Abstract: A method receives, from one or more subsystems, predicted outcomes associated with a device. The method provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The method receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The method identifies a first resolution action from the set of resolution actions that has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The method identifies a root cause associated with the first resolution action. The method provides a first instruction to execute the first resolution action.

Abstract: A system receives a plurality of data streams, including a plurality of data points, associated with properties of a device. The system generates, for each data stream, a data set that includes at least a specified number of data points over a prior time period. The system identifies a first data stream that represents a property to be predicted for a future time period. The system generates a joined data set that includes a subset of the data sets not including a first data set of the first data stream. The joined data set and the first data set are inputted into a trained machine learning model, trained to output a predicted value of the first property for the future time period. The predicted value of the first property indicates an anomaly, and a resolution action for preventing the anomaly from occurring at the future time period is identified.

Abstract: A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

Abstract: A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

Abstract: Drilling data including a plurality of values measured for each of a plurality of drilling parameters is received. An objective function that maximizes production of a material produced by a production operation, minimizes water usage by the production operation, and minimizes a sub-cool temperature of the material is determined using the received drilling data. An association rule that defines a range of values for a control parameter of the plurality of drilling parameters that is selected based on a value of a first parameter of the plurality of drilling parameters is determined using the received drilling data. Measured drilling data that indicates current control parameter values of the production operation is received. An optimal value for the control parameter is determined by executing the determined objective function with the received, measured drilling data as an input and subject to the determined association rule. The determined optimal value is output.