure presents methods and systems to perform fairway analysis on a computing system to automate tasks. The automation of the fairway analysis can reduce bias and uncertainty introduced by a user using their own set of assumptions, estimations, and preferred sequencing of rules and algorithms. The described processes can receive initial input parameters describing the area of interest (AOI) and a geological age range. The processes can retrieve appropriate geological and stratigraphic parameters using the initial input parameters. The combined input parameters can then be geoprocessed using age-aware rules and a determined sequence of algorithms and rules to generate synthesized geological data that can be upscaled and transformed into one or more chance maps indicating the presence and effectiveness of various hydrocarbon elements. The chance maps can be amalgamated and processed to produce a prospective map indicating the likelihood of success of further exploration of the specified AOI.
patents
