A meta-optimized hybrid global and local algorithm for well placement optimization. (2nd September 2018)

• Record Type: Journal Article
• Authors: Chen, Hongwei; Feng, Qihong; Zhang, Xianmin; Wang, Sen; Ma, Zhiyu; Zhou, Wensheng; Liu, Chen
• Is Part Of: Computers & chemical engineering. Volume 117(2018)
• Page Start: 209
• Page End: 220
• Publication Date: 2018-09-02
• Subjects: Well placement -- Optimization efficiency -- Hybrid optimization algorithm -- Meta-optimization approach -- Cat swarm optimization algorithm -- Mesh Adaptive Direct Search; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.06.013
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: An efficient algorithm with local and global searching ability is proposed. Well placement, type, and joint optimization of placement and control are studied. Parameters found by meta-optimized approach improve optimization performance. O-CSMADS outperforms CSO, MADS and CSMADS in three cases. Abstract: Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling Cat Swarm Optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle Swarm Optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control.