TY - GEN
T1 - Multi-objective optimization of heat extraction from multilateral-well geothermal energy system
AU - Song, Guofeng
AU - Li, Gensheng
AU - Song, Xianzhi
AU - Xu, Fuqiang
AU - Shi, Yu
AU - Wang, Gaosheng
PY - 2020
Y1 - 2020
N2 - Operational parameter optimization is of great significance to improve overall heat extraction performance from hydrothermal or enhanced geothermal systems. Injection flowrate/temperature and production pressure are relatively easy to control to optimize the exploitation of geothermal resources during the planned reservoir lifetime. The net heat power and flow impedance are two contradictory production indexes for describing the exploitation effect. One indicates energy efficiency from reservoirs, the other represents the mining difficulty or artificial energy input. In this study, a multi-objective optimization procedure is proposed and applied to a synthetic multilateral-well system for 30 years. Firstly, a multilateral-well geothermal model coupled with thermal and hydraulic parameters is established. Then, a multiple regression method is employed to obtain the net heat power and flow impedance functions with injection/production parameters and physical properties of the reservoir. Finally, a multi-objective genetic algorithm is used to gain a Pareto solution set of injection and production parameters. A comparison with the base case indicates the superiority, high efficiency, and intelligence of multi-objective optimization.
AB - Operational parameter optimization is of great significance to improve overall heat extraction performance from hydrothermal or enhanced geothermal systems. Injection flowrate/temperature and production pressure are relatively easy to control to optimize the exploitation of geothermal resources during the planned reservoir lifetime. The net heat power and flow impedance are two contradictory production indexes for describing the exploitation effect. One indicates energy efficiency from reservoirs, the other represents the mining difficulty or artificial energy input. In this study, a multi-objective optimization procedure is proposed and applied to a synthetic multilateral-well system for 30 years. Firstly, a multilateral-well geothermal model coupled with thermal and hydraulic parameters is established. Then, a multiple regression method is employed to obtain the net heat power and flow impedance functions with injection/production parameters and physical properties of the reservoir. Finally, a multi-objective genetic algorithm is used to gain a Pareto solution set of injection and production parameters. A comparison with the base case indicates the superiority, high efficiency, and intelligence of multi-objective optimization.
KW - Flow impedance
KW - Genetic algorithm
KW - Geothermal system
KW - Multi-objective optimization
KW - Multilateral-well
KW - Operational parameters
UR - http://www.scopus.com/inward/record.url?scp=85103348447&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85103348447
T3 - Transactions - Geothermal Resources Council
SP - 965
EP - 978
BT - Geothermal Resources Council Virtual Annual Meeting and Expo, GRC 2020
PB - Geothermal Resources Council
T2 - Geothermal Resources Council Virtual Annual Meeting and Expo: Clean, Renewable and Always On, GRC 2020
Y2 - 19 October 2020 through 23 October 2020
ER -