Multi-Objective Optimization of Heat Extraction From Multilateral-Well Geothermal System

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.