Publications

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Anisotropy in the mechanical properties of rock is often attributed to bedding and mineral texture. Here, we use 3D printed synthetic rock to show that, in addition to bedding layers, mineral fabric orientation governs sample strength, surface roughness and fracture path under mixed mode I and II three point bending tests (3PB). Arrester (horizontal layering) and short traverse (vertical layering) samples were printed with different notch locations to compare pure mode I induced fractures to mixed mode I and II fracturing. For a given sample type, the location of the notch affected the intensity of mode II loading, and thus affected the peak failure load and fracture path. When notches were printed at the same location, crack propagation, peak failure load and fracture surface roughness were found to depend on both the layer and mineral fabric orientations. The uniqueness of the induced fracture path and roughness is a potential method for the assessment of the orientation and relative bonding strengths of minerals in a rock. With this information, we will be able to predict isotropic or anisotropic flow rates through fractures which is vital to induced fracturing, geothermal energy production and CO2 sequestration.

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Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than Mw3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.

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This report summarizes the results obtained during the LDRD project entitled "Partitioning of Complex Fluids at Mineral Interfaces." This research addressed fundamental aspects of such interfaces, which are relevant to energy-water applications in the subsurface, including fossil energy extraction and carbon sequestration. This project directly addresses the problem of selectivity of complex fluid components at mineral-fluid interfaces, where complex fluids are defined as a mixture of hydrophobic and hydrophilic components: e.g., water, aqueous ions, polar/nonpolar organic compounds. Specifically, this project investigates how adsorption selectivity varies with surface properties and fluid composition. Both experimental and molecular modeling techniques were used to better understand trends in surface wettability on mineral surfaces. The experimental techniques spanned the macroscale (contact angle measurements) to the nanoscale (cryogenic electronic microscopy and vibrational spectroscopy). We focused on an anionic surfactant and a well-characterized mineral phase representative of clay phases present in oil- and gas-producing shale deposits. Collectively, the results consistently demonstrate that the presence of surfactant in the aqueous fluid significantly affects the mineral-fluid interfacial structure. Experimental and molecular modeling results reveal details of the surfactant structure at the interface, and how this structure varies with surfactant coverage and fluid composition.

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Recent observations of seismic events at the subsurface energy exploration sites show that spatial and temporal correlations sometimes do not match the spatial order of the known or detected fault location from the injection well. This study investigates the coupled flow and geomechanical control on the patterns of induced seismicity along multiple basement faults that show an unusual spatiotemporal relation with induced seismicity occurring in the far field first, followed by the near field. Two possible geological scenarios considered are (1) the presence of conductive hydraulic pathway within the basement connected to the distant fault (hydraulic connectivity) and (2) no hydraulic pathway, but the coexistence of faults with mixed polarity (favorability to slip) as observed at Azle, TX. Based on the Coulomb stability analysis and seismicity rate estimates, simulation results show that direct pore pressure diffusion through a hydraulic pathway to the distant fault can generate a larger number of seismicity than along the fault close to the injection well. Prior to pore pressure diffusion, elastic stress transfer can initiate seismic activity along the favorably oriented fault, even at the longer distance to the well, which may explain the deep 2013–2014 Azle earthquake sequences. This study emphasizes that hydrological and geomechanical features of faults will locally control poroelastic coupling mechanisms, potentially influencing the spatiotemporal pattern of injection-induced seismicity, which can be used to infer subsurface architecture of fault/fracture networks.

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Permeability prediction of porous media system is very important in many engineering and science domains including earth materials, bio-, solid-materials, and energy applications. In this work we evaluated how machine learning can be used to predict the permeability of porous media with physical properties. An emerging challenge for machine learning/deep learning in engineering and scientific research is the ability to incorporate physics into machine learning process. We used convolutional neural networks (CNNs) to train a set of image data of bead packing and additional physical properties such as porosity and surface area of porous media are used as training data either by feeding them to the fully connected network directly or through the multilayer perception network. Our results clearly show that the optimal neural network architecture and implementation of physics-informed constraints are important to properly improve the model prediction of permeability. A comprehensive analysis of hyperparameters with different CNN architectures and the data implementation scheme of the physical properties need to be performed to optimize our learning system for various porous media system.

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In this work, we have characterized the calcium carbonate (CaCO3) precipitates over time caused by reaction-driven precipitation and dissolution in a micromodel. Reactive solutions were continuously injected through two separate inlets, resulting in transverse-mixing induced precipitation during the precipitation phase. Subsequently, a dissolution phase was conducted by injecting clean water (pH = 4). The evolution of precipitates was imaged in two and three dimensions (2-, 3-D) at selected times using optical and confocal microscopy. With estimated reactive surface area, effective precipitation and dissolution rates can be quantitatively compared to results in the previous works. Our comparison indicates that we can evaluate the spatial and temporal variations of effective reactive areas more mechanistically in the microfluidic system only with the knowledge of local hydrodynamics, polymorphs, and comprehensive image analysis. Our analysis clearly highlights the feedback mechanisms between reactions and hydrodynamics. Pore-scale modeling results during the dissolution phase were used to account for experimental observations of dissolved CaCO3 plumes with dissolution of the unstable phase of CaCO3. Mineral precipitation and dissolution induce complex dynamic pore structures, thereby impacting pore-scale fluid dynamics. Pore-scale analysis of the evolution of precipitates can reveal the significance of chemical and pore structural controls on reaction and fluid migration.

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Quantifying in-situ subsurface stresses and predicting fracture development are critical to reducing risks of induced seismicity and improving modern energy activities in the subsurface. In this work, we developed a novel integration of controlled mechanical failure experiments coupled with microCT imaging, acoustic sensing, modeling of fracture initiation and propagation, and machine learning for event detections and waveform characterization. Through additive manufacturing (3D printing), we were able to produce bassanite-gypsum rock samples with repeatable physical, geochemical and structural properties. With these "geoarchitected" rock, we provided the role of mineral texture orientation on fracture surface roughness. The impact of poroelastic coupling on induced seismicity has been systematically investigated to improve mechanistic understanding of post shut-in surge of induced seismicity. This research will set the groundwork for characterizing seismic waveforms by using multiphysics and machine learning approaches and improve the detection of low-magnitude seismic events leading to the discovery of hidden fault/fracture systems.

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