US 2026/0219640 A1 · REINFORCEMENT LEARNING INFORMED CONTROL OF NONLINEAR SYSTEMS WITH APPLICATIONS IN HYDROCARBON EXTRACTION

Abstract

Systems and methods for hydrocarbon extraction. The controller first calculates a weighting parameter that depends on the distance between the current operating point and a boundary of a desired operating region. The weighting parameter is used to train a reinforcement learning algorithm based on a trade-off between performance and operating within the desired operating region. The reinforcement learning algorithm generates a nominal control action which is corrected by a quadratic programming optimizer configured to adjust the nominal control action a minimal amount while ensuring that the operating point remains in the desired operating region. During operating changes the constraints forcing the control into the desired operating region may become infeasible and the reinforcement learning algorithm is configured to drive the operating point towards the desired operating region by way of the nominal control action.

Claims20

Dependent claims

A system for controlling a hydrocarbon extraction site, the system comprising:

one or more processing circuits configured to:
calculate a bounding parameter based on a distance between an operating point and a boundary of a desired operating region;
generate a nominal control action for the hydrocarbon extraction site based on a reinforcement learning model;
generate one or more operating constraints based on the bounding parameter;
and
determine an implemented control action that (i) satisfies the one or more operating constraints and (ii) is based on the nominal control action, wherein the hydrocarbon extraction site is operated in accordance with the implemented control action.

3 more elements

A system for controlling a hydrocarbon extraction site, the system comprising one or more processing circuits configured to:

calculate a bounding parameter based on a distance between an operating point and a boundary of a desired operating region;
generate, using a machine learning model, one or more nominal control parameters for a control algorithm configured to control a variable of the hydrocarbon extraction site according to a setpoint;
generate one or more operating constraints for one or more implemented control parameters based on the bounding parameter, the one or more operating constraints configured to constrain a control action generated according to the control algorithm using the one or more implemented control parameters to maintain the variable within the desired operating region;
and
determine the one or more implemented control parameters that (i) satisfy the one or more operating constraints and (ii) are based on the one or more nominal control parameters, wherein the hydrocarbon extraction site is operated in accordance with the one or more implemented control parameters.

2 more elements

Dependencies are inferred from the claim text.

Reinforcement learning informed control of nonlinear systems with applications in hydrocarbon extraction

Life of this patent

Reinforcement learning informed control of nonlinear systems with applications in hydrocarbon extraction

Life of this patent

Abstract

Claims20