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.@en

The Los Humeros geothermal system is steam dominated and currently under exploration with 65 wells (23 producing). Having temperatures above 380 °C, the system is characterized as a super hot geothermal system (SHGS). The development of such systems is still challenging due to the high temperatures and aggressive reservoir fluids which lead to corrosion and scaling problems. The geothermal system in Acoculco (Puebla, Mexico; so far only explored via two exploration wells) is characterized by temperatures of approximately 300 °C at a depth of about 2 km. In both wells no geothermal fluids were found, even though a well-developed fracture network exists. Therefore, it is planned to develop an enhanced geothermal system (EGS).

For better reservoir understanding and prospective modeling, extensive geological, geochemical, geophysical and technical investigations are performed within the scope of the GEMex project. Outcrop analogue studies have been carried out in order to identify the main fracture pattern, geometry and distribution of geological units in the area and to characterize all key units from the basement to the cap rock regarding petro- and thermo-physical rock properties and mineralogy. Ongoing investigations aim to identify geological and structural heterogeneities on different scales to enable a more reliable prediction of reservoir properties. Beside geological investigations, physical properties of the reservoir fluids are determined to improve the understanding of the hydrochemical processes in the reservoir and the fluid-rock interactions, which affect the reservoir rock properties.

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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.

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