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.
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.
G06F 30/18 - Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
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 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.
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 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
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.
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
8.
DETERMINING PARAMETERS FOR A WELLBORE OPERATION BASED ON RESONANCE SPEEDS OF DRILLING EQUIPMENT
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 47/12 - 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 45/00 - Measuring the drilling time or rate of penetration
9.
RECOMMENDATION ENGINE FOR AUTOMATED SEISMIC PROCESSING
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.
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 41/00 - Equipment or details not covered by groups
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
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
11.
MACHINE LEARNING ASSISTED PARAMETER MATCHING AND PRODUCTION FORECASTING FOR NEW WELLS
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 49/08 - Obtaining fluid samples or testing fluids, in boreholes or 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
A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.
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
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
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
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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
G01V 1/28 - Processing seismic data, e.g. analysis, for interpretation, for correction
14.
DYNAMIC FILTER FOR SMOOTHING VELOCITY MODEL FOR DOMAIN-CONVERTING SEISMIC DATA
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.
A method includes retrieving an unstructured document and defining an area of interest of the unstructured document that visually represents geological formation information. The method also includes extracting a set of vectorized polygons from the area of interest. Additionally, the method includes assigning properties from the unstructured document to each of the vectorized polygons in the set of vectorized polygons. Further, the method includes assigning a coordinate reference frame to the set of vectorized polygons and generating a user-interactive document from the set of vectorized polygons.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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.
Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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.
Aspects of the disclosed technology provide techniques for determining frictional forces bearing on a downhole drill string. In some implementations, a method of the disclosed technology can include steps for segmenting a plurality of continuous nodes of the drilling string into a first segment and a second segment, computing a first set of values corresponding with one or more nodes in the first segment using a first model, computing a second set of values corresponding with one or more nodes in the second segment using a second model, and determining a torque of the drill string based on the first set of values and the second set of values. In some aspects, the method can further include steps for determining a drag force on the drill string based on the first set of values and the second set of values. Systems and machine-readable media are also provided.
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 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 47/007 - Measuring stresses in a pipe string or casing
21.
DIFFUSION FLUX INCLUSION FOR A RESERVOIR SIMULATION FOR HYDROCARBON RECOVERY
A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.
G06F 30/20 - Design optimisation, verification or simulation
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
G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
E21B 43/16 - Enhanced recovery methods for obtaining hydrocarbons
E21B 41/00 - Equipment or details not covered by groups
22.
Formation evaluation based on seismic horizon mapping with multi-scale optimization
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.
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.
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.
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 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
E21B 47/09 - Locating or determining the position of objects in boreholes or wells; Identifying the free or blocked portions of pipes
E21B 33/14 - Methods or devices for cementing, for plugging holes, crevices, or the like for cementing casings into boreholes
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 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
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
27.
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.
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.
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
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.
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.
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.
G06N 3/04 - Architecture, e.g. interconnection topology
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
34.
CASING WEAR AND PIPE DEFECT DETERMINATION USING DIGITAL IMAGES
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.
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.
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 lithology prediction that uses a geological age model as an input to a machine learning model. The geological age model is capable of separating and recoding different seismic packages derived from the horizon interpretation. Once the machine learning model has been trained, a validation may be performed to determine the quality of the machine learning model. The quality may be improved by refining the training of the machine learning model. The lithology prediction generated by the machine learning model that utilizes the geological age model provides an improved lithology prediction that more accurately reflects the subterranean formation of an area of interest.
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.
An estimated gas leak flow rate can be determined using a teaching set of concentration profiles, a regression model implemented by a machine-learning subsystem, and a subset of attributes measured within an environment. The teaching set of concentration profiles can include gas flow rates associated with relevant attributes. The regression model can be transformed into a gas leak flow regression model via the machine-learning subsystem using the teaching set. The subset of attributes measured within the environment can be applied to the gas leak flow regression model to determine other attributes absent from the subset of attributes and an estimated gas flow rate for the environment. A gas leak attenuation action can be performed in response to the estimated gas flow rate.
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
E21B 47/117 - Detecting leaks, e.g. from tubing, by pressure testing
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.
The invention concerns a computer implemented method for underbalanced drilling. In one embodiment, the method includes determining a plurality of gas injection rate versus bottom hole pressure curves for a plurality of liquid injection rates for a specified minimum and maximum gas injection rate and a minimum and maximum liquid injection rate. Next, the method determines a set of four interception curves including a minimum motor equivalent liquid rate interception curve, a minimum vertical liquid velocity intercept curve, a minimum horizontal liquid velocity intercept curve, and a maximum motor equivalent liquid rate intercept curve for a specified minimum and maximum mud motor rate range and a minimum horizontal and vertical annulus velocity.
G06F 30/20 - Design optimisation, verification or simulation
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 21/08 - Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure
E21B 41/00 - Equipment or details not covered by groups
42.
GEOLOGICAL FEATURE DETECTION USING GENERATIVE ADVERSARIAL NEURAL NETWORKS
Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.
A computer-implemented method is provided for processing gross depositional environment (GDE) maps. The method includes receiving end-member lowstand systems tract (LST) and maximum flood surface (MFS) gross depositional environment (GDE) maps that represent a particular geographic area at different respective times spaced by a time interval, processing both of the LST and MFS GDE maps in accordance with a predefined set of mles that use geoprocessing operations to relate the content of both the LST and MFS GDE maps, and outputting a transgressive system tract (TST) map based on the processing.
G06F 16/909 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
44.
PROCESSING WELLBORE DATA TO DETERMINE SUBTERRANEAN CHARACTERISTICS
A computer system and method for determining subterranean rock composition is described in which user input data is received having a plurality of parameters defining a desired subterranean rock composition from a wellbore. Data associated with at least one geologic environment is received, which data contains data acquired from at least one wellbore. An analytical analysis is then conducted by a computer processor utilizing the user input data and the received geologic environment data to determine a match between the user desired subterranean rock composition and the received geologic environment data. Output graphic data is then determined and generated, based at least in part on the analytical analysis, on a computer graphical display consisting of a two-dimensional (2D) graphical representation indicating a region of the geologic environment having a match between the user desired subterranean rock composition and the received geologic environment data.
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 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 47/09 - Locating or determining the position of objects in boreholes or wells; Identifying the free or blocked portions of pipes
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
46.
DYNAMIC AND INTERACTIVE SPIRAL-SHAPED GEOLOGICAL TIME SCALES
Displaying a spiral-shaped visualization of a geological time scale according to some aspects may include accessing a time-attributed data set representing a geological time scale of a subterranean region. The geological time scale may be segmented into a hierarchy of intervals (e.g., periods, epochs, and stages). The spiral-shaped visualization may include a path formed in a spiral formation. The path may begin at a center position of the spiral-shaped visualization and may end at an outer portion of the spiral-shaped visualization. The beginning of the path may represent a first time of the geological time scale. The ending of the path may represent a second time of the geological time scale. The spiral-shaped visualization may also be segmented to represent the hierarchy of intervals. Additionally, the spiral-shaped visualization may be interactive. Selecting an interval of the path may automatically cause the intervals of the spiral-shaped visualization to be filtered.
Systems, methods, and computer-readable media for a well construction activity graph builder. An example method can include obtaining a stream of events associated with a wellbore; obtaining mapping metadata identifying data points to be included in a graph data model from a store of data associated with the wellbore; generating the graph data model based on the stream of events, the mapping metadata, and the data points identified in the mapping metadata, the graph data model including nodes representing logical entities associated with the data points, the nodes having interconnections based on data relationships defined in the mapping metadata, each logical entity corresponding to a set of data points from the data points; and generating a view of the graph data model, the view depicting at least some of the nodes and interconnections in the graph data model.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
48.
WELL PATH DRILLING TRAJECTORY AND CONTROL FOR GEOSTEERING
Geosteering can be used in a drilling operation to create a wellbore that is used to extract hydrocarbons from a defined zone within the subterranean formation. According to some aspects, generating paths for the wellbore may include using path-planning protocols and pure-pursuit protocols. The pure-pursuit protocol may be executed to output a plurality of candidate drilling paths. The output may also include control parameters for controlling the drill bit. A trajectory optimizer may determine a result of multi-objective functions for each candidate path. A cost function may represent a cost or loss associated with a candidate path. Additionally, the trajectory optimizer may perform an optimization protocol, such as Bayesian optimization, on the cost functions to determine which candidate path to select. The selected candidate path may correspond to new control parameters for controlling the drill bit to reach the target location.
A system can include one or more sensors at a wellsite. The system can detect first emission data about emissions with respect to a wellbore over a first period of time. The system can detect second emission data about emissions with respect to the wellbore over a second period of time. The system can determine an adjustment to a plug and abandonment operation with respect to the wellbore based on the first emission data. The system can determine an amount of reduction in emissions from the wellbore using the first emission data and the second emission data. The system can output the amount of reduction in the emissions.
E21B 47/09 - Locating or determining the position of objects in boreholes or wells; Identifying the free or blocked portions of pipes
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
An automated offset well analytics engine generates offset well rankings for a prospect well. The engine aggregates data for offset wells and the prospect wells is across multiple disparate data sources corresponding to a user-specified scope. The engine generates features comparing the offset wells to the prospect well are using a combination of machine-learning based models and risk analysis. Offset wells are ranked by feature and further ranked across features using a weighted feature ranking map. Feature weights are iteratively trained using a reinforcement learning model in a feedback loops with a well expert. A prospect well casing schema and bottom hole assembly is designed using automatically generated offset well rankings.
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.
The present disclosure is related to improvements in methods for evaluating formation fluid properties of interest in an in-production wellbore as well as evaluating health and functionalities of physical sensors present in and collecting data within the well. In one aspect, a method includes receiving data from one or more physical sensors within a wellbore; determining at least one formation property of the wellbore using one or more machine learning models receiving the data as input and generating reservoir simulation models using the at least one formation property.
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 47/12 - 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 99/00 - Subject matter not provided for in other groups of this subclass
53.
SLIDE AND ROTATION PROJECTION FOR REDUCING FRICTION WHILE DRILLING
This disclosure relates to systems and methods for controlling a motor based on a slide-rotate ratio while drilling a wellbore. The system includes at least one sensor disposable with respect to a drillstring and a motor communicatively coupled to the drillstring. A computing device performs operations for controlling the motor based on the slide-rotate ratio. The computing receives input data corresponding to characteristics of the drillstring, the motor, or both. The computing device calculates a hook load for multiple time intervals. The computing device determines a friction factor based on the hook load for each of the time intervals. The computing device projects a slide-rotate ratio for the motor that substantially minimizes friction while operating the drill string, and controls the motor based on the slide-rotate ratio.
A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric based on the deep neural network output.
Certain aspects and features relate to a system for trajectory planning and control for new wellbores. Data can be received for multiple existing wells associated with a subterranean reservoir and used to train a deep neural network model to make accurate well property projections at any other location in the reservoir. A model of features for specific well locations based on seismic attributes of the well location can be automatically generated, and the model can be used in drilling trajectory optimization. In some examples, the system builds a deep neural network (DNN) model based on the statistical features, and trains the DNN model using Bayesian optimization to produce an optimized DNN model. The optimized model can be used to provide drilling parameters to produce an optimized trajectory for a new well.
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 method for determining annular fluid expansion (“AFE”) within a borehole with a sealed casing string annulus. The method may include defining a configuration of the borehole. The method may further include defining a production operation and a borehole operation. The method may also include determining AFE within the borehole when performing the production operation. The method may further include determining AFE within the borehole when performing the borehole operation based on the AFE within the borehole when performing the production operation.
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 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 37/00 - Methods or apparatus for cleaning boreholes or wells
58.
Drill bit wear and behavior analysis and correlation
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 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
A model optimizer for predicting a drill bit variable can select a model from multiple models based on a learned preference. The preference may be updated according to preference indicator received from a user in response to an output model selection.
Aspects of the subject technology relate to systems and methods for predicting physical characteristics of a physical environment using a physical characterization model trained based on simulated states of a modeled physical environment. A physical characterization model can be generated based on a plurality of simulated states of a modeled physical environment. Specifically, the physical characterization model can be trained by mapping simulated spatial properties of the modeled physical environment temporally across the plurality of simulated states of the modeled physical environment. Further, input state data describing one or more input states of a physical environment can be received. One or more physical characteristics of the physical environment can be predicted by applying the physical characterization model to the one or more input states of the physical environment.
Systems and methods for auto-detection and classification of rig activities from trend analysis of sensor data are provided. Sensor data from rig equipment may be obtained during wellsite operations. The sensor data may be analyzed to identify one or more index points where a trend in the sensor data changes. The sensor data may be segmented into a first set of time segments representing macro activities performed during the well site operations, based on the one or more identified index points. Statistical analysis may be performed on the sensor data within each first time segment to identify points where statistical properties of the sensor data change. Each first time segment may he segmented into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties.
E21B 43/16 - Enhanced recovery methods for obtaining hydrocarbons
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
62.
MECHANICAL AND HYDROMECHANICAL SPECIFIC ENERGY-BASED DRILLING
A method comprises drilling a borehole and capturing data during drilling of the borehole, wherein the data comprises at least one value of at least one operational parameter of the drilling. A specific energy formula is modified and used to determine at least one of an efficiency and a quality of drilling of a borehole. Modifying the specific energy formula is based on data captured during drilling of the borehole. The specific energy formula comprises at least one of a mechanical specific energy formula and a hydromechanical specific energy formula. An adjusted specific energy value for the drilling is calculated based on the modified specific energy formula. At least one of the efficiency and the quality of the drilling of the borehole is determined based on the adjusted specific energy value. Also disclosed is a system comprising a machine-readable medium having program code executing the method.
Methods and systems for analyzing a well system design including determining a volume change of trapped annular regions based on a plurality of initial temperatures and a plurality of final temperatures and an initial pressure. Analyzing the trapped annular regions to determine an enclosure volume change, a fluid expansion volume, and an annular pressure buildup for a safe well system and generating a graphical representation of the bounds of the safe well system envelop.
Systems, methods, and computer-readable media are provided for rig monitoring and in particular, to receiving data from a plurality of sensors in real-time, mapping the data from the plurality of sensors with a micro-activity and a macro-activity, generating a message based on the mapping of the data from the plurality of sensors with the micro-activity and the macro-activity, selecting a parameter to be compared with a bit depth, tuning the parameter and the bit depth with a corresponding model based on the message, generating a parameter uncertainty array and a bit depth uncertainty array based on the tuning of the parameter and the bit depth, and generating dynamic uncertainty ellipses based on the parameter uncertainty array and the bit depth uncertainty array.
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
The disclosed embodiments include well operation evaluation systems and methods to analyze a broomstick chart of a well operation. The method includes receiving data indicative of a broomstick chart of a well operation. The method also includes diagnosing an issue during the well operation based on the broomstick chart. The method further includes predicting an impact on the well operation as a result of the issue. The method further includes determining a likelihood of occurrence of the impact. The method further includes determining a solution to overcome the issue.
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
The disclosed embodiments include well operation evaluation systems and methods to analyze a broomstick chart of a well operation. The method includes receiving data indicative of a well operation involving deploying a conveyance to a predetermined location. The method also includes determining one or more conditions that impact deployment of the conveyance to the predetermined location. The method further includes determining a likelihood of occurrence of the one or more conditions. The method further includes determining one or more operations which, when performed by one or more tools used during the well operation, overcome the one or more conditions.
A method for detecting anomalies in a piece of wellsite equipment. The method may include measuring data related to the piece of wellsite equipment. The method may also include encoding the measured data with a first autoencoder to produce a first set of encoded data. The method may further include performing a first Gaussian process regression (“GPR”) on the first set of encoded data to produce a first set of results that identifies a first anomaly in the measured data and that provides a first confidence interval for the first anomaly.
The embodiments include systems and methods to determine torque and drag of a string. A method includes analyzing a plurality of continuous segments of a string deployed in a wellbore, and determining a deflection of the plurality of continuous segments from a node of the string and a tortuosity deflection of the wellbore. In response to a determination that deflection of the plurality of continuous segments from the node of the string is greater than the tortuosity deflection of the wellbore, the method includes applying a soft string model to determine a torque and a drag of the string. In response to a determination that deflection of the plurality of continuous segments from the node of the string is not greater than the tortuosity deflection of the wellbore, the method includes applying a stiff string model to determine a torque and a drag of the string.
Certain aspects and features relate to a system that includes a drilling tool, a processor, and a non-transitory memory device that includes instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving input data that corresponds to characteristics of at least one of drilling fluid, a drillstring, or a wellbore. The operations also include calculating at least one dynamic sideforce and at least one dynamic, hydraulic force based at least in part on the input data. The operations also include determining an equilibrium solution for an output value using the at least one dynamic sideforce and at least one dynamic, hydraulic force. The operations also include applying the output value to the drilling tool for controlling operation of the drilling tool.
E21B 45/00 - Measuring the drilling time or rate of penetration
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 44/04 - Automatic control of the tool feed in response to the torque of the drive
A system and method for controlling multiple drilling tools inside wellbores makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit, mud flow rate and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration and hydraulic mechanical specific energy for the observed values using an objective function. Range constraints including the physical drilling environment and the total power available to all drilling tools within the drilling environment can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameters to achieve a predicted value for the drilling parameters. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.
A method includes collecting a first set of borehole gravity data at a first time step along a length of a first wellbore and collecting a second set of borehole gravity data at the first time step along a length of a second wellbore. The method also includes interpolating a third set of borehole gravity data at the first time step in an area between the first wellbore and the second wellbore using the first and the second sets of borehole gravity data. Further, the method includes determining a first fluid saturation and a fluid saturation change over time in a reservoir containing the first wellbore and the second wellbore using the first set, the second set, and the third set. Moreover, the method includes controlling wellbore production operations or wellbore injection operations at the first wellbore based on the fluid saturation change.
A system for autonomous operation and management of oil and gas fields includes at least one autonomous vehicle. The system also includes a processor communicatively couplable to the plurality of autonomous vehicles and a non-transitory memory device including instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving field analytics data of an oil and gas field and producing at least one hydrocarbon field model based on the field analytics data. Additionally, the operations include deploying the at least one hydrocarbon field model to a sensor trap appliance using the at least one autonomous vehicles and collecting well sensor data from the sensor trap appliance. Further, the operations include detecting an anomaly using the at least one hydrocarbon field model and the well sensor data and triggering an operational process based on detecting the anomaly.
A system and method for controlling a gas supply to provide gas lift for wellbore(s) using Bayesian optimization. A computing device controls a gas supply to inject gas into wellbore(s). The computing device receives first reservoir data associated with a first subterranean reservoir and simulates production using the first reservoir data, using a model for the first subterranean reservoir. The production simulation provides first production data. The computing device receives second reservoir data associated with a subterranean reservoir and simulates production using the second reservoir data, using a model for the second subterranean reservoir. The production simulation provides second production data. A Bayesian optimization of an objective function of the first and second production data subject to any gas injection constraints can be performed to produce gas-lift parameters. The gas-lift parameters can be applied to the gas supply to control injection of gas into the wellbore(s).
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.
The disclosure presents processes for improving the design phase of plastic material lined tubular structures used downhole of a borehole. A plastic material lined tubular structure model is utilized for tubular structures that have a metal layer, a grout layer, and a plastic material layer. The model can use a modified wall thickness for the metal layer. A strength model can be applied to the modified critical dimensions, e.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.
A reservoir model for values of a formation property is simulated using a turning bands method with distributed computing. A distributed computing system simulates the reservoir on separate machines in parallel in several stages. First, line distributions are simulated independently on turning bands. The reservoir model is partitioned into tiles and unconditional simulations are run on each tile in parallel using the corresponding simulated turning bands. The unconditional simulations within each tile are conditioned on known formation values to generate conditional simulations. Conditional simulations are aggregated across tiles to create the simulated reservoir model.
A system for designing a casing string for a well. The system comprises a processor, a non-transitory memory, a thermodynamic modeling application stored in the non-transitory memory that, when executed by the processor, models carbon dioxide (CO2) material in the well using a carbon dioxide equation of state (EoS) to determine thermodynamic properties of the CO2 material, and a downhole environment modeling application stored in the non-transitory memory that, when executed by the processor determines temperatures of and pressures at well components at each of a plurality of points of a casing string design based in part on the thermodynamic properties of the CO2 material determined by the thermodynamic modeling application, and provides the temperatures of well components and pressures in the casing string at each of the plurality of points of the casing string to a casing string strength analysis application executing on the computer system.
Partially coupling a geomechanical simulation with a reservoir simulation facilitates predicting strain behavior for a reservoir from production and injection processes. A method comprises generating a geomechanical model based on a mechanical earth model that represents a subsurface area. The geomechanical model indicates a division of the mechanical earth model into a plurality of grid cells that each correspond to a different volume of the subsurface area. Based on a first virtual compaction experiment with the geomechanical model, compaction curves are generated. The compaction curves represent porosity as a function of stress. The compaction curves are converted from porosity as a function of stress to porosity as a function of pore pressure. The geomechanical model is partially coupled to a reservoir simulation model using the converted compaction curves.
A system for designing a casing string for an oil well, a gas well, an oil and gas well, and/or a geothermal well. The system comprises a processor, a non-transitory memory storing a casing string design, wherein the casing string design comprises at least one section of UOE-type pipe, a downhole environment simulation application stored in the non-transitory memory that, when executed by the processor determines downhole conditions based on the casing string design, wherein the downhole conditions comprise a downhole temperature, and a casing collapse strength modeling application stored in the non-transitory memory that, when executed by the processor, analyzes collapse strength of the casing string based on the downhole temperature and based on a UOE-type pipe collapse strength model and presents a collapse strength report on the casing string design based on analyzing the collapse strength of the casing string.
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.
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 method for assessing an integrity of metal tubular structures may comprise receiving one or more inputs, applying an algorithm to automatically select an appropriate model for a given corrosion scenario, applying a combined model including semi-empirical and multiphase flow corrosion characteristics to the one or more inputs, determining one or more corrosion parameters of either an internal pipe wall, an external pipe surface, or both, applying a corrosion correlation value to the one or more corrosion parameters to produce one or more correlated corrosion parameters, and storing the one or more correlated corrosion parameters on a computer readable medium. A system may comprise an information handling system which may comprise at least one memory operable to store computer-executable instructions, at least one communications interface to access the at least one memory, and at least one processor.
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 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 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 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 47/12 - 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 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 37/00 - Methods or apparatus for cleaning boreholes or wells
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 45/00 - Measuring the drilling time or rate of penetration
E21B 21/08 - Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure
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.
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.
E21B 47/022 - Determining slope or direction of the borehole, e.g. using geomagnetism
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
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 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.
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.
G01V 99/00 - Subject matter not provided for in other groups of this subclass
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
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.
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.
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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 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 49/08 - Obtaining fluid samples or testing fluids, in boreholes or wells
93.
Detecting wellpath tortuosity variability and controlling wellbore operations
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 47/022 - Determining slope or direction of the borehole, e.g. using geomagnetism
E21B 47/007 - Measuring stresses in a pipe string or casing
G01B 21/32 - Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
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.
G01V 13/00 - Manufacturing, calibrating, cleaning, or repairing instruments or devices 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
95.
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.
A system is described for determining an analogue geological feature. 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 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.
A well operation simulator predicts temperature and pressure profiles of a multi-tubing completion well for well design. The simulator is comprised of modules, which when executed, determine a first set of design limits based on stress conditions arising from the temperature and pressure profiles from a multi-tubing drilling module and a multi -tubing production module for drilling and production operations. A multi-tubing multi-string module predicts the annular fluid expansion (AFE) and annular pressure buildup (APB) of the multi-tubing well from the previously calculated temperature profile, pressure profile, and stress conditions and determines a second set of design limits with the AFE/APB effects in addition to the temperature profile and pressure profile predicted from multi-tubing drilling module and multi-tubing production module. The first and second sets of design limits are depicted using one or
Systems, methods and computer readable storage media for optimizing a determination of a number of grid cell counts to be used in creating the geocellular grid of an earth, geomechanical or petro-elastic model for reservoir simulation. These may involve determining at least one processing time for a simulation; determining a grid cell count to be used in creating a geocellular grid for the simulation based on the at least one processing time and a number of processors to be used for creating the model; creating the geocellular grid using the grid cell count, and generating a model for the simulation using the geocellular grid.
E21B 43/00 - Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
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
Disclosed herein are example configurations of centrifuges used for analyzing properties of core samples extracted from sub-surface environments, in which the configuration of the centrifuges and rotations thereof improve fluid distribution within core samples held in the apparatus. In one aspect, a centrifuge includes a rotating arm and a holder coupled to a distal end of the rotating arm, the holder being configured to rotate independently of the rotating arm for analyzing fluid-rock interaction within the holder.
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.
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