A FORGE Datathon Case Study to Optimize Well Spacing and Flow Rate for Power Generation

Abstract

Enhanced geothermal systems (EGS) are potential sources of low-carbon and continuous clean power. However, optimizing a profitable EGS power plant is a non-trivial task because it requires making critical design decisions (e.g., well spacing, well count) when site data uncertainty remains high (e.g., reservoir and stress characteristics). An optimal design decision maximizes the economic value of a power plant while site data and its uncertainties provide a mandate that a large number of realizations (sampling space) must be considered to search for reliably profitable scenarios. This study optimizes the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT simulations from the PIVOT 2022 Datathon to simulate the whole geothermal development cycle from the initial well design to the end of production. This dataset is based on the Utah FORGE site and includes measured uncertainties. In all, the database includes 44,492 unique realizations, each with at least 30 years of production. For each realization, we computed power outputs from a combined binary and flash power plant and deducted parasitic pumping power to estimate NPV. Next, we used a binning-based optimization technique to search inputs and NPVs in bins. Bins were formed within the specified range of well spacing and flow rates. Finally, we estimated the range of well spacing and flow rates that provides the most profitable NPV with uncertainty.