Abstract: Pump maintenance data associated with each of plural components of each of plural frac pumps of each of plural frac pump transports of a frac fleet is accessed. The pump maintenance data includes usage data of plural well completion operations performed by the frac fleet. The usage data indicates actual component life intervals of each of the plural components and corresponding operating conditions. The pump maintenance data is analyzed to generate a maintenance prediction model that is configured to output for each of the plural components, a predicted component life interval as a function of an operating condition. A current operating condition corresponding to a current well completion operation of the frac fleet is accessed. The maintenance prediction model is applied to generate the predicted component life intervals for each of the plurality of components of the frac fleet based on the current operating condition.

Abstract: Activity data associated with a fleet move is received. The activity data is associated with performance of activities for one or more events according to a move play prescribed for the fleet move. A plurality of features associated with the activity data are extracted. The extracted features are input into a machine learning model that is trained based on features of historical activity data of historical fleet moves. A probability of the fleet move having the prescribed move play failing to complete within a predetermined timeframe is received as output from the machine learning model. In response to the probability being higher than a threshold, one or more recommendations configured to improve a likelihood of the fleet move being completed within the predetermined timeframe or reduce overtime, are generated. A graphical user interface of a client device is caused to display the one or more recommendations.

Abstract: Well completion design data for completing plural stages of a well completion operation is accessed. Predicted component life intervals for each of a plurality of components of a frac fleet are accessed. Remaining component life intervals for each of the plurality of components of the frac fleet are determined based on the predicted component life intervals. A continuous completion event for the well completion operation is set based on the remaining component life intervals for each of the plurality of components. The continuous completion event includes a continuous pumping block identifying one or more contiguous stages from among the plurality of stages during which the well completion operation is performed with the frac fleet, and a maintenance block identifying one or more of the plurality of frac pumps to be subject to predetermined maintenance operations upon completion of the one or more contiguous stages of the continuous pumping block.

Abstract: Well completion design data for completing plural stages of a well completion operation is accessed. Predicted component life intervals for each of a plurality of components of a frac fleet are accessed. Remaining component life intervals for each of the plurality of components of the frac fleet are determined based on the predicted component life intervals. A continuous completion event for the well completion operation is set based on the remaining component life intervals for each of the plurality of components. The continuous completion event includes a continuous pumping block identifying one or more contiguous stages from among the plurality of stages during which the well completion operation is performed with the frac fleet, and a maintenance block identifying one or more of the plurality of frac pumps to be subject to predetermined maintenance operations upon completion of the one or more contiguous stages of the continuous pumping block.

Abstract: Pump maintenance data associated with each of plural components of each of plural frac pumps of each of plural frac pump transports of a frac fleet is accessed. The pump maintenance data includes usage data of plural well completion operations performed by the frac fleet. The usage data indicates actual component life intervals of each of the plural components and corresponding operating conditions. The pump maintenance data is analyzed to generate a maintenance prediction model that is configured to output for each of the plural components, a predicted component life interval as a function of an operating condition. A current operating condition corresponding to a current well completion operation of the frac fleet is accessed. The maintenance prediction model is applied to generate the predicted component life intervals for each of the plurality of components of the frac fleet based on the current operating condition.

Abstract: Well completion design data for completing plural stages of a well completion operation is accessed. Predicted component life intervals for each of a plurality of components of a frac fleet are accessed. Remaining component life intervals for each of the plurality of components of the frac fleet are determined based on the predicted component life intervals. A continuous completion event for the well completion operation is set based on the remaining component life intervals for each of the plurality of components. The continuous completion event includes a continuous pumping block identifying one or more contiguous stages from among the plurality of stages during which the well completion operation is performed with the frac fleet, and a maintenance block identifying one or more of the plurality of frac pumps to be subject to predetermined maintenance operations upon completion of the one or more contiguous stages of the continuous pumping block.

Abstract: Activity data associated with a fleet move is received. The activity data is associated with performance of activities for one or more events according to a move play prescribed for the fleet move. A plurality of features associated with the activity data are extracted. The extracted features are input into a machine learning model that is trained based on features of historical activity data of historical fleet moves. A probability of the fleet move having the prescribed move play failing to complete within a predetermined timeframe is received as output from the machine learning model. In response to the probability being higher than a threshold, one or more recommendations configured to improve a likelihood of the fleet move being completed within the predetermined timeframe or reduce overtime, are generated. A graphical user interface of a client device is caused to display the one or more recommendations.