Abstract: A method, apparatus, and program product utilize a super resolution machine learning model to reconstruct high resolution seismic data from low resolution seismic data in connection with generating seismic visualizations, e.g., to reduce storage and/or communication costs associated with generating seismic visualizations.

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 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 method implements property modeling using attentive neural processes. The method includes receiving sparse context input comprising a plurality of context locations corresponding to a plurality of geological property values for a geological property and selecting a plurality of target locations in a space of the plurality of context locations. The method further includes generating a predicted mean image for the geological property by an attentive neural process model using the plurality of target locations and the sparse context input and presenting the predicted mean image.

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 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 method, apparatus, and program product utilize a super resolution machine learning model to reconstruct high resolution seismic data from low resolution seismic data in connection with generating seismic visualizations, e.g., to reduce storage and/or communication costs associated with generating seismic visualizations.

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.