Use of Advanced Process Control for Automating Conventional Oilfield Operations

Patel, Kamlesh; Bakhurji, Ammar; Salloum, Hussain; Kim, H..; Winarno, Mulad B.; Mubarak, Saeed

doi:10.2118/192393-ms

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…They included reference flow rate constraints to indirectly minimize ESP power consumption by regulating the motor current. Furthermore, the first practical MPC large-scale implementation in a real onshore oilfield (more than 100 wells), with multiple ESP-lifted oil wells, was presented by Patel et al [6]. Their formulation applied a data-driven step-response model and used the rotational speed, the choke valve opening, and the ESP voltage as manipulated variables to track the reference flow rate setpoint, considering practical ESP constraints, achieving a significant power saving (10% to 20%).…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

“…Although advanced process control techniques, e.g., model predictive control (MPC), can systematically deal with the particularities of ESP-lifted oil wells, providing an optimal and safer approach, it is a common practice to adopt a manual operation in fields equipped with this artificial lift method, as highlighted by Krishnamoorthy et al [4]. In the oil and gas exploration and production (E&P) field, implementing model predictive control (MPC) strategies in an actual ESP facility is a scarce subject, with a few examples treated in the literature, such as Pavlov et al [5] and Patel et al [6].…”

Section: Introduction 1overview Of Esp-lifted Oil-well Systemmentioning

confidence: 99%

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Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

et al. 2023

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This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Section: Introduction 1overview Of Esp-lifted Oil-well Systemmentioning

confidence: 99%

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

et al. 2023

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Machine Learning in Oil & Gas Industry: A Novel Application of Clustering for Oilfield Advanced Process Control

Patel

Patwardhan

2019

SPE Middle East Oil and Gas Show and Conference

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Data Analytics is an emerging area that involves using advanced statistical and machine learning algorithms to discover information & relationsips present in different types of data. The work described in this paper illustrates the application of machine learning techniques to an Oilfield Advanced Process Control (APC) project involving deployment of APC at a large onshore conventional oilfield in Saudi Aramco. APC implementation enables better control and optimization of the production from hundreds of oilwells. APC rollout at the large oilfield involved APC deployment on 300+ oil wells. Using conventional APC implementation methodology, the rollout would be very difficult to manage and would have taken about 3 man years which was not practical. Use of innovative data analytics techniques was essential to ensuring the timely deployment of such a large scale APC project. A machine learning algorithm used to cluster similarly behaving wells, enabled significant (80%) reduction in the engineering effort and operator involvement in developing the models for each well. This allowed the implementation to be completed one year in advance thus realizing the APC benefits earlier than planned.

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

Hernandez,

Matta,

Almadhoun

et al. 2024

Adipec

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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.

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Use of Advanced Process Control for Automating Conventional Oilfield Operations

Cited by 3 publications

References 4 publications

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

Machine Learning in Oil & Gas Industry: A Novel Application of Clustering for Oilfield Advanced Process Control

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

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