Revolutionizing Oil Field Operations: Implementing Multivariable Predictive Control to Drive Autonomous Well Control Operations

Abstract

The objective of this paper is to present the utilization of Multivariable Predictive Control (MPC) technology in the implementation of an Autonomous Well Control (AWC) solution for an Onshore asset. MPC is an advanced control strategy used in various industries, including oil and gas, and uses mathematical models of the system dynamics to predict future behavior and optimize control actions over a specified time horizon. The paper presents an overview of the Multivariable Predictive Control strategy implemented for autonomous well control operations. It also describes how MPC technology can streamline well-controlled operations, reducing downtime and optimizing production output. Implementation of an MPC scheme at the field level involves several critical components and considerations to ensure it operates efficiently and achieves the desired control objectives. A key requirement for MPC is a reliable and accurate model of the process to be controlled. This model should be able to predict the system behavior based on current and past data. Developing an appropriate control strategy that includes defining the control objectives and constraints and tuning the parameters of the MPC is fundamental to ensure it responds appropriately to changes in the system's behavior. Effective MPC implementation also depends on the integration of the MPC with existing regulatory control systems. This includes interfacing with the control room hardware and software for data acquisition, control execution, and feedback. Before full-scale deployment, the MPC system should be rigorously tested and validated under various operating conditions to ensure it behaves as expected and can handle real-world disturbances and changes. MPC implementation has significantly enhanced control over oil and gas wells, ensuring operation within recommended ranges. Furthermore, it supports operators and production engineers in sustaining production targets by employing optimization algorithms to calculate new set points for production choke and artificial lift systems, optimize energy usage, and adhere to operational and reservoir guidelines. The success of this project underscores the transformative potential of MPC, a specific type of Advanced Process Control (APC) technology, in driving efficiency, maximizing profitability, and enabling a culture of digitalization for the Upstream sector. The paper presents a novel implementation of MPC for the control of well operation. It offers fresh perspectives on the hurdles of deploying MPC technology in upstream operations. It introduces an innovative strategy, merging physics with AI models to enable multi-variable control and optimization of oil and gas wells. This novel approach has demonstrated enhanced operational efficiency and performance, marking a significant advancement in the industry's quest for autonomous well management.