Digital twin preparation and hydro-mechanical barite deposition modelling in fractured rocks
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Abstract
Global warming is a central challenge of this century. Thus, reducing anthropogenic greenhouse gas emissions by transitioning to renewable energy sources is critical. Geothermal plants have a considerable potential as a renewable energy source since they can constantly generate heat and energy. However, disequilibrium in circulating fluids in the (fractured) reservoir can disturbed this constant heat generation by e.g., mineral precipitation. Mineral scaling can reduce the efficiency of plants; therefore, it must be minimized to guarantee a sustainable operation.
This work aims to contribute to a deeper understanding of mineral scaling in fractured reservoirs and is part of the joint project called ReSalt. The digital rock physics (DRP) workflow was applied to model hydro-mechanical particle deposition in fractured sandstone cores, which pose as analogue rocks to reservoirs in Germany. The analysis focuses on barite particles since they are typical scaling minerals in these reservoirs. Two main problems were studied in this study. The first topic is dedicated to the extraction of reliable digital twins (DTs) of rocks. This work focuses on two crucial steps: the segmentation and the correction of the partial volume effect (PVE). Some Novel machine learning approaches were applied and compared to the conventional segmentation methods. The results highlighted the potential of the machine learning segmentation methods. Based on the segmentation results, a novel workflow was developed to correct the PVE in fractures. The fractures were reconstructed at a higher resolution and validated by laboratory data (i.e., permeability, tracer curves, mean aperture). It was demonstrated that the fracture reconstruction resulted in plausible DTs. Recommendations for future studies were made to apply and advance the presented framework.
The second topic of this study focuses on the influence of various parameters (temperature, flow rate, adhesion forces, particle size, and amount) on the hydro-mechanical barite particle deposition. A numerical sensitivity analysis was conducted with two validated reconstructed fracture models. Insights on optimal conditions for the least particle deposition were given. In the conclusion, assumptions in the analysis are discussed, and an outlook for future studies is presented.
This thesis attempts to expand the concept of digital rock analysis for fractured rocks. Novel methods were applied, and their benefits and current limitations were discussed. Since the PVE can heavily affect DTs of rock fractures, the developed reconstruction workflow might contribute to creating reliable DTs in future studies. The sensitivity analysis is the first step in understanding the importance of various parameters on the process of hydro-mechanical particle deposition in fractures. Insights from this dissertation can contribute to the design of more complex numerical and experimental studies.