Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.

Abstract: A system and method that include receiving offset well data collected from an offset well, wherein the offset well data comprises data representing a trajectory of an offset. The system and method also include receiving subject well data comprising a trajectory of at least a portion of a subject. The system and method additionally include determining a similarity value between the trajectory of the offset well and the subject well. The system and method also include selecting at least one offset well for offset well analysis based on the similarity value. The system and method further include adjusting at least one parameter of the subject well based on the offset well analysis.

Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Abstract: A system and method that include receiving offset well data collected from an offset well, wherein the offset well data comprises data representing a trajectory of an offset. The system and method also include receiving subject well data comprising a trajectory of at least a portion of a subject. The system and method additionally include determining a similarity value between the trajectory of the offset well and the subject well. The system and method also include selecting at least one offset well for offset well analysis based on the similarity value. The system and method further include adjusting at least one parameter of the subject well based on the offset well analysis.

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Abstract: A method for offset well analysis includes receiving offset well data collected from an offset well, the offset well data including data representing a trajectory of an offset well, receiving subject well data comprising a trajectory of at least a portion of a subject well, partitioning the trajectory of the offset well into a plurality of offset well segments, partitioning the trajectory of the subject well into a plurality of subject well segments, determining a distance between at least some of the plurality of offset well segments and at least some of the plurality of subject well segments, selecting the offset well based in part on the distance, and performing an offset well analysis using the offset well and the subject well.

Abstract: A method may include obtaining input data including an environmental condition and chemical properties of input components of an input fluid mixture, encoding, by an encoder machine learning model, the input data to obtain encoded input data, and receiving, by an aggregator function and from the encoder machine learning model, the encoded input data ordered in a sequence corresponding to an order of the input components. The method may further include aggregating, by the aggregator function, the encoded input data to obtain aggregated input data. The aggregated input data may be independent of the sequence. The method may further include decoding, by a decoder machine learning model, the aggregated input data to obtain output data including a phase for an output mixture, and presenting the output data.

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Abstract: A method, apparatus, and program product utilize a disentangled factor learning framework to analyze petro-technical image data such as seismic image data to infer properties of a subsurface volume and/or to generate image data for use in training machine learning algorithms for use in petro-technical applications.

Abstract: A method, apparatus, and program product may utilize data associated with one or more electric submersible pumps (ESPs) to train a machine learning model and/or use a machine learning model to perform ESP failure analysis. In addition, one or more features from the data may be encoded into a machine-readable format to facilitate ingestion by the machine learning model.

Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.

Abstract: A subsurface structure identification system includes one or more processors and a memory coupled to the one or more processors. The memory is encoded with instructions that when executed by the one or more processors cause the one or more processors to provide a convolutional neural network trained to identify a subsurface structure in an input migrated seismic volume, and to partition the input migrated seismic volume into multi-dimensional sub-volumes of seismic data. The instructions also cause the one or more processors to process each of the multi-dimensional sub-volumes of seismic data in the convolutional neural network, and identify the subsurface structure in the input migrated seismic volume based on a probability map of the input migrated seismic volume generated by the convolutional neural network.

Abstract: A method, apparatus, and program product utilize a disentangled factor learning framework to analyze petro-technical image data such as seismic image data to infer properties of a subsurface volume and/or to generate image data for use in training machine learning algorithms for use in petro-technical applications.

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Abstract: A method for offset well analysis includes receiving offset well data collected from an offset well, the offset well data including data representing a trajectory of an offset well, receiving subject well data comprising a trajectory of at least a portion of a subject well, partitioning the trajectory of the offset well into a plurality of offset well segments, partitioning the trajectory of the subject well into a plurality of subject well segments, determining a distance between at least some of the plurality of offset well segments and at least some of the plurality of subject well segments, selecting the offset well based in part on the distance, and performing an offset well analysis using the offset well and the subject well.