Abstract: Methods, computing systems, and computer-readable media for training and using a machine learning system to predict equipment pressure measurements, of which the method includes inputting a set of training data including a first set of equipment pressure measurements, inputting a set of supplemental data. The supplemental data is obtained from a physical model that estimates a second set of equipment pressure measurements. The method includes training the machine learning system based on the set of training data and the set of supplemental data to generate a trained machine learning system, receiving real-time operational data, inputting the real-time operational data into the trained machine learning system, predicting a real-time equipment pressure measurement based on the inputting the real-time operational data into the trained machine learning system, and executing a computer-based instruction based on the predicting the real-time equipment pressure measurement.

Abstract: Methods, computing systems, and computer-readable media for training and using a machine learning system to predict equipment pressure measurements, of which the method includes inputting a set of training data including a first set of equipment pressure measurements, inputting a set of supplemental data. The supplemental data is obtained from a physical model that estimates a second set of equipment pressure measurements. The method includes training the machine learning system based on the set of training data and the set of supplemental data to generate a trained machine learning system, receiving real-time operational data, inputting the real-time operational data into the trained machine learning system, predicting a real-time equipment pressure measurement based on the inputting the real-time operational data into the trained machine learning system, and executing a computer-based instruction based on the predicting the real-time equipment pressure measurement.

Abstract: A system and method that include receiving a drilling trajectory plan to drill an intended trajectory. The system and method also include analyzing the drilling trajectory plan and identifying a tool face orientation to drill the intended trajectory based on the drilling trajectory plan. The system and method additionally include identifying an uncertainly level associated with the tool face orientation based on a data structure storing uncertainty levels at different tool face orientations. The system and method further include determining a drilling plan based on the uncertainty level associated with the tool face orientation.

Abstract: A system and method that include receiving a drilling trajectory plan to drill an intended trajectory. The system and method also include analyzing the drilling trajectory plan and identifying a tool face orientation to drill the intended trajectory based on the drilling trajectory plan. The system and method additionally include identifying an uncertainty level associated with the tool face orientation based on a data structure storing uncertainty levels at different tool face orientations. The system and method further include determining a drilling plan based on the uncertainty level associated with the tool face orientation.

Abstract: A method for determining a maximum tripping velocity for tripping a downhole tool string in a wellbore includes obtaining contextual data including wellbore data, tool string data, and drilling fluid data; computing downhole drilling fluid pressures at multiple tool string velocities and accelerations using the obtained contextual data and a wellbore hydraulics model; generating an equivalent circulation density (ECD) contour along a two-dimensional acceleration and velocity parameter space from the computed downhole drilling fluid pressures; and evaluating the ECD contour with at least one of a formation pore pressure or a formation fracture pressure to generate the maximum tripping velocity.

Abstract: A system and method that includes querying a database to obtain offset well data collected while drilling previously drilled wells. The system and method also include determining if at least one risk is identified with respect to a planned well based on the offset well data. The system and method additionally include generating a machine learning model based on the at least one risk that is identified based on the offset well data. The system and method further include predicting at least one drilling risk based on the machine learning model, wherein a drill plan that includes drilling parameters is adjusted based on the at least one predicted drilling risk.

Abstract: A method includes receiving first input values for a first parameter of a physical system, calculating first modeled values for a second parameter using a model that represents the physical system, based on the first input values, receiving measured values for the second parameter, training a machine learning model to adjust modeled values generated by the model based on a difference between the first modeled values and the measured values, receiving second input values for the first parameter, calculating second modeled values for the second parameter using the model, generating adjusted values for the second parameter by adjusting the second modeled values using the trained machine learning model, and visualizing the adjusted values for the second parameter as representing operation of the physical system.

Abstract: A method includes receiving first input values for a first parameter of a physical system, calculating first modeled values for a second parameter using a model that represents the physical system, based on the first input values, receiving measured values for the second parameter, training a machine learning model to adjust modeled values generated by the model based on a difference between the first modeled values and the measured values, receiving second input values for the first parameter, calculating second modeled values for the second parameter using the model, generating adjusted values for the second parameter by adjusting the second modeled values using the trained machine learning model, and visualizing the adjusted values for the second parameter as representing operation of the physical system.

Abstract: A method includes receiving historical well data comprising trajectories, performance data, and one or more drilling parameters for a plurality of wells, clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories, using a machine learning model, receiving trajectory data for a subject well, identifying one of the clusters based on the trajectory data of the subject well, using the machine learning model, selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the plurality of wells or the portion thereof, and visualizing the selected one or more of the plurality of wells or one or more sections thereof.

Abstract: A first method is provided for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method includes receiving a set of measurement logs including one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The method also includes segmenting the wellbore into regions based on identified change of trend in one or more of the measurement logs of the set, and sub-segmenting at least one region into zones based on detection of appearance or disappearance of a rock type in the cuttings percentage log, The method also includes determining, in each zone, a location, length and rock type of one or more layers based on a total percentage of each rock type in the zone in the cuttings percentage log and at least one of the additional measurement logs.

Abstract: A system and method that include receiving a drilling trajectory plan to drill an intended trajectory. The system and method also include analyzing the drilling trajectory plan and identifying a tool face orientation to drill the intended trajectory based on the drilling trajectory plan. The system and method additionally include identifying an uncertainly level associated with the tool face orientation based on a data structure storing uncertainty levels at different tool face orientations. The system and method further include determining a drilling plan based on the uncertainty level associated with the tool face orientation.

Abstract: A system and method that includes querying a database to obtain offset well data collected while drilling previously drilled wells. The system and method also include determining if at least one risk is identified with respect to a planned well based on the offset well data. The system and method additionally include generating a machine learning model based on the at least one risk that is identified based on the offset well data. The system and method further include predicting at least one drilling risk based on the machine learning model, wherein a drill plan that includes drilling parameters is adjusted based on the at least one predicted drilling risk.

Abstract: A computer-implemented method including receiving a steering command identifying a tool face orientation in which the steering command is expected to produce an intended steering response of an intended drilling trajectory. The method further includes receiving an actual steering response result of the steering command in which the actual steering response result identifies an actual drilling trajectory. The method further includes storing a dataset comparing the actual steering response result in relation to the intended steering response, determining an uncertainty level of the tool face orientation based on the stored dataset, and outputting a visual representation of steering response with the uncertainty level.

Abstract: Methods, computing systems, and computer-readable media for training and using a machine learning system to predict equipment pressure measurements, of which the method includes inputting a set of training data including a first set of equipment pressure measurements, inputting a set of supplemental data. The supplemental data is obtained from a physical model that estimates a second set of equipment pressure measurements. The method includes training the machine learning system based on the set of training data and the set of supplemental data to generate a trained machine learning system, receiving real-time operational data, inputting the real-time operational data into the trained machine learning system, predicting a real-time equipment pressure measurement based on the inputting the real-time operational data into the trained machine learning system, and executing a computer-based instruction based on the predicting the real-time equipment pressure measurement.

Abstract: A method, computing system, and non-transitory computer-readable medium, of which the method includes receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile.

Abstract: A first method is provided for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method includes receiving a set of measurement logs including one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The method also includes segmenting the wellbore into regions based on identified change of trend in one or more of the measurement logs of the set, and sub-segmenting at least one region into zones based on detection of appearance or disappearance of a rock type in the cuttings percentage log, The method also includes determining, in each zone, a location, length and rock type of one or more layers based on a total percentage of each rock type in the zone in the cuttings percentage log and at least one of the additional measurement logs.

Abstract: A method is provided for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method includes receiving a set of measurement logs including one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The method also includes segmenting the wellbore into regions based on identified change of trend in one or more of the measurement logs of the set, and sub-segmenting at least one region into zones based on detection of appearance or disappearance of a rock type in the cuttings percentage log, The method also includes determining, in each zone, a location, length and rock type of one or more layers based on a total percentage of each rock type in the zone in the cuttings percentage log and at least one of the additional measurement logs.

Abstract: A computer-implemented method including receiving a steering command identifying a tool face orientation in which the steering command is expected to produce an intended steering response of an intended drilling trajectory. The method further includes receiving an actual steering response result of the steering command in which the actual steering response result identifies an actual drilling trajectory. The method further includes storing a dataset comparing the actual steering response result in relation to the intended steering response, determining an uncertainty level of the tool face orientation based on the stored dataset, and outputting a visual representation of steering response with the uncertainty level.

Abstract: A computer-implemented method including receiving a steering command identifying a tool face orientation in which the steering command is expected to produce an intended steering response of an intended drilling trajectory. The method further includes receiving an actual steering response result of the steering command in which the actual steering response result identifies an actual drilling trajectory. The method further includes storing a dataset comparing the actual steering response result in relation to the intended steering response, determining an uncertainty level of the tool face orientation based on the stored dataset, and outputting a visual representation of steering response with the uncertainty level.

Abstract: A method, computing system, and non-transitory computer-readable medium, of which the method includes receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile.