The efficient operation and management of a geothermal project can be largely affected by geological, physical, operational and economic uncertainties. Systematic uncertainty quantification (UQ) involving these parameters helps to determine the probability of the focused outputs, e.g., energy production, Net Present Value (NPV), etc. However, how to efficiently assess the specific impacts of different uncertain parameters on the outputs of a geothermal project is still not clear. In this study, we performed a comprehensive UQ to a low-enthalpy geothermal reservoir using the GPU implementation of the Delft Advanced Research Terra Simulator (DARTS) framework with stochastic Monte Carlo samplings of uncertain parameters. With processing the simulation results, large uncertainties have been found in the production temperature, pressure drop, produced energy and NPV. It is also clear from the analysis that salinity influences the producing energy and NPV via changing the amount of energy carried in the fluid. Our work shows that the uncertainty in NPV is much larger than that in produced energy, as more uncertain factors were encompassed in NPV evaluation. An attempt to substitute original 3D models with upscaled 2D models in UQ demonstrates significant differences in the stochastic response of these two approaches in representation of realistic heterogeneity. The GPU version of DARTS significantly improved the simulation performance, which guarantees the full set (10,000 times) UQ with a large model (circa 3.2 million cells) finished within a day. With this study, the importance of UQ to geothermal field development is comprehensively addressed. This work provides a framework for assessing the impacts of uncertain parameters on the concerning system output of a geothermal project and will facilitate analyses with similar procedures.

This paper introduces an analytical model analyzing the effect of groundwater flow on heat transfer in an infinite conductive-convective porous domain representing shallow geothermal systems with arbitrarily configured cylindrical heat sources. The model is formulated based on the moving source concept and solved based on the spectral analysis method and the superposition principle. Compared to models based on the Green's function and the Laplace transform, the proposed spectral model has a simpler formulation, computationally efficient and easy to implement in computer codes. It can handle random time-dependent thermal loads and any arbitrarily configured grid distribution. The verification and numerical examples demonstrate the computational capabilities of the model, and show how the groundwater flow can play an important role in the thermal interaction between heat sources. They also feature how to make use of the direction of groundwater flow to avoid undesirable thermal interaction between neighboring installations, rapid depletion of energy sources and unfair mining of geothermal energy.

Geothermal energy is gaining momentum as a renewable energy source. Reservoir simulation studies are often used to understand the underlying physics interactions and support decision making. Uncertainty related to geothermal systems can be substantial for subsurface and operational parameters and their interaction with regards to the output in terms of lifetime, energy and economic output. Specifically, for geothermal systems with the fault acting as the main fluid pathway the relevant field development uncertainties have not been comprehensively addressed. In this study we show how the produced energy, system lifetime and NPV are affected considering a range of subsurface and operational parameters as uncertainty sources utilizing an ensemble of 16,200 3D Hydraulic-Thermal (HT) reservoir simulations, conceptually based on the Rittershoffen field. A well configuration with oblique angles with respect to the main permeability anisotropy axes results in higher system lifetime, generated energy and NPV. A well spacing of 600 m consistently yields a higher economic efficiency (€/MWh) under all uncertainty parameters considered. More robust development options could be utilized in the absence of fault permeability characterization to ensure improved output prediction under uncertainty. Studies based on the methodology presented can improve investment efficiency for field development under subsurface and operational uncertainty.

A realistic deep low-enthalpy geothermal reservoir based on real data with high detail and complicated sedimentary structure is utilized to perform sensitivity analyses of the geological features influencing reservoir properties. We perform simulations using the Delft Advanced Research Terra Simulator (DARTS). Compelling numerical performance of DARTS makes it suitable for handling a large ensemble of models including efficient sensitivity and uncertainty analyses. The major finding is that shale facies, generally ignored in hydrocarbon reservoir simulations, can significantly extend the predictive lifetime of geothermal reservoirs exploited by deep well doublets. It is important to accurately account for the shale facies in the simulation, though with an additional computational overhead. The overburden layers can improve doublet performance, but the impact depends on reservoir heterogeneity. In addition, heterogeneity will also divert the flow path with even a minor shift in the well placement. The discharge rate, an essential parameter of geothermal operation strategy, inversely corresponds to the doublet lifetime but positively correlates with the energy production for studied parameter ranges. Low sensitivity of doublet lifetime to vertical-horizontal permeability ratio and permeability-porosity correlation is observed. All these systematic findings for a realistic geothermal field with characterization at unprecedented level of detail can help to provide a general guideline for forward simulation and farther improve the profitability of geothermal energy production in realistic deep geothermal reservoirs through computer-assisted modeling and optimization.

This paper introduces a spectral model for a moving cylindrical heat source in an infinite conductive-convective domain. This physical process occurs in many engineering and technological applications including heat conduction-convection in ground source heat pump systems, where the borehole heat exchangers likely go through layers with groundwater flow. The governing heat equation is solved for Dirichlet and Neumann boundary conditions using the fast Fourier transform for the time domain, and the Fourier series for the spatial domain. A closed form solution based on the modified Bessel functions is obtained for the Dirichlet boundary condition and an integral form for the Neumann boundary condition. Limiting cases of the moving cylindrical heat source to represent a moving line heat source are also derived. Compared to solutions based on the Green's function and the Laplace transform, the spectral model has a simpler form, applicable to complicated time-variant input signals, valid for a wide range of physical parameters and easy to implement in computer codes. The model is verified against the existing infinite line heat source model and a finite element model.

The efficient development of a geothermal field can be largely affected by the inherent geological and physical uncertainties. Besides, the uncertain operational and economic parameters can also impact the profit of a project. Systematic uncertainty quantification involving these parameters helps to determine the probability of concerning outputs. In this study, a low-enthalpy geothermal reservoir with strong heterogeneity, located in the West Netherlands Basin, is selected as the research area. Detailed geological model is constructed based on various static data including seismic and log interpretation. However, significant uncertainties still exist in definition of the model parameters, mainly reservoir permeability and porosity. Besides, the fluid properties have not been sampled in this field and can vary in the range between brackish to highly saline water. Also, the heat price and operational investment fluctuate with time and add up to uncertainty. Taking all interested parameters into consideration, the Monte Carlo method is utilized to select specific input data set. The forward simulations are powered by the GPU version of Delft Advance Research Terra Simulator (DARTS), which provides efficient simulation capabilities for geothermal applications. Through this investigation, a wide range of production temperature has been observed due to the uncertainty of the input parameters.