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.

Abstract: Systems and methods are provided for estimating and/or using drilling efficiency parameters of a drilling operation. A method for estimating drilling efficiency parameters may include using a borehole assembly that includes a drill bit to drill into a geological formation. A number of measurements of weight-on-bit and torque-on-bit may be obtained during a period in which weight-on-bit and torque-on-bit are non-steady-state. The measurements of weight-on-bit and torque-on-bit may be used to estimate one or more drilling efficiency parameters relating to the drilling of the geological formation during the period.

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 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: The disclosure relates to a method for estimating a transit time of an element circulating in a borehole during the drilling of the borehole. The transit time is representative of a time period for the element to move from the bottom of the borehole to its exit at the surface. The method comprises measuring a plurality of drilling parameters, computing a first signal of a first indicator based on a first set of measured drilling parameters and a second signal of a second indicator based on a second set of measured drilling parameters versus time. The first indicator is representative of a first type of events happening at the bottom of the borehole and the second indicator is representative of a second type of events happening at the exit of the borehole linked to the first type of events. The method also comprises characterizing a correlation between the first and second signals and determining a shift between the first and second signals. An estimated transit time is then determined from the shift.

Abstract: The disclosure relates to a first method for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method comprises receiving a set of measurement logs comprising one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The measured characteristic include at least cuttings percentage and one or more additional measured characteristics. 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 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 method is provided for controlling a managed pressure drilling system. The method comprises providing a fast system interpretation model for the operation of the managed pressure drilling system, obtaining real-time measurements from one or more sensors regarding the operation of the managed pressure drilling system, using the measurements to calibrate the fast system interpretation model for the managed pressure drilling system in real-time and using a predictive controller to run the calibrated fast system interpretation model to a time horizon in the future.

Abstract: The disclosure relates to a method for analysing cuttings exiting a borehole. The method comprises taking at least an image of a sample of cuttings on a background surface, obtaining spectra representative of the image in the (hue, saturation, brightness) coordinate space, wherein each spectrum is associated to a coordinate and is representative of the distribution of the values of the pixels for the coordinate. Based on the spectrum and the values of each pixel for the associated coordinate, classifying the pixel in one of a plurality of groups, wherein each group is representative of a type of objects within the image, i.e. cuttings and background surface. The method also comprises determining at least a cuttings zone in the image based on the classification of the pixels.

Abstract: A method for directional drilling a subterranean borehole includes transforming surface sensor measurements from time domain sensor data to frequency domain sensor data. A rotary drilling parameter may be changed when a parameter of the frequency domain sensor data reaches a threshold or is within a predetermined range of values.