Abstract: A method can include receiving a location from a process guided by an agent, where the process intends to reach a target; assigning uncertainty to the process; performing multiple simulation runs, guided by agent output, from the location with an intent to reach the target, where the multiple simulation runs account for the uncertainty; and generating output based on the multiple runs that characterizes an ability of the agent to reach the target in view of the uncertainty.

Abstract: An apparatus and method utilize a trained machine learning model to synthesize formation evaluation data such as formation tops and LWD logs. In some instances, the synthesis of formation evaluation data may further be based upon drilling mechanics data collected during drilling, thus effectively enabling formation evaluation data to be synthesized primarily based upon surface measurements collected in real time, and in many cases without the need for collecting downhole measurements during drilling. In addition, in some instances, a machine learning model implemented as a generative adversarial network (GAN) may be used to synthesize formation evaluation data, with drilling mechanics data collected during drilling also used in some instances.

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: The present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and/or predicted drilling metrics.

Abstract: A method can include accessing data associated with a well and one or more offset wells; based on at least a portion of the data, generating a set of distributions via parametric estimation, where the distributions are associated with a well-related activity and time; analyzing individual distributions in the set of distributions with respect to at least a portion of the data to pass or fail each of the individual distributions; and, for one or more passed individual distributions, outputting one of the passed individual distributions for the well.

Abstract: A method for evaluating one or more bottom hole assemblies (BHAs) includes receiving a plurality of inputs. The inputs include one or more properties of the one or more BHAs, a planned trajectory of a wellbore, and one or more properties of a subterranean formation into which the wellbore will be drilled. The method also includes simulating drilling the wellbore in the subterranean formation based at least partially upon the inputs. Drilling of the wellbore is simulated with one or more artificial intelligence (AI) agents. Drilling of the wellbore is simulated a plurality of times using each of the one or more BHAs, thereby producing a plurality of simulations. Each simulation is generated using a different one of the AI agents. The method also includes generating one or more outputs in response to simulating drilling the wellbore.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting a washout or other anomaly event in a wellbore. In particular, in one or more embodiments, the disclosed systems receive a plurality of measurements including a measured flow rate into the wellbore, a measured weight on a drill bit in the wellbore, a measured depth of the drill bit in the wellbore, and a measured pressure at a standpipe of the wellbore. In one or more embodiments, the disclosed systems estimate one or more parameters of a physical model for determining a theoretical estimate of the standpipe pressure. In one or more embodiments, the disclosed systems determine a probability that the washout or other anomaly event is occurring in the wellbore based at least partially upon the measurements and the theoretical estimate of the standpipe pressure.

Abstract: A method can include receiving input for a drilling operation that utilizes a bottom hole assembly and drilling fluid; generating a set of offset drilling operations using historical feature data, where the historical feature data are processed by computing feature distances; performing an assessment of the offset drilling operations as characterized by at least feature distance-based similarity between the drilling operation and the offset drilling operations; and outputting at least one recommendation for selection of one or more of a component of the bottom hole assembly and the drilling fluid based on the assessment.

Abstract: A method for drilling a well includes generating a plurality of proposed drilling actions using a plurality of working agents based on a working environment, simulating drilling responses to the proposed drilling actions using a plurality of validation agents in a validation environment that initially represents the working environment, determining rewards for the proposed drilling actions based on the simulating, using the validation agents, selecting one of the proposed drilling actions, and causing a drilling rig to execute the selected one of the proposed actions.

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, 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 can include accessing data associated with a well and one or more offset wells; based on at least a portion of the data, generating a set of distributions via parametric estimation, where the distributions are associated with a well-related activity and time; analyzing individual distributions in the set of distributions with respect to at least a portion of the data to pass or fail each of the individual distributions; and, for one or more passed individual distributions, outputting one of the passed individual distributions for the well.

Abstract: A method includes receiving information that includes elastic property information and that includes sonic data acquired via a tool disposed at a plurality of depths in a bore in a subterranean environment that includes at least one anisotropic formation; processing the information to generate processed information where the processed information includes variance information associated with the elastic property information and where the processed information includes velocity information and orientation information associated with the sonic data; performing an inversion based at least in part on the processed information; and outputting values for elastic parameters based at least in part on the inversion.

Abstract: A method of computer modeling a reservoir using multiple-point statistics from non-stationary training images is provided.