Fast Joint Optimization of Well Placement and Control Strategy Based on Prior Experience and Quasi-Affine Transformation

Author:

Wang Haochen1,Zhang Kai234ORCID,Liu Chengcheng3,Zhang Liming24

Affiliation:

1. Geosteering & Logging Research Institute, Sinopec Matrix Co., Ltd., Qingdao 266001, China

2. State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China

3. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China

4. School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China

Abstract

Well placement optimization is one of the most important means to control the decline of oilfields and improve the recovery rate in the development process of deep and heterogeneous reservoirs, such as deep buried carbonate oil reservoirs. However, the mapping relationship from deployed well positions to actual profits is non-linear and multi-modal. At the same time, the injection and production relationship of new wells also affects the contribution of well positions to final profits. Currently, common algorithms include gradient-based and heuristic non-gradient algorithms, which have advantages, but face problems of high computational complexity, slow optimization speed, and difficulty in convergence. We propose an evolutionary algorithm for well placement optimization in carbonate reservoirs. This algorithm improves well placement optimization and computational speed by constraining the sampling process to effective sampling spaces, integrating prior knowledge to enhance sampling efficiency, strengthening local optima exploration, and utilizing parallel computing. Additionally, it refines the optimized variable content based on actual control factors, enhancing the algorithm’s robustness in practical applications. A case study from a carbonate reservoir in northwestern China demonstrated that this algorithm not only improved the performance by 50% compared to the classic DE algorithm but also achieved 15% higher optimization effectiveness than the current state-of-the-art algorithms.

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Major Scientific and Technological Projects of CNOOC

111 Project

Publisher

MDPI AG

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