A probabilistic application of oil and gas data for exploration stage geothermal reservoir assessment in the Appalachian Basin
- Cornell University, Ithaca, NY (United States)
- Southern Methodist University, Dallas, TX (United States)
Geothermal energy is a renewable, widespread, baseload energy source that has the potential to supply a large portion of the world’s energy needs. High development risk, specifically high drilling costs combined with uncertainty in subsurface flow properties, have hampered growth of geothermal energy development. One option to reduce subsurface risk is to target locations where reservoir data are already available, including sedimentary basins where there has been extensive hydrocarbon production. Sedimentary basins are widespread and often have suitable geothermal gradients. This chapter presents a low-cost methodology that can be used at the pre-drilling exploration phase of a low-temperature geothermal project to predict the location of high-productivity and low-risk reservoirs, applied to a case study of the Appalachian Basin in the eastern United States. Because only averaged reservoir data were available for analysis, the technique uses a Monte Carlo simulation of the Reservoir Flow Capacity (RFC) and Reservoir Productivity Index (RPI) for over 1800 individual reservoirs, using liquid water or supercritical carbon dioxide (sCO2) as the reservoir fluid. RFC is used as an initial screening tool to identify high-priority geologic formations and reservoirs. Here our results indicate that 99% of the reservoirs in the basin are of insufficient quality for geothermal heat production with water as the assumed fluid and without using stimulation technologies associated with Enhanced Geothermal Systems (EGS). RPI is also used as an alternative metric to identify high-productivity reservoirs. When comparing the RPI results to natural gas production volumes from several selected reservoirs, the predicted RPI proves to be a good estimate of flow potential for porous reservoirs, but overestimates flow by a factor of three for fractured or vuggy reservoirs. A greater uncertainty ought to be utilized when applying the RPI equation to non-porous media, therefore we recommend utilizing the RPI metric when higher resolution data are available for analysis. This methodology can be utilized in other basins that have experienced hydrocarbon exploration and production, to highlight low-risk reservoirs or geologic formations for further geothermal development.
- Research Organization:
- Cornell Univ., Ithaca, NY (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Science Foundation (NSF)
- Grant/Contract Number:
- EE0006726
- OSTI ID:
- 1538317
- Alternate ID(s):
- OSTI ID: 1549500
- Journal Information:
- Geothermics, Vol. 71, Issue C; ISSN 0375-6505
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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