Abstract: A method includes generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data, training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models, receiving sensor data representing present drilling data, predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model, and predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.

Abstract: A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.

Abstract: A method automatically validates sensor data. The method includes extracting a sample from a sample time series using a sample window, generating an input vector from the sample, and generating a context vector from the input vector using an encoder model comprising a first recurrent neural network. The method further includes generating an output vector from the context vector by a decoder model comprising a second recurrent neural network and generating a reconstruction error from a comparison of the output vector to the input vector. The reconstruction error indicates an error with the sample. The method further includes presenting the reconstruction error.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.

Abstract: Systems and methods for generating digital gamma-ray logs for target wells based on combined physics and machine learning model using real-time information (e.g., drilling parameters, survey data, gamma-ray logs, and so forth) obtained from offset wells analogous to the subject well in terms of gamma-ray readings. The systems and methods may provide solutions that may lower the cost of Measuring While Drilling (MWD) and/or Logging While Drilling (LWD) process and facilitate the users (e.g., drillers, geoscientists, and so forth) to make enhanced data driven decisions.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: Bayesian recursive estimation is used to analyze performance parameters of a membrane separation system based on historical operational data of a membrane system. Bayesian estimation considers historical data over prior time intervals to predict future membrane separation performance to avoid unexpected downtime and unanticipated maintenance. A set of state variables used for modeling performance is used with a degradation model of to anticipate performance changes and maintenance based on measured properties of permeate, non-permeate, and feed flows.

Abstract: Bayesian recursive estimation is used to analyze performance parameters of a membrane separation system based on historical operational data of a membrane system. Bayesian estimation considers historical data over prior time intervals to predict future membrane separation performance to avoid unexpected downtime and unanticipated maintenance. A set of state variables used for modeling performance is used with a degradation model of to anticipate performance changes and maintenance based on measured properties of permeate, non-permeate, and feed flows.