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, 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 computer-implemented method may include receiving test data representing a cutter/rock interaction for a cutter/rock pair; calibrating an analytical model to represent the cutter/rock interaction mechanism for a cutter/rock pair; applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets; generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interaction between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.

Abstract: A method for generating a drilling plan for drilling a wellbore at a field includes receiving data. The data includes one or more of geological properties at the field, wellbore properties, drilling tool parameters, rig characteristics of drilling rigs, and working practices of a plurality of drilling crews. The method also includes generating a plurality of candidate drilling plans for drilling the wellbore at the field. The method also includes estimating one or more outputs for the candidate drilling plans based at least partially upon the data. The one or more outputs include an amount of emissions generated to drill the wellbore using the candidate drilling plans. The method also includes presenting for selection one or more of the candidate drilling plans based at least partially upon the one or more outputs.

Abstract: A method can include providing a trained drilling motor model trained via machine learning based at least in part on drilling motor simulation results; instantiating a motor engine component with an interface in a computational environment; and, responsive to receipt of a call via the interface, returning drilling motor information based at least in part on the trained drilling motor model.

Abstract: A system, computer-readable medium, and method for identifying drilling events in drilling reports, of which the method includes receiving one or more drilling reports including text data representing one or more observations recorded during a drilling activity, identifying a drilling event, a drilling activity, or both from the text data using a model, obtaining feedback based at least in part on the drilling event, the drilling activity, or both that were identified, and training the model based on the feedback.

Abstract: Techniques for determining trajectories for a plurality of wells while avoiding collision between wells are presented. The techniques can include determining a zone of uncertainty for individual wells of the plurality of wells, determining a minimum separation factor for individual wells of the plurality of wells, determining a gradient of a separation factor for at least one pair of wells the plurality of pairs of wells, updating a nudge position for at least one well, and providing nudge positions for the individual wells of the plurality of wells.

Abstract: A method can include acquiring drilling performance data for a downhole tool; modeling drilling performance of the downhole tool to generate results; training a machine learning model using the drilling performance data and the results to generate a trained machine learning model; and predicting behavior of the downhole tool using the trained machine learning model.

Abstract: A method for designing a drill bit includes simulating a cutting element cutting a digitized rock sample, storing outputs from the simulating in a digital rock interaction file, modeling the drill bit to have at least one of the cutting element, and using data in the digital rock interaction file to simulate the drill bit in a drilling operation

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, 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 acquiring trajectory information; based at least in part on a portion of the trajectory information, generating a set of candidate trajectories with associated performance indicator values; rendering a graphical user interface to a display; via the graphical user interface, receiving input that adjusts one of the performance indicator values; and, responsive to the adjustment of the one of the performance indicator values, selecting one of the set of candidate trajectories.

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 system, computer-readable medium, and method for identifying drilling events in drilling reports, of which the method includes receiving one or more drilling reports including text data representing one or more observations recorded during a drilling activity, identifying a drilling event, a drilling activity, or both from the text data using a model, obtaining feedback based at least in part on the drilling event, the drilling activity, or both that were identified, and training the model based on the feedback.

Abstract: A computer-implemented method may include receiving test data representing a cutter/rock interaction for a cutter/rock pair; calibrating an analytical model to represent the cutter/rock interaction mechanism for a cutter/rock pair; applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets; generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interaction between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.

Abstract: A method can include receiving well plan information that includes a reservoir target associated with a reservoir of a subterranean environment; based at least in part on characteristics of the subterranean environment, determining well trajectory metrics that comprise a geometrical metric; determining a weighted metric based at least in part on weighting of the well trajectory metrics; and, based at least in part on an assessment of the weighted metric, outputting directional drilling information for a well trajectory to the reservoir target.

Abstract: A method can include receiving well plan information for drilling at least a section of a well at a site; generating a set of candidate drillstring assemblies based at least in part on at least a portion of the well plan information and drillstring assembly component information; generating a proposed set of drillstring assemblies with performance indexes based at least in part on the set of candidate drillstring assemblies and engineering analysis information associated with the site; and generating a final drillstring assembly based at least in part on the proposed set of drillstring assemblies and the performance indexes.

Abstract: A method can include providing a trained drilling motor model trained via machine learning based at least in part on drilling motor simulation results; instantiating a motor engine component with an interface in a computational environment; and, responsive to receipt of a call via the interface, returning drilling motor information based at least in part on the trained drilling motor model.

Abstract: A method can include receiving well plan information that includes a reservoir target associated with a reservoir of a subterranean environment; based at least in part on characteristics of the subterranean environment, determining well trajectory metrics that comprise a geometrical metric; determining a weighted metric based at least in part on weighting of the well trajectory metrics; and, based at least in part on an assessment of the weighted metric, outputting directional drilling information for a well trajectory to the reservoir target.

Abstract: A method includes acquiring trajectory information; based at least in part on a portion of the trajectory information, generating a set of candidate trajectories with associated performance indicator values; rendering a graphical user interface to a display; via the graphical user interface, receiving input that adjusts one of the performance indicator values; and, responsive to the adjustment of the one of the performance indicator values, selecting one of the set of candidate trajectories.