Abstract: A method for training a backpropagation-enabled segmentation process is used for identifying an occurrence of a sub-surface feature. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A prediction of the occurrence of the subsurface feature has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

Abstract: A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

Abstract: A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

Abstract: A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

Abstract: A method for training a backpropagation-enabled segmentation process is used for identifying an occurrence of a sub-surface feature. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A prediction of the occurrence of the subsurface feature has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

Abstract: A method for assessing risk to a marine hydrocarbon recovery operation involves collecting a set of training images and labeling sea surface anomalies on the set of training images. The set of training images and associated labels are used to train a model via backpropagation. A set of non-training images is collected and the trained model is applied to identify a potentially disruptive sea surface anomaly on the set of non-training images. Any risk to the marine hydrocarbon recovery operation by the potentially disruptive sea surface anomaly is then assessed.

Abstract: A backpropagation-enabled method for identifying a sea surface anomaly involves collecting an initial set of images and labeling the anomaly on the initial set of images. The initial set of images are selected from satellite-acquired images and/or simulated satellite images. The labels are used to train a model via backpropagation. A subsequent set of images, including satellite-acquired, airborne-acquired images, and combinations thereof, is collected and the trained model is applied to identify a sea surface anomaly on the subsequent set of images.

Abstract: A distribution of sand sediment class in a subsurface of the Earth is identified. Three-dimensional stratigraphic images of sediment distribution in a subsurface of the Earth are constructed, including at least two depositional domains, by simulating sediment transport and deposition over a selected time interval of sediments originating from one or more clastic sediment input sources. The simulation uses at least two equilibrium surfaces bounding the at least two depositional domains and at least two sediment classes, and updates the equilibrium surfaces in a sequence of timesteps, by accounting for sediment feed from the one or more clastic sediment input sources, erosion and deposition in accordance with mass-balance equality constraints. The images are used to identify a distribution of sand sediment class in the subsurface of the Earth suitable for acquiring hydrocarbons or fresh water, or for storing gas or liquids.

Abstract: A method for producing a synthetic model for training a backpropagation-enabled process for identifying subsurface features, includes generating synthetic subsurface models with realizations of subsurface features. The synthetic subsurface models are generated by introducing at least three distinct model variations selected from geologically realistic features simulating the outcome of a geologic process, simulations of geologic processes, simulations of noise sources, and combinations thereof. Labels are applied to one or more of the subsurface features in one or more of the synthetic subsurface models. The labels and the corresponding synthetic subsurface models are imported into the backpropagation-enabled process for training.

Abstract: A method for producing a synthetic model for training a backpropagation-enabled process for identifying subsurface features, includes generating noise-free synthetic subsurface models with realizations of subsurface features. The noise-free synthetic subsurface models are generated by introducing a model variation selected from geologically realistic features simulating the outcome of a geologic process, simulations of geologic processes, and combinations thereof. Labels are applied to one or more of the subsurface features in one or more of the synthetic subsurface models. A simulation of a noise source is applied to a copy of one or more of the noise-free synthetic subsurface models to produce a noise-augmented copy. The labels and the corresponding synthetic subsurface models are imported into the backpropagation-enabled process for training.

Abstract: A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.

Abstract: The computer system and computer-implemented method allow a user to position an interactive cursor my interaction with a user-input device, to select a point anywhere within a 3D seismic data volume that is visible on a display. In response, the computer dynamically calculates a horizon-based stratal slice that includes the user-selected point. A selected attribute rendering from seismic data that is contained within the horizon-based stratal slice is automatically calculated and dynamically shown on a second display.

Abstract: The computer system and computer-implemented method allow a user to position an interactive cursor my interaction with a user-input device, to select a point anywhere within a 3D seismic data volume that is visible on a display. In response, the computer dynamically calculates a horizon-based stratal slice that includes the user-selected point. A selected attribute rendering from seismic data that is contained within the horizon-based stratal slice is automatically calculated and dynamically shown on a second display.

Abstract: A distribution of sand sediment class in a subsurface of the Earth is identified. Three-dimensional stratigraphic images of sediment distribution in a subsurface of the Earth are constructed, including at least two depositional domains, by simulating sediment transport and deposition over a selected time interval of sediments originating from one or more clastic sediment input sources. The simulation uses at least two equilibrium surfaces bounding the at least two depositional domains and at least two sediment classes, and updates the equilibrium surfaces in a sequence of timesteps, by accounting for sediment feed from the one or more clastic sediment input sources, erosion and deposition in accordance with mass-balance equality constraints. The images are used to identify a distribution of sand sediment class in the subsurface of the Earth suitable for acquiring hydrocarbons or fresh water, or for storing gas or liquids.