Application of the ALRW-DDPG Algorithm in Offshore Oil–Gas–Water Separation Control

Xiaoyong He, Han Pang (), Boying Liu and Yuqing Chen
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Xiaoyong He: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Han Pang: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Boying Liu: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Yuqing Chen: College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China

Energies, 2024, vol. 17, issue 18, 1-16

Abstract: With the offshore oil–gas fields entering a decline phase, the high-efficiency separation of oil–gas–water mixtures becomes a significant challenge. As essential equipment for separation, the three-phase separators play a key role in offshore oil–gas production. However, level control is critical in the operation of three-phase gravity separators on offshore facilities, as it directly affects the efficacy and safety of the separation process. This paper introduces an advanced deep deterministic policy gradient with the adaptive learning rate weights (ALRW-DDPG) control algorithm, which improves the convergence and stability of the conventional DDPG algorithm. An adaptive learning rate weight function has been meticulously designed, and an ALRW-DDPG algorithm network has been constructed to simulate three-phase separator liquid level control. The effectiveness of the ALRW-DDPG algorithm is subsequently validated through simulation experiments. The results show that the ALRW-DDPG algorithm achieves a 15.38% improvement in convergence rate compared to the traditional DDPG algorithm, and the control error is significantly smaller than that of PID and DDPG algorithms.

Keywords: offshore oil–gas production; ALRW-DDPG algorithm; three-phase separator; liquid level control; slug flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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