Distributed machine learning control of electric submersible pumps

A system comprising:

• a network of a plurality of electric submersible pumps (ESPs), wherein each of the plurality of ESPs includes a motor, a processor, a deep learning model, and at least one sensor, and wherein each of the plurality of ESPs are located in a different wellbore of a plurality of wellbores
• a centralized computing system networked with the network of the plurality of ESPs, the centralized computing system having installed thereon a reservoir model;
• program code stored in memory and executable by the processor on each ESP of the plurality of ESPs to:
• generate by a deep learning model associated with the each ESP, predicted operating conditions for each ESP based on measurement data from at the at least the one sensor associated with each ESP;
• send the predicted operating conditions to the centralized computing system;
• and
• adjust operation of the motor of at least a first ESP of the plurality of ESPs based on operating parameters obtained from the central deep learning model executing on the centralized computing system;
• program code stored in memory and executable by the centralized computing system to:
• obtain from the reservoir model the operating parameters for at least the first ESP based on the predicted operating conditions, wherein the reservoir model is a mathematical model of fluid flows and geologic properties of a plurality of wellbores in which the network of the plurality of ESPs are deployed and flows and properties of reservoir rocks that connect the plurality of wellbores;
• and
• send the operating parameters to at least the first ESP of the plurality of ESPs.

A method comprising:

• based on measurement data of at least one electric submersible pump (ESP) networked with a plurality of ESPs, obtaining from a first deep learning model operating conditions of the at least one ESP, wherein the first deep learning model comprises a model corresponding to a portion of a reservoir model associated with a first wellbore, wherein each of the plurality of ESPs includes a motor controller and a deep learning model associated with each ESP, and wherein each of the plurality of ESPs are located in a different wellbore of a plurality of wellbores;
• and
• adjusting, by the motor controller associated with the at least one ESP, operation of a motor of the at least one ESP based on the operating conditions;
• and
• wherein the reservoir model is a mathematical model of fluid flows and geologic properties of a plurality of wellbores in which the network of the plurality of ESPs are deployed and flows and properties of reservoir rocks that connect the plurality of wellbores.