Abstract: A method can include receiving seismic image data of a geologic region and interpretation information of the seismic image data for a geologic feature in the geologic region, where the seismic image data include a geologic feature class imbalance; shifting the geologic feature class imbalance toward a class of the geologic feature by increasing the spatial presence of the geologic feature in the seismic image data to generate training data; and training a neural network using the training data to generate a trained neural network.

Abstract: Systems, methods, and non-transitory computer-readable media for processing geological data, of which the method includes receiving a geological data processing tool at a client system. The geological data processing tool includes artificial intelligence, and the geological data processing tool is generated by a geological processing tool provider. The method also includes obtaining training data for the geological data processing tool. The training data includes a plurality of labels. The method also includes training the geological data processing tool based on the training data, receiving data representing a physical, subterranean volume, identifying one or more geological features in the subterranean volume by using the geological data processing tool after training the geological data processing tool, and modifying, using the client system, one or more parameters of the geological data processing tool, or one or more labels of the plurality of labels.

Abstract: A method can include selecting a type of geophysical data; selecting a type of algorithm; generating synthetic geophysical data based at least in part on the algorithm; training a deep learning framework based at least in part on the synthetic geophysical data to generate a trained deep learning framework; receiving acquired geophysical data for a geologic environment; implementing the trained deep learning framework to generate interpretation results for the acquired geophysical data; and outputting the interpretation results.

Abstract: A method is disclosed and includes receiving a seismic cube. The seismic cube includes a three-dimensional image of a portion of a subsurface area. The method further includes providing the seismic cube to a machine learning process. The machine learning process includes one or more neural networks used for predicting a location of a subsurface seismic layer in the received seismic cube. The method also includes receiving, from the machine learning process, the prediction of the location of the subsurface seismic layer in the seismic cube.

Abstract: A method can include receiving points representative of at least a portion of a surface of a multi-dimensional geobody; partitioning the points; computing smooth compactly supported basis functions based at least in part on differential surface areas associated with the partitioning of the points; approximating an indicator function for a body based at least in part on the computed basis functions; and, based at least in part on values of the approximated indicator function, generating a mesh that represents a surface of the body.

Abstract: Systems, computer-readable media, and methods for generating machine learning training data by obtaining reservoir data, determining subsections of the reservoir data, labeling the subsections of the reservoir data to generate labeled reservoir data, and feeding the labeled reservoir data into an artificial neural network. The reservoir data can be labeled using analysis data or based on interpretive input from an interpreter.

Abstract: A method for detecting an unknown fault in a target seismic volume. The method includes generating a number of patches from a training seismic volume that is separate from the target seismic volume, where a patch includes a set of training areas, generating a label for assigning to the patch, where the label represents a subset, of the set of training areas, intersected by an known fault specified by a user in the training seismic volume, training, during a training phase and based at least on the label and the training seismic volume, a machine learning model, and generating, by applying the machine learning model to the target seismic volume during a prediction phase subsequent to the training phase, a result to identify the unknown fault in the target seismic volume.

Abstract: Systems and methods for speed-adjustable model navigation are provided. In aspects, a model platform includes a model engine and a speed tool that operates with the model engine to generate a graphical view of a geological model. Various features of the geological object may be encoded or reflected in the geological model, including the composition, pressure, temperate, structure, fracture lines, and other aspects of a hydrocarbon deposit, cavity, or other geological structure. The user may operate the speed tool to examine the histogram of color or intensity of the pixels or voxels of regions of the model view, and set a speed curve to control how quickly or slowly a cursor or other control may move through or traverse a region, based on the color, intensity, or other value. Regions of interest may be explored more efficiently and accurately.

Abstract: A method can include receiving points representative of at least a portion of a surface of a multi-dimensional geobody; partitioning the points; computing smooth compactly supported basis functions based at least in part on differential surface areas associated with the partitioning of the points; approximating an indicator function for a body based at least in part on the computed basis functions; and, based at least in part on values of the approximated indicator function, generating a mesh that represents a surface of the body.

Abstract: Systems and methods for speed-adjustable model navigation are provided. In aspects, a model platform includes a model engine and a speed tool that operates with the model engine to generate a graphical view of a geological model. Various features of the geological object may be encoded or reflected in the geological model, including the composition, pressure, temperate, structure, fracture lines, and other aspects of a hydrocarbon deposit, cavity, or other geological structure. The user may operate the speed tool to examine the histogram of color or intensity of the pixels or voxels of regions of the model view, and set a speed curve to control how quickly or slowly a cursor or other control may move through or traverse a region, based on the color, intensity, or other value. Regions of interest may be explored more efficiently and accurately.