Infill Well Placement Optimization for Polymer Flooding in Offshore Oil Reservoirs via an Improved Archimedes Optimization Algorithm with a Halton Sequence

Engao Tang, Jian Zhang, Anlong Xia, Yi Jin, Lezhong Li, Jinju Chen, Biqin Hu and Xiaofei Sun ()
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Engao Tang: State Key Laboratory of Offshore Oil & Gas Exploitation, Beijing 100028, China
Jian Zhang: State Key Laboratory of Offshore Oil & Gas Exploitation, Beijing 100028, China
Anlong Xia: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Yi Jin: State Key Laboratory of Offshore Oil & Gas Exploitation, Beijing 100028, China
Lezhong Li: State Key Laboratory of Offshore Oil & Gas Exploitation, Beijing 100028, China
Jinju Chen: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Biqin Hu: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Xiaofei Sun: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China

Energies, 2024, vol. 17, issue 22, 1-18

Abstract: Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved Archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimization algorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 × 10 8 , which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.

Keywords: well placement; polymer flooding; Archimedes optimization algorithm; Halton sequence; offshore oil reservoirs; reservoir simulation (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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