Well Pattern Optimization for Gas Reservoir Compressed Air Energy Storage Considering Multifactor Constraints

Ming Yue (), Chaoran Wei, Mingqi Jia, Kun Dai, Weiyao Zhu and Hongqing Song
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Ming Yue: School of Resources and Safety Engineering, Beijing University of Science and Technology, Beijing 100083, China
Chaoran Wei: School of Resources and Safety Engineering, Beijing University of Science and Technology, Beijing 100083, China
Mingqi Jia: School of Resources and Safety Engineering, Beijing University of Science and Technology, Beijing 100083, China
Kun Dai: CNPC Engineering Technology R&D Company Limited, Beijing 102206, China
Weiyao Zhu: School of Resources and Safety Engineering, Beijing University of Science and Technology, Beijing 100083, China
Hongqing Song: School of Resources and Safety Engineering, Beijing University of Science and Technology, Beijing 100083, China

Energies, 2025, vol. 18, issue 22, 1-24

Abstract: As an effective energy storage solution, gas reservoir compressed air energy storage (CAES) can efficiently utilize curtailed wind power to meet urban electricity demands. Well pattern optimization enables rational design and adjustment of well layouts to maximize productivity, efficiency, and economic benefits while reducing energy losses and operational costs. To address limitations in conventional optimization methods—including oversimplified constraints, neglect of reservoir heterogeneity, and insufficient consideration of complex flow regimes—this study proposes an innovative multi-constraint well pattern optimization method incorporating productivity, energy conversion efficiency, drainage area, and economic performance for quantitative evaluation of well configurations. First, the reservoir flow domain was partitioned based on two flow regimes (Darcy and non-Darcy flow) near wells. Mathematical flow equations accounting for reservoir heterogeneity were established and solved using the rectangular grid method to determine productivity and formation pressure distributions for vertical and horizontal wells. Second, a drainage radius prediction model was developed based on pressure drop superposition principles to calculate gas drainage areas. Finally, an optimization function F, integrating productivity models and drainage radius calculations through ratio optimization criteria, was formulated to quantitatively characterize well pattern performance. An optimization workflow adhering to inter-well interference minimization principles was designed, culminating in a comprehensive CAES well pattern optimization framework. Case studies and sensitivity analyses on the depleted Mabei Block 8 CAES reservoir demonstrated the following: The quantitative optimization metric w decreases with increasing reservoir heterogeneity. w exhibits a unimodal relationship with production pressure differential, peaking at approximately 2.5 MPa. Optimal configuration was achieved with 3 horizontal wells and 23 vertical wells.

Keywords: compressed air energy storage; well pattern optimization; multifactor constraints; reservoir heterogeneity; production capacity (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: 2025
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