The long-term thermal extraction from enhanced geothermal systems (EGSs) highly depends on the flow and transport characteristics in the underlying fracture networks connecting injection and production wells. Tracer testing is a powerful diagnostic tool for subsurface fracture characterization. However, interpreting the obtained tracer data for long-term thermal performance prediction is not an easy task because of the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical knowledge. We explore using a data assimilation approach, ensemble smoother with multiple data assimilation (ESMDA), to interpreting tracer data for long-term thermal performance prediction in EGS reservoirs. There are three major components in the proposed approach: 1) We use principal component analysis (PCA) to reduce the dimensionality of fracture models. 2) We use ESMDA to assimilate various tracer data (conservative and sorptive tracer) jointly and obtain a posterior ensemble of fracture models. 3) The posterior fracture models are used to perform thermal simulation and predict long-term thermal performance. We developed a field-scale EGS model to verify the capability of the proposed approach in fracture characterization and long-term thermal performance prediction.

Press the Back button in your browser, or search again.

Copyright 2021, Stanford Geothermal Program: Readers who download papers from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the original publisher.