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Geostatistical Modeling of Subsurface Uncertainty in Deep Sedimentary Geothermal Reservoirs
Nathaniel NIMO YEBOAH, Emmanuel AGYEI, Emmanuel GYIMAH, William AMPOMAH, Godsway AKPABLI, Benedicta VIDZRO
[New Mexico Institute of Mining and Technology, USA]
Geothermal energy serves as a sustainable and low-carbon alternative that supports the adoption of a diversified and sustainable energy mix. The deep sedimentary basins of North Dakota, particularly within the Mississippian and Devonian formations, possess substantial geothermal potential, making them promising targets for geothermal energy development. Efficient utilization of these geothermal resources requires a robust understanding of the inherent subsurface uncertainties and spatial heterogeneities that influence reservoir performance. This study integrates geostatistical modeling with geothermal reservoir engineering principles to evaluate uncertainty parameters within the geothermal system of the Three Forks formation (Williston basin). Through probabilistic analysis and Monte-Carlo simulation, we quantify the variability and ambiguity of critical subsurface properties within the target formation to improve the reliability of resource assessment and project feasibility within the basin. Field data from the Mississippian and Devonian intervals were analyzed to characterize uncertainties in key parameters that control geothermal energy potential, thereby reducing exploration risks and promoting sustainable development. Uncertainty quantification and probabilistic resource assessment are crucial for informed decision-making and efficient reservoir management. In the case of the Three Forks formation, the arithmetic mean estimates for total geothermal resources, aquifer geothermal resources, and producible geothermal resources were found to be approximately 3.3 × 10¹⁸ J, 1.08× 10¹⁷ J, and 1.02 × 107 J, respectively. These insights provide a quantitative basis for optimizing geothermal energy extraction from the Mississippian–Devonian formations in North Dakota.
Topic: Modeling