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The traditional model of solid dissolution in porous media consists of three dissolution regimes (uniform, compact, wormhole)—or patterns—that are established depending on the relative dominance of reaction rate, flow, and diffusion. In this work, we investigate the evolution of pore structure using numerical simulations during acid injection on two models of increasing complexity. We investigate the boundaries between dissolution regimes and characterize the existence of a fourth dissolution regime called channeling, where initially fast flow pathways are preferentially widened by dissolution. Channeling occurs in cases where the distribution in pore throat size results in orders of magnitude differences in flow rate for different flow pathways. This focusing of dissolution along only dominant flow paths induces an immediate, large change in permeability with a comparatively small change in porosity, resulting in a porosity–permeability relationship unlike any that has been previously seen. This work suggests that the traditional conceptual model of dissolution regimes must be updated to incorporate the channeling regime for reliable forecasting of dissolution in applications like geothermal energy production and geologic carbon storage. [ABSTRACT FROM AUTHOR]

At the 2015 United Nations International Climate Change Conference in Paris (COP21), 197 national parties committed to limit global warming to well below 2°C. But current plans and pace of progress are still far from sufficient to achieve this objective. Here we review the role that geoscience and the subsurface could play in decarbonizing electricity production, industry, transport and heating to meet UK and international climate change targets, based on contributions to the 2019 Bryan Lovell meeting held at the Geological Society of London. Technologies discussed at the meeting involved decarbonization of electricity production via renewable sources of power generation, substitution of domestic heating using geothermal energy, use of carbon capture and storage (CCS), and more ambitious technologies such as bioenergy and carbon capture and storage (BECCS) that target negative emissions. It was noted also that growth in renewable energy supply will lead to increased demand for geological materials to sustain the electrification of the vehicle fleet and other low-carbon technologies. The overall conclusion reached at the 2019 Bryan Lovell meeting was that geoscience is critical to decarbonization, but that the geoscience community must influence decision-makers so that the value of the subsurface to decarbonization is understood. [ABSTRACT FROM AUTHOR]

Abstract: Multi-phase multi-component flow processes are fundamental to engineering applications in hydrocarbon and geothermal reservoirs but also to many classical geological processes. This review will highlight recent developments in the mathematical modelling and numerical simulations of the underlying physical processes from the pore- to the reservoir scale. Many modern approaches now rely on integrating numerical and analytical methods and incorporate results across the different length scales. This provides new insights into the fundamental properties of multi-phase multi-component flow and helps to mitigate some of the inherent difficulties in quantifying them in subsurface reservoirs. [Copyright &y& Elsevier]

An important issue in the numerical simulation of geothermal reservoirs is the problem of scales. Data are collected at a scale usually smaller than the one used to discretise the sedimentological units in the numerical model. For instance, thermal conductivities sampled from field scale cores have measurement support in the order of centimeters to meters, whereas numerical models for heat flow require conductivities representative of scales ranging between tens to hundreds of meters. We present a study aimed at demonstrating the upscaling of thermal conductivities. Based on the spatial characteristics of a large sample data set of thermal conductivities of permo-carboniferous sedimentary rocks, 10 different realizations of the system are randomly generated at a fine scale of resolution and are then upscaled to four different resolutions using diverse averaging procedures (based on arithmetic, geometric, or harmonic averaging) as well as renormalization. Results show that upscaling based on harmonic averaging of local values is superior in reproducing the original values while renormalization gives the poorest results. Generally it is demonstrated that the specific kind of upscaling has only a small impact on the resulting temperature distribution. Due to the diffusive character of heat conduction all results tend towards the arithmetic mean value associated with the data. [ABSTRACT FROM AUTHOR]

• Scarce data and modeler bias introduce uncertainty to geothermal reservoir models. • Parameter uncertainty affects prediction of geothermal reservoir performance. • Optimization of plant design has to consider the different geological scenarios. • Considering uncertainty requires separate simulations for each geological scenario. • Optimization under uncertainty reduces investment risk for geothermal projects. Geothermal energy has a high potential to contribute to a more sustainable energy system if the associated economic risks can be overcome in the design process. The development planning of deep geothermal reservoirs (over 1000 m depth) relies on computer models to forecast and then optimize system design. Optimization is easy where all the objective's (e.g. NPV) optimization parameters and, most importantly, the geology are considered as known, but this is almost always not the case. Where the complex engineering design (e.g. well placement) meets significant geological uncertainty every development option should be tested using an expensive simulation against the range of geological possibilities. The impracticality of simulating so many models results in a limited exploration of geological uncertainties and development options. Consequently, the risk of improper system design cannot be properly assessed. This paper presents an approach to understand the trade-offs in maximizing heat extraction while minimizing energy usage in re-injection for a new geothermal reservoir development while considering the uncertainty from 18 different geological models. Our approach is computationally feasible because we apply multi-objective particle swarm optimization (MOPSO), to an ensemble of response surface models, built using Gaussian process regression (GPR), for each and every geological scenario. MOPSO explores the trade-off surface for the competing objectives using the mean reservoir responses (covering the geological uncertainty). Our results highlight the impact of geological uncertainty on the optimal well placement and show the need to consider geological uncertainties adequately in optimization. The work demonstrates the shortcomings of using only one geological model of a geothermal reservoir and/or a single objective in optimization. We additionally demonstrate the practicalities of using response surface models in this way for geothermal systems. We anticipate that our work raises awareness for the scope of optimization of geothermal reservoir design under geological uncertainty. [ABSTRACT FROM AUTHOR]

A multi-physics framework for compositional and non-isothermal two-phase flow in a porous medium is presented which adapts locally the model complexity depending on the physical state. It is based on a sequential (IMPET) solution scheme. As a second adaptive strategy, the simulation grid can be refined locally. This may lead to a mesh with hanging nodes, which is treated by a multi-point flux approximation (MPFA) for improved flux representation. The two adaptivity concepts are employed to simulate a combined subsurface CO 2 injection and geothermal application in an existing reservoir, the Tensleep formation. [ABSTRACT FROM AUTHOR]

Deep geothermal systems have the potential to provide sustainable, reliable, carbon-free power for the UK. However, exploitation of the UK deep thermal resource is held back by knowledge gaps about permeability and fluid/heat flow within fractured hot rocks. The BGS-led GWatt project aims to fill these gaps via novel studies of rock stress, natural fracture networks, hot rock permeability, heat/fluid transport, and optimisation, in order to maximise reservoir utilisation whilst minimizing risks. The project aims to achieve a better understanding of fluid flow and heat transport in deep fracture-dominated systems that will enable the prediction of the economically-utilisable geothermal resource, the optimisation of reservoir development and the quantification of geological uncertainty and geological risks (such as induced seismicity).GWatt aims to tackle the above challenges, by combining innovative and detailed scientific understanding gained from an in-depth study of the Variscan granites of south-west England and their adjacent host rocks, with data obtained from two new boreholes being developed at the UK's deepest onshore geothermal project at United Downs in Cornwall. Our project plan includes: identifying indicators of deep fracture flow from shallower measurements; developing new 3D fracture/structural/tectonic models of the SW England granites and their immediately adjacent host rocks that reflect the complete post-Variscan tectonic evolution; developing a quantitative understanding of the heat resource and sustainability of the reservoir; providing an assessment of seismic potential due to fluid flow through the Porthtowan Fault Zone; constructing robust geological risk assessments based on well-established oil & gas uncertainty quantification and optimisation methods, with a view to reducing perceived risks; and applying the integrated results of site-specific research at United Downs to new geothermal exploration models of the other granites of SW England. We will describe the scope for this new project, together with initial findings. [ABSTRACT FROM AUTHOR]