Abstract
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.
Highlights
Introduction iationsGeothermal energy technology is continuing to evolve to provide heat and electricity for both industrial and residential applications [1,2,3]
For the entire training In set.our. This relative difference can potentially translate into a substantial work, we have proposed and evaluated a computational approach that are measured in millions energy gain, considering that the absolute for EWe show bines geologic reservoir modelingvalues with machine learning (ML)
We demonstrate how geologic reservoir modeling can be used in combination with machine learning
Summary
Geothermal energy technology is continuing to evolve to provide heat and electricity for both industrial and residential applications [1,2,3]. The need to provide low-carbon electricity and heat has increased interest in geothermal energy as a significant contributor to the low-carbon energy mix [4]. Geothermal energy is projected to contribute around. 2–3% of global electricity generation by 2050 [5] and produce approximately 5% of global heat load [6]. Significant environmental, economic, and social challenges remain [7,8]. Application of machine learning (ML) and artificial intelligence (AI) methods has the potential to improve the economic competitiveness of geothermal energy while reducing the environmental impact. ML has already been successfully applied to the identification of lithologies [9], extraction of hidden yet important features in hydrological data [10], Licensee MDPI, Basel, Switzerland
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