Characterization of Geothermal Reservoir Structures Based on a Deep Generative Neural Network with Local Edge Pattern Learning

Accurate three-dimensional (3D) characterization of geothermal reservoirs is crucial for resource assessment and development, yet it remains a significant challenge due to sparse direct observations and the complex, heterogeneous nature of subsurface geology. While deep learning has emerged as a powerful tool for subsurface modeling, existing approaches often struggle to preserve critical fine-scale structural details and adaptively focus on geologically informative regions, limiting their effectiveness for geothermal applications. To address these limitations, we propose Gen-LEP, a novel deep generative neural network specifically designed for geothermal reservoir characterization. The proposed framework integrates a key component of local edge pattern (LEP) learning module to enhance the preservation of lithological boundaries and structural discontinuities. The LEP learning module is embedded within a conditional generative framework to effectively learn the nonlinear relationships between sparse conditioning data and complex 3D reservoir structures. We evaluate our method on a geothermal reservoir modeling dataset. Experimental results demonstrate that Gen-LEP can achieve accurate reconstruction and preserve complex geological boundaries. Gen-LEP can provide an effective deep learning framework that improves the fidelity of geothermal reservoir reconstructions by explicitly addressing the specific spatial characteristics of subsurface geological systems.