Closed-loop geothermal systems (CLGSs) rely on circulation of a heat transfer fluid in a closed-loop design without penetrating the reservoir to extract subsurface heat and bring it to the surface. We developed and applied numerical models to study u-shaped and coaxial CLGSs in hot-dry-rock over a more comprehensive parameter space than has been studied before, including water and supercritical CO2 (sCO2) as working fluids. An economic analysis of each realization was performed to evaluate the levelized cost of heat (LCOH) for direct heating application and levelized cost of electricity (LCOE) for electrical power generation. The results of the parameter study, composed of 2.5 million simulations, combined with a plant and economic model comprise the backbone of a publicly accessible web application that can be used to query, analyze, and plot outlet states, thermal and mechanical power output, and LCOH/LCOE, thereby facilitating feasibility studies led by potential developers, geothermal scientists, or the general public (https://gdr.openei.org/submissions/1473). Our results indicate competitive LCOH can be achieved; however, competitive LCOE cannot be achieved without significant reductions in drilling costs. We also present a site-based case study for multi-lateral systems and discuss how our comprehensive single-lateral analyses can be applied to approximate multi-lateral CLGSs. Looking beyond hot-dry-rock, we detail CLGS studies in permeable wet rock, albeit for a more limited parameter space, indicating that reservoir permeability of greater than 250 mD is necessary to significantly improve CLGS power production, and that reservoir temperatures greater than 200 °C, achieved by going to greater depths (∼3–4 km), may significantly enhance power production.
Accurate modeling of subsurface flow and transport processes is vital as the prevalence of subsurface activities such as carbon sequestration, geothermal recovery, and nuclear waste disposal increases. Computational modeling of these problems leverages poroelasticity theory, which describes coupled fluid flow and mechanical deformation. Although fully coupled monolithic schemes are accurate for coupled problems, they can demand significant computational resources for large problems. In this work, a fixed stress scheme is implemented into the Sandia Sierra Multiphysics toolkit. Two implementation methods, along with the fully coupled method, are verified with one-dimensional (1D) Terzaghi, 2D Mandel, and 3D Cryer sphere benchmark problems. The impact of a range of material parameters and convergence tolerances on numerical accuracy and efficiency was evaluated. Overall the fixed stress schemes achieved acceptable numerical accuracy and efficiency compared to the fully coupled scheme. However, the accuracy of the fixed stress scheme tends to decrease with low permeable cases, requiring the finer tolerance to achieve a desired numerical accuracy. For the fully coupled scheme, high numerical accuracy was observed in most of cases except a low permeability case where an order of magnitude finer tolerance was required for accurate results. Finally, a two-layer Terzaghi problem and an injection–production well system were used to demonstrate the applicability of findings from the benchmark problems for more realistic conditions over a range of permeability. Simulation results suggest that the fixed stress scheme provides accurate solutions for all cases considered with the proper adjustment of the tolerance. This work clearly demonstrates the robustness of the fixed stress scheme for coupled poroelastic problems, while a cautious selection of numerical tolerance may be required under certain conditions with low permeable materials.
Prediction of flow, transport, and deformation in fractured and porous media is critical to improving our scientific understanding of coupled thermal-hydrological-mechanical processes related to subsurface energy storage and recovery, nonproliferation, and nuclear waste storage. Especially, earth rock response to changes in pressure and stress has remained a critically challenging task. In this work, we advance computational capabilities for coupled processes in fractured and porous media using Sandia Sierra Multiphysics software through verification and validation problems such as poro-elasticity, elasto-plasticity and thermo-poroelasticity. We apply Sierra software for geologic carbon storage, fluid injection/extraction, and enhanced geothermal systems. We also significantly improve machine learning approaches through latent space and self-supervised learning. Additionally, we develop new experimental technique for evaluating dynamics of compacted soils at an intermediate scale. Overall, this project will enable us to systematically measure and control the earth system response to changes in stress and pressure due to subsurface energy activities.
Greater utilization of subsurface reservoirs perturbs in-situ chemical-mechanical conditions with wide ranging consequences from decreased performance to project failure. Understanding the chemical precursors to rock deformation is critical to reducing the risks of these activities. To address this need, we investigated the coupled flow-dissolution- precipitation-adsorption reactions involving calcite and environmentally-relevant solid phases. Experimentally, we quantified (1) stable isotope fractionation processes for strontium during calcite nucleation and growth, and during reactive fluid flow; (2) consolidation behavior of calcite assemblages in the common brines. Numerically, we quantified water weakening of calcite using molecular dynamics simulations; and quantified the impact of calcite dissolution rate on macroscopic fracturing using finite element models. With microfluidic experiments and modeling, we show the effect of local flow fields on the dissolution kinetics of calcite. Taken together across a wide range of scales and methods, our studies allow us to separate the effects of reaction, flow, and transport, on calcite fracturing and the evolution of strontium isotopic signatures in the reactive fluids.
Recent advances in drilling technology, especially horizontal drilling, have prompted a renewed interest in the use of closed loop geothermal energy extraction systems. Deeply placed closed loops in hot wet or dry rock reservoirs offer the potential to exploit the vast thermal energy in the subsurface. To better understand the potential and limitations for recovering thermal and mechanical energy from closed-loop geothermal systems (CLGS), a collaborative study is underway to investigate an array of system configurations, working fluids, geothermal reservoir characteristics, operational periods, and heat transfer enhancements (Parisi et al., 2021; White et al., 2021). This paper presents numerical results for the heat exchange between a closed loop system (single U-tube) circulating water as the working fluid in a hot rock reservoir. The characteristics of the reservoir are based on the Frontier Observatory for Research in Geothermal Energy (FORGE) site, near Milford Utah. To determine optimal system configurations, a mechanical (electrical) objective function is defined for a bounded optimization study over a specified design space. The objective function includes a surface plant thermal to mechanical energy conversion factor, pump work, and an energy drilling capital cost. To complement the optimization results, detailed parametric studies are also performed. The numerical model is built using the Sandia National Laboratories (SNL) massively parallel Sierra computational framework, while the optimization and parametric studies are driven using the SNL Dakota software package. Together, the optimization and parametric studies presented in this paper will help assess the impact of CLGS parameters (e.g., flow rate, tubing length and diameter, insulation length, etc.) on CLGS performance and optimal energy recovery.
Predicting performance of parts produced using laser-metal processing remains an out- standing challenge. While many computational models exist, they are generally too computationally expensive to simulate the build of an engineering-scale part. This work develops a reduced order thermal model of a laser-metal system using analytical Green's function solutions to the linear heat equation, representing a step towards achieving a full part performance prediction in an "overnight" time frame. The developed model is able to calculate a thermal history for an example problem 72 times faster than a traditional FEM method. The model parameters are calibrated using a non-linear solution and microstructures and residual stresses calculated and compared to a non-linear case. The calibrated model shows promising agreement with a non-linear solution.