Abstract: A method can include, for a control action for a hydraulic fracturing operation of a well, using a trained machine learning model that predicts treatment pressure of the hydraulic fracturing operation, determining if the control action increases efficiency; if the control action increases efficiency, assessing viability of the control action with respect to one or more predefined criteria; and, if the control action is viable, issuing the control action for implementation during the hydraulic fracturing operation.

Abstract: A system for determining pressure in a hydraulic fracturing system for a well includes a processing module executing code and configured to receive a plurality of input parameters. The processing module can predict either a bottomhole pressure, based on statistical predictions and physics-based predictions, or a surface pressure based on the predicted bottomhole pressure.

Abstract: Systems and methods presented herein relate to systems and methods for determining a number of effective perforation clusters created during hydraulic fracturing operations performed using wellsite equipment of a wellsite system based on surface data collected in substantially real-time during the hydraulic fracturing operations using an autoencoder/convolutional neural network architecture. In certain embodiments, the wellsite equipment of the wellsite system may be controlled in substantially real-time based on the determined number of effective perforation clusters insofar as the autoencoder/convolutional neural network architecture facilitates such real-time responsiveness.

Abstract: A system for determining pressure in a hydraulic fracturing system for a well includes a processing module executing code and configured to receive a plurality of input parameters. The processing module can predict either a bottomhole pressure, based on statistical predictions and physics-based predictions, or a surface pressure based on the predicted bottomhole pressure.