Multi-Objective Optimization of Geothermal Well Clusters in Hydrothermal Systems

Hydrothermal geothermal resources are the most widely utilized form of geothermal energy, with hydrothermal heating systems typically developed as well clusters for urban heating. The performance of these systems is significantly influenced by geothermal well patterns and operational parameters. However, there is a lack of optimization in both well pattern design and operation scheme, leading to system instability and low heat extraction efficiency. In this paper, a numerical model of flow and heat transfer is established by considering reservoir heterogeneity in the field. Nine geothermal well patterns are developed for comprehensive pattern evaluation. Production temperature, total heat extraction, and injection-production water level difference were used to select the optimal well pattern. The impact of geological and engineering parameters on the performance of the optimal well pattern was thoroughly analyzed, and a database was established. Subsequently, maximizing production temperature, maximizing total heat extraction, and minimizing the injection-production water level difference were set as the optimization objectives, with permeability, well spacing, and injection flow rate serving as decision variables. Multi-objective optimization based on the NSGA-III algorithm and multi-attribute decision analysis were employed to obtain optimal schemes of the selected well pattern. The results demonstrate that the selected well network outperforms others in terms of heat extraction and operational stability. Compared to the basic scheme, the optimized configuration increased production temperature by 11.12% and reduced the injection-production water level difference by 14.04%. These studies are of significant importance for the efficient development of hydrothermal geothermal systems.