Closed-loop geothermal systems: Modeling and predictions

TL;DR

This paper introduces a reduced-order modeling framework for closed-loop geothermal systems that accurately predicts temperature fields and system performance, addressing a key knowledge gap for efficient design and operation.

Contribution

The paper develops a fast, accurate reduced-order model that incorporates jump conditions to predict temperature fields in closed-loop geothermal systems.

Findings

The ROM provides quick predictions of outlet temperature and thermal draw-down.

The framework demonstrates solution uniqueness and numerical accuracy.

It enables performance assessment and optimization of geothermal systems.

Abstract

Geothermal energy is a sustainable baseload source recognized for its ability to provide clean energy on a large scale. Advanced Geothermal Systems (AGS) -- offer promising prototypes -- employ a closed-loop vascular layout that runs deep beneath the Earth's surface. A working fluid (e.g., water or supercritical carbon dioxide (sCO2)) circulates through the vasculature, entering the subsurface at the inlet and exiting at the outlet with an elevated temperature. For designing and performing cost-benefit analysis before deploying large-scale projects and maintaining efficiency while enabling real-time monitoring during the operational phase, modeling offers cost-effective solutions; often, it is the only available option for performance assessment. A knowledge gap exists due to the lack of a fast predictive modeling framework that considers the vascular intricacies, particularly the jumps in the solution fields across the channel. Noting that the channel diameter is considerably smaller in scale compared to the surrounding geological domain, we develop a reduced-order modeling (ROM) framework for closed-loop geothermal systems. This ROM incorporates the jump conditions and provides a quick and accurate prediction of the temperature field, including the outlet temperature, which directly correlates with the power production capacity and thermal draw-down. We demonstrate the predictive capabilities of the framework by establishing the uniqueness of the solutions and reporting representative numerical solutions. The modeling framework and the predictions reported in this paper benefit the closed-loop geothermal community, enabling them to determine the system's performance and optimal capacity.