Abstract: A method may include receiving a sustainability model indicative of expected sustainability parameters associated with implementing action plans that correspond to enterprise operations of an enterprise over a period of time, such that the enterprise operations correspond to production data performed in a hydrocarbon production system, facility data corresponding to buildings associated with the enterprise, or both. The method may also include receiving an indication to optimize one sustainability parameter and identifying an engineering workflow system that improves a first sustainability parameter associated with the one sustainability parameter. The method may involve sending the sustainability model to the engineering workflow system and receiving an action plan to improve the sustainability parameter, such that commands may be sent to devices based on the action plan to cause the one or more devices to adjust one or more respective operations.

Abstract: A method includes receiving an updated sustainability report associated with enterprise operations of an enterprise, such that the enterprise operations correspond to production data corresponding to operational tasks performed in a hydrocarbon production system, facility data corresponding to utility operations within buildings associated with the enterprise, or both. A computing system performing the method previously received a sustainability report associated with the enterprise operations. The method involves identifying at least one sustainability parameter change between the sustainability report and the updated sustainability report that is greater than at least one threshold, identifying engineering workflow systems to determine action plans associated with improving the at least one sustainability parameter, and sending the updated sustainability report to the one or more engineering workflow systems.

Abstract: An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system. The sustainability platform system may obtain input data from one or more input data sources, and determine a respective authority level for each of the input data sources. The sustainability platform system may also determine uncertainty data associated with the input data based on the respective authority level for each of the input data sources, determine confidence parameters associated with the input data based on the uncertainty data, generate one or more sustainability action plans for improving the sustainability parameters of the enterprise based on the input data stored in the database, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans.

Abstract: Techniques for semantic segmentation of images are presented. The techniques include obtaining a semantic segmentation model, the semantic segmentation model having been trained using a first subset of sliding windows and corresponding first ground truth masks for the first subset of sliding windows; ranking a plurality of sliding windows from a corpus of training images according to an uncertainty metric; selecting a next subset of sliding windows from the corpus of training images based on the ranking and based on a similarity metric for one or more characteristics of a sliding window relative to other sliding windows; providing a collaborative user interface; receiving ground truth masks for the next subset of sliding windows using the collaborative user interface; and retraining the semantic segmentation model using the next subset of sliding windows and the ground truth masks for the next subset of sliding windows.

Abstract: Techniques for semantic segmentation of images are presented. The techniques include obtaining a semantic segmentation model, the semantic segmentation model having been trained using a first subset of sliding windows and corresponding first ground truth masks for the first subset of sliding windows; ranking a plurality of sliding windows from a corpus of training images according to an uncertainty metric; selecting a next subset of sliding windows from the corpus of training images based on the ranking and based on a similarity metric for one or more characteristics of a sliding window relative to other sliding windows; providing a collaborative user interface; receiving ground truth masks for the next subset of sliding windows using the collaborative user interface; and retraining the semantic segmentation model using the next subset of sliding windows and the ground truth masks for the next subset of sliding windows.

Abstract: A method implements machine learning based methane emissions monitoring. The method includes collecting sensor data from a plurality of sensors. The method further includes applying an augmentation model to the sensor data to form a regression training set. The method further includes creating a classification training set for a classification model by replacing regression output values from the regression training set with classification output values. The classification output values include binary values. The method further includes training the regression model with the regression training set to generate a regression prediction. The method further includes training the classification model with the classification training set to generate a classification prediction.

Abstract: An active learning framework is provided that employs a plurality of machine learning components that operate over iterations of a training phase followed by an active learning phase. In each iteration of the training phase, the machine learning components are trained from a pool of labeled observations. In the active learning phase, the machine learning components are configured to generate metrics used to control sampling of unlabeled observations for labeling such that newly labeled observations are added to a pool of labeled observations for the next iteration of the training phase. The machine learning components can include an inspection (or primary) learning component that generates a predicted label and uncertainty score for an unlabeled observation, and at least one additional component that generates a quality metric related to the unlabeled observation or the predicted label. The uncertainty score and quality metric(s) can be combined for efficient sampling of observations for labeling.

Abstract: A method for controlling a subsea vehicle. The method includes receiving sensor data representing a subsea environment from one or more sensors of the subsea vehicle. The method identifies one or more objects present in the subsea environment based on the sensor data using an artificial intelligence machine. The method transmits at least a portion of the sensor data, including an identification of the one or more objects, to a user interface. The method includes receiving a requested vehicle task from the user interface. The requested vehicle task being selected by a user via the user interface. The method performs the requested vehicle task without vehicle position control from the user.

Abstract: Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

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: A method can include receiving labeled images; acquiring unlabeled images; performing active learning by training an inspection learner using at least a portion of the labeled images to generate a trained inspection learner that outputs information responsive to receipt of one of the unlabeled images by the trained inspection learner; based at least in part on the information, making a decision to call for labeling of the one of the unlabeled images; receiving a label for the one of the unlabeled images; and further training the inspection learner using the label.

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 can include receiving seismic image data; processing the received seismic image data to generate stratigraphic information using a trained convolution neural network that includes channels subjected to convolution, activation and pooling that reduce spatial resolution and subjected to deconvolution and concatenation that increase spatial resolution; and enhancing the seismic image data using the stratigraphic information to generate an enhanced seismic image.

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: An active learning framework is provided that employs a plurality of machine learning components that operate over iterations of a training phase followed by an active learning phase. In each iteration of the training phase, the machine learning components are trained from a pool of labeled observations. In the active learning phase, the machine learning components are configured to generate metrics used to control sampling of unlabeled observations for labeling such that newly labeled observations are added to a pool of labeled observations for the next iteration of the training phase. The machine learning components can include an inspection (or primary) learning component that generates a predicted label and uncertainty score for an unlabeled observation, and at least one additional component that generates a quality metric related to the unlabeled observation or the predicted label. The uncertainty score and quality metric(s) can be combined for efficient sampling of observations for labeling.

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: Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

Abstract: A method for controlling a subsea vehicle. The method includes receiving sensor data representing a subsea environment from one or more sensors of the subsea vehicle. The method identifies one or more objects present in the subsea environment based on the sensor data using an artificial intelligence machine. The method transmits at least a portion of the sensor data, including an identification of the one or more objects, to a user interface. The method includes receiving a requested vehicle task from the user interface. The requested vehicle task being selected by a user via the user interface. The method performs the requested vehicle task without vehicle position control from the user.

Abstract: A method can include receiving seismic image data; processing the received seismic image data to generate stratigraphic information using a trained convolution neural network that includes channels subjected to convolution, activation and pooling that reduce spatial resolution and subjected to deconvolution and concatenation that increase spatial resolution; and enhancing the seismic image data using the stratigraphic information to generate an enhanced seismic image.