TY - JOUR
T1 - Numerical simulation and optimization of injection rates and wells placement for carbon dioxide enhanced gas recovery using a genetic algorithm
AU - Liu, Shuyang
AU - Agarwal, Ramesh
AU - Sun, Baojiang
AU - Wang, Bin
AU - Li, Hangyu
AU - Xu, Jianchun
AU - Fu, Guangming
N1 - Funding Information:
This paper has been financially supported by the National Natural Science Foundation of China ( 51906256 and U1762216 ), Program for Changjiang Scholars and Innovative Research Team in University of Ministry of China ( IRT_14R58 ) and Qingdao Postdoctoral Applied Research Project ( 2019238 ), which are gratefully acknowledged.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1/20
Y1 - 2021/1/20
N2 - The aim of CO2 enhanced gas recovery (CO2-EGR) is to extract more natural gas from depleted gas reservoirs and simultaneously sequestrate large amount of CO2. To achieve these dual objectives, the optimization of CO2 injection strategy in CO2-EGR is proposed to achieve maximum benefit. Thus, the focus of this work is to study CO2-EGR by numerical simulation and optimization. For this purpose, a 3D reservoir model with ‘five-spot’ well pattern (an injection well and four production wells) is established for simulations using the multiphase simulator TOUGH2, and the main focus is on optimization of CO2 injection rate and well placements by coupling the genetic algorithm (GA) with TOUGH2. Simulations are performed to determine the optimal injection rate for both two types of injection wells placements, one vertical injection well with number of perforations at various depth and orthogonal horizontal wells on top of the reservoir. The results show that multipoint perforations in the vertical injection well and the horizontal injection wells with appropriate length show improvement in natural gas recovery and CO2 storage compared to the injection location at the reservoir bottom. More importantly, the optimized injection rate determined by GA-TOUGH2 can substantially improve the natural gas recovery factor for both vertical and horizontal well injection. After computing a number of optimized cases, it's shown that the case employing the horizontal injection wells with optimized injection rate of 0.0778 kg/s achieves the maximum recovery of 67.21% and maximum CO2 storage efficiency of 69.54%, equal to 23.88 × 106 kg of natural gas exploited from the depleted reservoir and 74.52 × 106 kg of CO2 stored in pore volume of 1.81 × 106 m3 of the reservoir. This case also has minimum injection pressure jump of 0.08 MPa thereby reducing the risk of formation fracture and CO2 leakage. However, by conducting a simple cost/benefit analysis, it is determined that this optimally performing case is not the best economically because of higher drilling cost of horizontal well compared to a vertical well. It is found that a vertical well with two suitably placed perforations with optimized injection rate of 0.1025 kg/s is economically superior since the previously used production wells can be reused for CO2 injection, which can therefore reduce both the equipment and the drilling costs. It is hoped that the findings of this work should provide some insights into the optimization of CO2-EGR with maximum economic benefit for implementation in industrial practice.
AB - The aim of CO2 enhanced gas recovery (CO2-EGR) is to extract more natural gas from depleted gas reservoirs and simultaneously sequestrate large amount of CO2. To achieve these dual objectives, the optimization of CO2 injection strategy in CO2-EGR is proposed to achieve maximum benefit. Thus, the focus of this work is to study CO2-EGR by numerical simulation and optimization. For this purpose, a 3D reservoir model with ‘five-spot’ well pattern (an injection well and four production wells) is established for simulations using the multiphase simulator TOUGH2, and the main focus is on optimization of CO2 injection rate and well placements by coupling the genetic algorithm (GA) with TOUGH2. Simulations are performed to determine the optimal injection rate for both two types of injection wells placements, one vertical injection well with number of perforations at various depth and orthogonal horizontal wells on top of the reservoir. The results show that multipoint perforations in the vertical injection well and the horizontal injection wells with appropriate length show improvement in natural gas recovery and CO2 storage compared to the injection location at the reservoir bottom. More importantly, the optimized injection rate determined by GA-TOUGH2 can substantially improve the natural gas recovery factor for both vertical and horizontal well injection. After computing a number of optimized cases, it's shown that the case employing the horizontal injection wells with optimized injection rate of 0.0778 kg/s achieves the maximum recovery of 67.21% and maximum CO2 storage efficiency of 69.54%, equal to 23.88 × 106 kg of natural gas exploited from the depleted reservoir and 74.52 × 106 kg of CO2 stored in pore volume of 1.81 × 106 m3 of the reservoir. This case also has minimum injection pressure jump of 0.08 MPa thereby reducing the risk of formation fracture and CO2 leakage. However, by conducting a simple cost/benefit analysis, it is determined that this optimally performing case is not the best economically because of higher drilling cost of horizontal well compared to a vertical well. It is found that a vertical well with two suitably placed perforations with optimized injection rate of 0.1025 kg/s is economically superior since the previously used production wells can be reused for CO2 injection, which can therefore reduce both the equipment and the drilling costs. It is hoped that the findings of this work should provide some insights into the optimization of CO2-EGR with maximum economic benefit for implementation in industrial practice.
KW - CO-Enhanced gas recovery
KW - Genetic algorithm
KW - Horizontal well
KW - Injection rate optimization
KW - Optimization
KW - Vertical well with perforations
UR - http://www.scopus.com/inward/record.url?scp=85092064679&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.124512
DO - 10.1016/j.jclepro.2020.124512
M3 - Article
AN - SCOPUS:85092064679
SN - 0959-6526
VL - 280
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 124512
ER -