Hydraulic stimulation and fluid circulation experiments in underground laboratories: Stepping up the scale towards engineered geothermal systems

2020 ◽  
Vol 24 ◽  
pp. 100175 ◽  
Author(s):  
Valentin S. Gischig ◽  
Domenico Giardini ◽  
Florian Amann ◽  
Marian Hertrich ◽  
Hannes Krietsch ◽  
...  
Keyword(s):  
Geothermal Systems ◽  
Fluid Circulation ◽  
Hydraulic Stimulation ◽  
Engineered Geothermal Systems

Modeling fluid flow through complex fracture network in geological media by using hierarchical hydraulic properties

2020 ◽  
Author(s):  
Kyung Won Chang ◽  
Gungor Beskardes ◽  
Chester Weiss
Keyword(s):  
Fluid Flow ◽  
Surrounding Medium ◽  
Computational Cost ◽  
Energy Resource ◽  
Fracture Network ◽  
Fractured Media ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Hydraulic Stimulation ◽  
Complex Fracture

<p>Hydraulic stimulation is the process of initiating fractures in a target reservoir for subsurface energy resource management with applications in unconventional oil/gas and enhanced geothermal systems. The fracture characteristics (i.e., number, size and orientation with respect to the wellbore) determines the modified permeability field of the host rock and thus, numerical simulations of flow in fractured media are essential for estimating the anticipated change in reservoir productivity. However, numerical modeling of fluid flow in highly fractured media is challenging due to the explosive computational cost imposed by the explicit discretization of fractures at multiple length scales. A common strategy for mitigating this extreme cost is to crudely simplify the geometry of fracture network, thereby neglecting the important contributions made by all elements of the complex fracture system.</p><p>The proposed “Hierarchical Finite Element Method” (Hi-FEM; Weiss, Geophysics, 2017) reduces the comparatively insignificant dimensions of planar- and curvilinear-like features by translating them into integrated hydraulic conductivities, thus enabling cost-effective simulations with requisite solutions at material discontinuities without defining ad-hoc, heuristic, or empirically-estimated boundary conditions between fractures and the surrounding formation. By representing geometrical and geostatistical features of a given fracture network through the Hi-FEM computational framework, geometrically- and geomechanically-dependent fluid flow properly can now be modeled economically both within fractures as well as the surrounding medium, with a natural “physics-informed” coupling between the two.</p><p>SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.</p>

Evaluation of Engineered Geothermal Systems as a Heat Source for Oil Sands Production in Northern Alberta

2013 ◽  
Vol 23 (2) ◽  
pp. 247-265 ◽  
Author(s):  
V. Pathak ◽  
T. Babadagli ◽  
J. A. Majorowicz ◽  
M. J. Unsworth
Keyword(s):  
Heat Source ◽  
Oil Sands ◽  
Geothermal Systems ◽  
Northern Alberta ◽  
Engineered Geothermal Systems

scholarly journals Novel chemical stimulation for geothermal reservoirs by chelating agent driven selective mineral dissolution in fractured rocks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Noriaki Watanabe ◽  
Kaori Takahashi ◽  
Ryota Takahashi ◽  
Kengo Nakamura ◽  
Yusuke Kumano ◽  
...  
Keyword(s):  
Fractured Rocks ◽  
Chelating Agent ◽  
Chemical Stimulation ◽  
Mineral Dissolution ◽  
Moderate Intensity ◽  
Geothermal Systems ◽  
Confining Stress ◽  
Traditional Methods ◽  
Hydraulic Stimulation ◽  
Permeability Enhancement

AbstractImproving geothermal systems through hydraulic stimulation to create highly permeable fractured rocks can induce seismicity. Therefore, the technique must be applied at a moderate intensity; this has led to concerns of insufficient permeability enhancement. Adding chemical stimulation can mitigate these issues, but traditional methods using strong mineral acids have challenges in terms of achieving mineral dissolution over long distances and highly variable fluid chemistry. Here, we demonstrate a novel chemical stimulation method for improving the permeability of rock fractures using a chelating agent that substantially enhances the dissolution rate of specific minerals to create voids that are sustained under crustal stress without the challenges associated with the traditional methods. Applying this agent to fractured granite samples under confining stress at 200 °C in conjunction with 20 wt% aqueous solutions of sodium salts of environmentally friendly chelating agents (N-(2-hydroxyethyl)ethylenediamine-N, N′, N′-triacetic acid and N, N-bis(carboxymethyl)-l-glutamic acid) at pH 4 was assessed. A significant permeability enhancement of up to approximately sixfold was observed within 2 h, primarily due to the formation of voids based on the selective dissolution of biotite. These results demonstrate a new approach for chemical stimulation.

scholarly journals Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network

2020 ◽  
Vol 205 ◽  
pp. 03007
Author(s):  
Yejin Kim ◽  
Seong Jun Ha ◽  
Tae sup Yun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Frequency Noise ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Hydraulic Stimulation ◽  
Roughness Analysis

Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method.

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