Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools

Abstract

The study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the Foresudetic Monocline, Poland. Advanced modeling techniques were employed to analyze the studied formations’ heat, storage, and transport properties. The obtained results were then used to calculate the heat in place (HIP) and evaluate the recoverable heat (Hrec) for both water and CO2 as working fluids, considering a geothermal system lifetime of 50 years. The petrophysical parameters and Hrec were subsequently utilized in the generalized c-means (GFCM) clustering analysis, which helped to identify plays with the greatest geothermal potential within the studied formations. The central block emerged as the most promising area for the studied carbonate formation with Hrec values of ~1.12 and 0.26 MW when H2O and CO2 were used as working fluids, respectively. The central block has three wells that can be easily adapted for geothermal production. The area, however, may require permeability enhancement techniques to increase reservoir permeability. Two prospective zones were determined for the analyzed Rotliegend sandstone formation: one in the NW region and the other in the SE region. In the NW region, the estimated Hrec was 23.16 MW and 4.36 MW, while in the SE region, it was 19.76 MW and 3.51 MW, using H2O and CO2 as working fluids, respectively. Both areas have high porosity and permeability, providing good storage and transport properties for the working fluid, and abundant wells that can be configured for multiple injection-production systems. When comparing the efficiency of geothermal systems, the water-driven system in the Visean carbonate formation turned out to be over four times more efficient than the CO2-driven one. Furthermore, in the case of the Rotliegend sandstone formation, it was possible to access over five times more heat using water-driven system.

Keywords

• machine learning
• GFCM clustering
• geological modeling
• geothermal resource exploration