Browsing by Author "Chen, Cheng"

Now showing 1 - 20 of 28
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    Adsorption-Mediated Fluid Transport at the Nanoscale
    Moh, Do Yoon (Virginia Tech, 2022-04-20)
    Injecting CO2 into unconventional reservoirs to enhance oil recovery has been widely studied due to its potential to improve the profitability of these reservoirs. CO2 Huff-n-Puff is emerging as a promising method, but exploiting its full potential is challenging due to difficulties in optimizing its operations. The latter arises from the limited understanding of CO2 and oil transport in unconventional reservoirs. This dissertation used molecular dynamics simulations to study the storage and transport of oil and CO2 in unconventional reservoirs in single nanopores. The first study examined the modulation of oil flow in calcite pores by CO2. It is discovered that CO2 molecules adsorb strongly on calcite walls and can change decane permeability through 8 nm-wide pores by up to 30%. They impede decane flow at moderate adsorption density but enhance flow as adsorption approaches saturation. The second study investigated the CO2 transport in 4 nm-wide calcite pores during the soaking phase of Huff-n-Puff operations. CO2 entering the pore can become adsorbed on pore walls and diffuse on them or diffuse as free CO2 molecules. The accumulation of CO2 follows a diffusion behavior with an effective diffusivity ~50% smaller than bulk CO2. Two dimensionless groups are proposed to gauge the importance of surface adsorption and diffusion in CO2 storage and transport in nanopores. The third study examined the extraction of decane initially sealed in a 4 nm-wide calcite pore through exchange with CO2 and CH4 in a reservoir. The CO2-decane exchange is significantly driven by the evolution of adsorbed oil and gas initially, but a transition to dominance by free oil and gas occurs later; for CH4-decane exchange, the opposite occurs. The net gas accumulation and decane extraction follow the diffusive law, but their effective diffusivities do not always align well with the self-diffusion coefficients of CO2, CH4, and decane in the nanopore. The three studies identified the essential roles of gas/oil adsorption in their net transport in nanopores and, thus, unconventional reservoirs. Delineating these roles and formulating dimensionless groups to gauge their importance help develop better models for enhanced oil recovery from unconventional reservoirs by CO2 injection.
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    Application of Machine Learning and Deep Learning Methods in Geological Carbon Sequestration Across Multiple Spatial Scales
    Wang, Hongsheng (Virginia Tech, 2022-08-24)
    Under current technical levels and industrial systems, geological carbon sequestration (GCS) is a viable solution to maintain and further reduce carbon dioxide (CO2) concentration and ensure energy security simultaneously. The pre-injection formation characterization and post-injection CO2 monitoring, verification, and accounting (MVA) are two critical and challenging tasks to guarantee the sequestration effect. The tasks can be accomplished using core analyses and well-logging technologies, which complement each other to produce the most accurate and sufficient subsurface information for pore-scale and reservoir-scale studies. In recent years, the unprecedented data sources, increasing computational capability, and the developments of machine learning (ML) and deep learning (DL) algorithms provide novel perspectives for expanding the knowledge from data, which can capture highly complex nonlinear relationships between multivariate inputs and outputs. This work applied ML and DL methods to GCS-related studies at pore and reservoir scales, including digital rock physics (DRP) and the well-logging data interpretation and analysis. DRP provides cost-saving and practical core analysis methods, combining high-resolution imaging techniques, such as the three-dimensional (3D) X-ray computed tomography (CT) scanning, with advanced numerical simulations. Image segmentation is a crucial step of the DRP framework, affecting the accuracy of the following analyses and simulations. We proposed a DL-based workflow for boundary and small target segmentation in digital rock images, which aims to overcome the main challenge in X-ray CT image segmentation, partial volume blurring (PVB). The training data and the model architecture are critical factors affecting the performance of supervised learning models. We employed the entropy-based-masking indicator kriging (IK-EBM) to generate high-quality training data. The performance of IK-EBM on segmentation affected by PVB was compared with some commonly used image segmentation methods on the synthetic data with known ground truth. We then trained and tested the UNet++ model with nested architecture and redesigned skip connections. The evaluation metrics include the pixel-wise (i.e. F1 score, boundary-scaled accuracy, and pixel-by-pixel comparison) and physics-based (porosity, permeability, and CO2 blob curvature distributions) accuracies. We also visualized the feature maps and tested the model generalizations. Contact angle (CA) distribution quantifies the rock surface wettability, which regulates the multiphase behaviors in the porous media. We developed a DL-based CA measurement workflow by integrating an unsupervised learning pipeline for image segmentation and an open-source CA measurement tool. The image segmentation pipeline includes the model training of a CNN-based unsupervised DL model, which is constrained by feature similarity and spatial continuity. In addition, the over-segmentation strategy was adopted for model training, and the post-processing was implemented to cluster the model output to the user-desired target. The performance of the proposed pipeline was evaluated using synthetic data with known ground truth regarding the pixel-wise and physics-based evaluation metrics. The resulting CA measurements with the segmentation results as input data were validated using manual CA measurements. The GCS projects in the Illinois Basin are the first large-scale injection into saline aquifers and employed the latest pulsed neutron tool, the pulsed neutron eXtreme (PNX), to monitor the injected CO2 saturation. The well-logging data provide valuable references for the formation evaluation and CO2 monitoring in GCS in saline aquifers at the reservoir scale. In addition, data-driven models based on supervised ML and DL algorithms provide a novel perspective for well-logging data analysis and interpretation. We applied two commonly used ML and DL algorithms, support vector machine regression (SVR) and artificial neural network (ANN), to the well-logging dataset from GCS projects in the Illinois Basin. The dataset includes the conventional well-logging data for mineralogy and porosity interpretation and PNX data for CO2 saturation estimation. The model performance was evaluated using the root mean square error (RMSE) and R2 score between model-predicted and true values. The results showed that all the ML and DL models achieved excellent accuracies and high efficiency. In addition, we ranked the feature importance of PNX data in the CO2 saturation estimation models using the permutation importance algorithm, and the formation sigma, pressure, and temperature are the three most significant factors in CO2 saturation estimation models. The major challenge for the CO2 storage field projects is the large-scale real-time data processing, including the pore-scale core and reservoir-scale well-logging data. Compared with the traditional data processing methods, ML and DL methods achieved accuracy and efficiency simultaneously. This work developed ML and DL-based workflows and models for X-ray CT image segmentation and well-logging data interpretations based on the available datasets. The performance of data-driven surrogate models has been validated regarding comprehensive evaluation metrics. The findings fill the knowledge gap regarding formation evaluation and fluid behavior simulation across multiple scales, ensuring sequestration security and effect. In addition, the developed ML and DL workflows and models provide efficient and reliable tools for massive GCS-related data processing, which can be widely used in future GCS projects.
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    Applications and Development of Intelligent UAVs for the Resource Industries
    Bishop, Richard Edwin (Virginia Tech, 2022-04-21)
    Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly with GPS localization and advanced mission planning software. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy in GPS-denied environments. Developments in lidar technology and Simultaneous Localization and Mapping (SLAM) algorithms are enabling UAVs to function safely underground where they can be used to map workings and digitally reconstruct them into 3D point clouds for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drone-based lidar and photogrammetry were used to capture 3D point clouds of underground mines, and the associated applications of mine digitization, such as geotechnical analysis and pillar strength analysis. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites.
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    Applications of Close-Range Terrestrial 3D Photogrammetry to Improve Safety in Underground Stone Mines
    Bishop, Richard (Virginia Tech, 2020-05-22)
    The underground limestone mining industry is a small, but growing segment of the U.S. crushed stone industry. However, its fatality rate has been amongst the highest of the mining sector in recent years due to ground control issues related to ground collapses. It is therefore important to improve the engineering design, monitoring and visualization of ground control by utilizing new technologies that can help an underground limestone company maintain a safe and productive operation. Photogrammetry and laser scanning are remote sensing technologies that are useful tools for collecting three-dimensional spatial data with high levels of precision for many types of mining applications. Due to the reality of budget constraints for many underground stone mining operations, this research concentrates on photogrammetry as a more accessible technology for the average operation. Despite the challenging lighting conditions and size of underground limestone mines that has previous hindered photogrammetric surveys in these environments, over 13,000 photographic images were taken over a 3-year period in active mines to compile these models. This research summarizes that work and highlights the many applications of terrestrial close-range photogrammetry, including practical methodologies for implementing the techniques in working operations to better visualize hazards and pragmatic approaches for geotechnical analysis, improved engineering design and monitoring.
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    Assessment of the Geological Storage Potential of Carbon Dioxide in the Mid-Atlantic Seaboard: Focus on the Outer Continental Shelf of North Carolina
    Mullendore, Marina Anita Jacqueline (Virginia Tech, 2019-05-02)
    In an effort to mitigate carbon dioxide (CO2) emissions in the atmosphere, the Southeast Offshore Storage Resource Assessment (SOSRA) project has for objective to identify geological targets for CO2 storage in two main areas: the eastern part of the Gulf of Mexico and the Atlantic Ocean subsurface. SOSRA's second objective is to estimate the geological targets' capacity to store up to 30 million metric tons of CO2 each year with an error margin of ±30%. As part of this project, the research presented here focuses on the outer continental shelf of North Carolina and its potential for the deployment of large-scale offshore carbon storage in the near future. To identify geological targets, workflow followed typical early oil and gas exploration protocols: collecting existing datasets, selecting the most applicable datasets for reservoir exploration, and interpreting datasets to build a comprehensive regional geological framework of the subsurface of the outer continental shelf. The geomodel obtained can then be used to conduct static volumetric calculations estimating the storage capacity of each identified target. Numerous uncertainties regarding the geomodel were attributed to the variable coverage and quality of the geological and geophysical data. To address these uncertainties and quantify their potential impact on the storage capacity estimations, dynamic volumetric calculations (reservoir simulations) were conducted. Results have shown that, in this area, both Upper and Lower Cretaceous Formations have the potential to store large amounts of CO2 (in the gigatons range). However, sensitivity analysis highlighted the need to collect more data to refine the geomodel and thereby reduce the uncertainties related to the presence, dimensions and characteristics of potential reservoirs and seals. Reducing these uncertainties could lead to more accurate storage capacity estimations. Adequate injection strategies could then be developed based on robust knowledge of this area, thus increasing the probability of success for carbon capture and storage (CCS) offshore projects in North Carolina's outer continental shelf.
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    Correlations between the Mineralogy and Recovery Behavior of Rare Earth Elements (REEs) in Coal Waste
    Ji, Bin (Virginia Tech, 2023-01-12)
    Many literatures have been published recently regarding the recovery of REEs from coal-related materials, such as coal waste, acid mine drainage, and coal combustion ash. The recovery of REEs from coal waste has been investigated by the author in recent years, and it was found that after calcination at 600 ℃ for 2 h, a significant improvement in REE recovery can be achieved. In order to reveal the mechanisms of the enhanced REE recovery after calcination, coal waste samples from two different seams, i.e., Western Kentucky No. 13 and Fire Clay, were selected to investigate the modes of occurrence of REEs. Scanning electron microscopy- and transmission electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS and TEM-EDS) analyses were conducted to investigate the mineralogy of REEs in two coal waste samples. Totally, 49 and 50 REE-bearing particles were found from the SEM specimens of Western Kentucky No. 13 and Fire Clay coal waste samples, respectively. Based on the elemental composition analyses and TEM-EDS characterization, it was found that apatite, monazite, and crandallite-group minerals were the major light REE (LREE) carriers, while the heavy REEs (HREE) primarily occurred in zircon and xenotime in these two coal waste samples. Further analyzing the REE content and number of REE-bearing particles, it was confirmed that monazite, xenotime, and crandallite-group minerals were the dominant contributors to the total REE (TREE) contents in both materials. In addition to the mineralogy of REEs, the morphology of REE-bearing particles was also investigated. The SEM images suggested that the particle size of most REE-bearing particles was less than 5 μm. Moreover, not only completely liberated particles, but particles encapsulated by the host minerals present in the two coal waste samples. To identify the changes of mineralogy of REEs after recovery, the leaching solid residues of the raw and calcined coal waste samples were also characterized by SEM-EDS analysis. After REE recovery, the same REE mineralogical results were observed from the leaching residues of the raw coal waste samples. However, as for the calcined samples, the crandallite-group minerals disappeared. These results suggested that the crandallite-group minerals were decomposed into easy-to-leach forms after calcination at 600 ℃, thus leading to the improved REE recovery. Moreover, the number of REE-bearing particles (N) found from per area of the calcined leaching residue was confirmed to be larger compared to that of the raw ones. A combination analysis of these results indicated that two mechanisms of the enhanced REE recovery after calcination can be confirmed: (1) decomposing the crandallite-group minerals into more soluble species; and (2) promoting the liberation of the REE-bearing particles encapsulated in the host minerals. The thermal decomposition of crandallite-group minerals was mainly responsible for the enhanced REE recovery from coal waste. However, as a result of the complex isomorphic substitutions and association characteristics, it is difficult to collect a pure endmember of crandallite-group mineral for characterization. Therefore, florencite-(Ce) was synthesized in this study. X-ray diffraction (XRD), SEM-EDS, TEM, thermogravimetric and differential thermal analyses (TGA-DTA), and acid leaching tests were conducted on the synthesized product. The results showed that the variation in Ce leaching recovery corresponded to the phase transformation of florencite. The gradual transformation of florencite from a crystalline mineral into an amorphous phase resulted in the increases in the solubility of Ce. In addition, the thermal transformation of florencite was an independent reaction, which was not interfered by the host materials, such as kaolinite and coal waste.
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    Distress Image Library for Precision and Bias of Fully Automated Pavement Cracking Survey
    Wang, Kelvin C. P.; Ji, Ran; Chen, Cheng (2014-09)
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    Experimental, Theoretical, and Numerical Investigations of Geomechanics/Flow Coupling in Energy Georeservoirs
    Li, Zihao (Virginia Tech, 2021-09-01)
    The development of hydrocarbon energy resources from shale, a fine-grained, low-permeability geological formation, has altered the global energy landscape. Constricting pressure exerted on a shale formation has a significant effect on the rock's apparent permeability. Gas flow in low-permeability shales is significantly different from liquid flow due to the Klinkenberg effect caused by gas molecule slip at the nanopore wall surfaces. This has the effect of increasing apparent permeability (i.e., the measured permeability). Optimizing the conductivity of the proppant assembly is another critical component of designing subsurface hydrocarbon production using hydraulic fracturing. Significant fracture conductivity can be achieved at a much lower cost than conventional material costs, according to the optimal partial-monolayer proppant concentration (OPPC) theory. However, hydraulic fracturing performance in unconventional reservoirs is problematic due of the complex geomechanical environment, and the experimental confirmation and investigation of the OPPC theory have been rare in previous studies. In this dissertation, a novel multiphysics shale transport (MPST) model was developed to account for the coupled multiphysics processes of geomechanics, fluid dynamics, and the Klinkenberg effect in shales. Furthermore, A novel multi-physics multi-scale multi-porosity shale gas transport (M3ST) model was developed based on the MPST model research to investigate shale gas transport in both transient and steady states, and a double-exponential empirical model was also developed as a powerful substitute for the M3ST model for fitting laboratory-measured apparent permeability. Additionally, throughout the laboratory experiment of fracture conductivity with proppant, the four visible stages documented the evolution of non-monotonic conductivity and proppant concentration. The laboratory methods and empirical model were then applied to the shale plugs from Central Appalachia to investigate the formation properties there. The benefits of developing these regions wisely include a smaller surface footprint, reduced infrastructure requirements, and lower development costs. The developed MPST, M3ST, double-exponential empirical models and research findings shed light on the role of multiphysics mechanisms, such as geomechanics, fluid dynamics and transport, and the Klinkenberg effect, in shale gas transport across multiple spatial scales in both steady and transient states. The fracture conductivity experiments successfully validate the theory of OPPC and illustrate that proppant embedment is the primary mechanism that causes the competing process between fracture width and fracture permeability and consequently the non-monotonic fracture conductivity evolution as a function of increasing proppant concentration. The laboratory experimental facts and the numerical fittings in this study provided critical insights into the reservoir characterization in Central Appalachia and will benefit the reservoir development using non-aqueous fracturing techniques such as CO2 and advanced proppant technologies in the future.
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    Field Laboratory for Emerging Stacked Unconventional Plays (ESUP): Project No. DE-FE0031576
    Ripepi, Nino; Karmis, Michael E.; Chen, Cheng; Gilliland, Ellen; Nojabaei, Bahareh (Virginia Tech, 2018-08-24)
    The objective for this project is to investigate and characterize the resource potential for multi-play production of emerging unconventional reservoirs in Central Appalachia. The project team includes Virginia Tech; Virginia Center for Coal & Energy Research; Enervest Operating, LCC; Pashin Geoscience, LLC; and Gerald R. Hill, PhD, Inc. The anticipated duration of the project is April 1, 2018 - March 31, 2023.
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    The Impact Dynamics of Weakly Charged Droplets
    Gao, Fan (Virginia Tech, 2019-08-07)
    Electric charges are often found in naturally or artificially formed droplets, such as raindrops and those generated by Kelvin's water dropper. In contrast to the impact of neutral droplets on a flat solid surface upon which a thin convex lens shape layer of the gas film is typically formed, I show that the delicate gas thin film can be fundamentally altered for even weakly charged droplets both experimentally and numerically. As the charge level is raised above a critical level of about 1% of the Rayleigh limit for representative impact conditions, the Maxwell stress overcomes the gas pressure buildup to deform the droplet bottom surface. A conical liquid tip forms and pierces Through the gas film, leading to a circular contact line moving outwards that does not trap any gas. The critical charge level only depends on the capillary number based on the gas viscosity. The deformation applies to common liquids and molten alloy droplets. Even dielectric surfaces can also induce conical deformation. The charged droplets can also deform upon hydrophobic surfaces, and increase the contact time on hydrophobic surfaces or even avoid bouncing.
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    Improving Molecular Sensitivity in X-Ray Fluorescence Molecular Imaging (XFMI) of Iodine Distribution in Mouse-Sized Phantoms via Excitation Spectrum Optimization
    Dong, Xu; Chen, Cheng; Cao, Guohua (IEEE, 2018-10-25)
    X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. Recently, progress has been made in enabling the XFMI technique with laboratory X-ray sources for various biomedical applications. However, the sensitivity of XFMI still needs to be improved for in vivo biomedical applications at a reasonably low radiation dose. In laboratory X-ray source-based XFMI, the main factor that limits the molecular sensitivity of XFMI is the scatter X-rays that coincide with the fluorescence X-rays from the targeted material. In this paper, we experimentally investigated the effects of excitation beam spectrum on the molecular sensitivity of XFMI, by quantitatively deriving minimum detectable concentration (MDC) under a xed surface entrance dose of 200 mR at three different excitation beam spectra. XFMI experiments were carried out on two customized mouse-sized phantoms. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data. The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could nd applications for in vivo pre-clinical small-animal XFMI in the future.
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    Integrated Experimental Characterization of the Lower Huron Shale in the Central Appalachian Basin
    Tan, Xinyu (Virginia Tech, 2020-06-04)
    Reservoir characterization is an essential step in the oil/gas exploration process and is of great significance in the evaluation of oil/gas resources. To evaluate the production potential of the Lower Huron shale in the central Appalachian Basin, matrix permeability, Raman spectroscopy, Fourier Transform infrared spectroscopy (FTIR), and atomic force microscopy (AFM) were used in this study. According to the experimental results, matrix permeability is relatively high for a shale gas formation, suggesting great production potential of shale gas resources in this region. Additionally, four shale samples with varying thermal maturity were characterized by the complementary Raman and FTIR spectroscopy, and curve-fitting results successfully demonstrated the change of chemical structures with the evolution of thermal maturity. Raman spectroscopy results show that the curve fitted G band position and the band separation between the G band and D1 band tend to increase with the rise of thermal maturity level. Results of FTIR spectroscopy show that the aromaticity level and the condensation extent of aromatic rings show an increasing tendency with the increase of maturation level. Moreover, mechanical properties of these four shale samples were characterized by AFM. Results show that Young's modulus is in the range of 8.20 GPa - 12.94 GPa, which is in the normal range compared with the results from other shale formations. Additionally, scanned results show an increasing tendency for Young's modulus of the organic components with the rise of thermal maturity level in these shale samples. The potential reason for this phenomenon was also explored, specifically, the growth of aromatic groups and the decrease of the CH2/CH3 ratio may be possible reasons for the rise of Young's modulus of organic components in these shale samples. This work is meaningful for the evaluation of shale gas resources, especially emerging plays, in the central Appalachian Basin, and it also provides a valuable database for relevant research on shale matrix permeability, Raman, FTIR and AFM.
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    Integrating Laser Scanning with Discrete Element Modeling for Improving Safety in Underground Stone Mines
    Monsalve, Juan J. (Virginia Tech, 2019-05-10)
    According to the Mine Health and Safety Administration (MSHA), between 2006 and 2016, the underground stone mining industry had the highest fatality rate in 4 out of 10 years, compared to any other type of mining in the United States. Additionally, the National Institute for Occupational Safety and Health (NIOSH) stated that structurally controlled instability is a predominant failure mechanism in underground limestone mines. This type of instability occurs when the different discontinuity sets intercept with each other forming rock blocks that displace inwards the tunnel as the excavation takes place, posing a great hazard for miners and overall mine planning. In recent years, Terrestrial laser scanning (TLS) has been used for mapping and characterizing fractures present in a rock mass. TLS is a technology that allows to generate a three-dimensional multimillion point cloud of a scanned area. In addition to this, the advances in computing power throughout the past years, have allowed numerical modeling codes to represent more realistically the behavior of a fractured rock masses. This work presents and implements a methodology that integrates laser scanning technology along with Discrete Element Modeling as tools for characterizing, preventing, and managing structurally controlled instability that may affect large-opening underground mines. The stability of an underground limestone mine that extracts a dipping ore body with a room and pillar (and eventual stoping) mining method is analyzed with this approach. While this methodology is proposed based on a specific case study that does not meet the requirements to be designed with current NIOSH published guidelines, this process proposes a general methodology that can be applied in any mine experiencing similar failure mechanisms, considering site-specific conditions. The aim of this study is to ensure the safety of mine workers and to reduce accidents that arise from ground control issues. The results obtained from this methodology allowed us to generate Probability Density Functions to estimate the probability of rock fall in the excavations. These models were also validated by comparing the numerical model results with those obtained from the laser scans.
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    Investigation of Nanopore Confinement Effects on Convective and Diffusive Multicomponent Multiphase Fluid Transport in Shale using In-House Simulation Models
    Du, Fengshuang (Virginia Tech, 2020-09-28)
    Extremely small pore size, low porosity, and ultra-low permeability are among the characteristics of shale rocks. In tight shale reservoirs, the nano-confinement effects that include large gas-oil capillary pressure and critical property shifts could alter the phase behaviors, thereby affecting the oil or gas production. In this research, two in-house simulation models, i.e., a compositionally extended black-oil model and a fully composition model are developed to examine the nano-pore confinement effects on convective and diffusive multicomponent multiphase fluid transport. Meanwhile, the effect of nano-confinement and rock intrinsic properties (porosity and tortuosity factor) on predicting effective diffusion coefficient are investigated. First, a previously developed compositionally extended black-oil simulation approach is modified, and extended, to include the effect of large gas-oil capillary pressure for modeling first contact miscible (FCM), and immiscible gas injection. The simulation methodology is applied to gas flooding in both high and very low permeability reservoirs. For a high permeability conventional reservoir, simulations use a five-spot pattern with different reservoir pressures to mimic both FCM and immiscible displacements. For a tight oil-rich reservoir, primary depletion and huff-n-puff gas injection are simulated including the effect of large gas-oil capillary pressure in flow and in flash calculation on recovery estimations. A dynamic gas-oil relative permeability correlation that accounts for the compositional changes owing to the produced gas injection is introduced and applied to correct for changes in interfacial tension (IFT), and its effect on oil recovery is examined. The results show that the simple modified black-oil approach can model well both immiscible and miscible floods, as long as the minimum miscibility pressure (MMP) is matched. It provides a fast and robust alternative for large-scale reservoir simulation with the purpose of flaring/venting reduction through reinjecting the produced gas into the reservoir for EOR. Molecular diffusion plays an important role in oil and gas migration in tight shale formations. However, there are insufficient reference data in the literature to specify the diffusion coefficients within porous media. Another objective of this research is to estimate the diffusion coefficients of shale gas, shale condensate, and shale oil at reservoir conditions with CO2 injection for EOR/EGR. The large nano-confinement effects including large gas-oil capillary pressure and critical property shifts could alter the phase behaviors. This study estimates the diffusivities of shale fluids in nanometer-scale shale rock from two perspectives: 1) examining the shift of diffusivity caused by nanopore confinement effects from phase change (phase composition and fluid property) perspective, and 2) calculating the effective diffusion coefficient in porous media by incorporating rock intrinsic properties (porosity and tortuosity factor). The tortuosity is obtained by using tortuosity-porosity relations as well as the measured tortuosity of shale from 3D imaging techniques. The results indicated that nano-confinement effects could affect the diffusion coefficient through altering the phase properties, such as phase compositions and densities. Compared to bulk phase diffusivity, the effective diffusion coefficient in porous shale rock is reduced by 102 to 104 times as porosity decreases from 0.1 to 0.03. Finally, a fully compositional model is developed, which enables us to process multi-component multi-phase fluid flow in shale nano-porous media. The validation results for primary depletion, water injection, and gas injection show a good match with the results of a commercial software (CMG, GEM). The nano-confinement effects (capillary pressure effect and critical property shifts) are incorporated in the flash calculation and flow equations, and their effects on Bakken oil production and Marcellus shale gas production are examined. The results show that including oil-gas capillary pressure effect could increase the oil production but decrease the gas production. Inclusion of critical property shift could increase the oil production but decrease the gas production very slightly. The effect of molecular diffusion on Bakken oil and Marcellus shale gas production are also examined. The effect of diffusion coefficient calculated by using Sigmund correlation is negligible on the production from both Bakken oil and Marcellus shale gas huff-n-puff. Noticeable increase in oil and gas production happens only after the diffusion coefficient is multiplied by 10 or 100 times.
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    Measurements, Modeling and Analysis of High Pressure Gas Sorption in Shale and Coal for Unconventional Gas Recovery and Carbon Sequestration
    Tang, Xu (Virginia Tech, 2017-01-10)
    In order to exploit unconventional gas and estimate carbon dioxide storage potential in shale formations and coal seams, two key questions need to be initially answered: 1) What is the total gas-in-place (GIP) in the subsurface reservoirs? 2) What is the exact ratio between bulk gas content and adsorbed gas content? Both questions require precise estimation of adsorbed phase capacity of gases (methane and carbon dioxide) and their adsorption behavior in shale and coal. This dissertation therefore analyzes adsorption isotherms, thermodynamics, and kinetics properties of methane and carbon dioxide in shale and coal based on experimental results to provide preliminary answers to both questions. It was found that the dual-site Langmuir model can describe both methane and carbon dioxide adsorption isotherms in shale and coal under high pressure and high temperature conditions (up to 27 MPa and 355.15K). This allows for accurate estimation of the true methane and carbon dioxide GIP content and the relative quantity of adsorbed phases of gases at in situ temperatures and pressures representative of deep shale formations and coal seams. The concept of a deep shale gas reservoir is then proposed to optimize shale gas development methodology based on the successful application of the model for methane adsorption in shale. Based on the dual-site Langmuir model, the isosteric heat of adsorption is calculated analytically by considering both the real gas behavior and the adsorbed phase under high pressure, both of which are ignored in the classic Clausius–Clapeyron approximation. It was also found that the isosteric heat of adsorption in Henry's pressure region is independent of temperature and can serve as a quantified index to evaluate the methane adsorption affinity on coal. In order to understand the dynamic response of gas adsorption in coal for carbon sequestration, both gas adsorption kinetics and pore structure of coal are investigated. The pseudo-second order model is applied to simulate the adsorption kinetics of carbon dioxide in coals under different pressures. Coal particle size effects on pore characterization of coal and carbon dioxide and nitrogen ad/desorption behavior in coal was also investigated.
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    Methane Emission Monitoring of Appalachian Compressor Station
    Lataille, Roger Andrew (Virginia Tech, 2022-01-19)
    A single compressor station site along a gathering line network was monitored for fugitive methane emissions to quantify long-term emissions in Appalachia Virginia. Continuous monitoring was conducted from January 2021 to April 2021. The compressor station undergoing monitoring operated two CAT3516 Tale and one CAT3516 B engines operating at 80% of max output flow. Data presented on methane emissions during this period was gathered with an eddy covariance monitoring station. This station was equipped with an LI-7700 methane analyzer, LI-7500A - CO_2/H_2 O analyzer as well as a sonic anemometer these sensors could be observed remotely through cellular connection. The data is represented in flux output ((µmol)/(s m^2 )) as well as kg CO_2 equivalence of methane outlined by the EPA greenhouse gas inventory. The average daily emissions for this compressor station are estimated to be 136 kg CO_2 equivalent emissions. This study shows that the site during the observational period the compressor station emitted on average are estimated to be 5.43 kg of CH_4 per day.
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    Molecular Dynamics Study of Nano-confinement Effect on Hydrocarbons Fluid Phase Behavior and Composition in Organic Shale
    de Carvalho Jacobina Andrade, Deraldo (Virginia Tech, 2021-03-31)
    The depletion of conventional oil reservoirs forced companies and consequently researchers to pursue alternatives such as resources that in the past were considered not economically viable, in consequence of the high depth, low porosity and permeability of the play zone. The exploration challenges were overcome mainly by the development of horizontal drilling and hydraulic fracturing. However, the extremely high temperatures and pressures, in association to a complex nanopore structure, in which reservoir fluids are now encountered, instigate further investigation of fluid phase behavior and composition, and challenge conventional macroscale reservoir simulation predictions. Moreover, the unusual high temperatures and pressures have increased the cost as well as the hazardous level for reservoir analyzes by lab experiments. Molecular Dynamics (MD) simulation of reservoirs can be a safe and inexpensive alternative tool to replicate reservoir pore and fluid conditions, as well as to monitor fluid behavior. In this study, a MD simulation of nanoconfinement effect on hydrocarbon fluid phase and compositional behavior in organic shale rocks is presented. Chapter 1 reviews and discusses previous works on MD simulations of geological resources. With the knowledge acquired, a fully atomistic squared graphite pore is proposed and applied to study hydrocarbon fluid phase and compositional behavior in organic shale rocks in Chapter 2. Results demonstrate that nano-confinement increases fluid mass density, which can contribute to phase transition, and heptane composition inside studied pores. The higher fluid density results in an alteration of oil in place (OIP) prediction by reservoir simulations, when nano-confinement effect is not considered.
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    Multi-scale Investigations of Geological Carbon Sequestration in Deep Saline Aquifers
    Guo, Ruichang (Virginia Tech, 2022-05-25)
    Geological carbon dioxide (CO2) sequestration (GCS) in deep saline aquifers is viewed as a viable solution to dealing with the impact of anthropogenic CO2 emissions on global warming. The trapping mechanisms that control GCS include capillary trapping, structural trapping, dissolution trapping, and mineral trapping. Wettability and density-driven convection play an important role in GCS, because wettability significantly affects the efficiency of capillary trapping, and density-driven convection greatly decreases the time scale of dissolution trapping. This work focuses on the role of wettability on multiphase flow in porous media, density-driven convection in porous media, and their implications for GCS in deep saline aquifers. Wettability is a critical control over multiphase fluid flow in porous media. However, our understanding on the wettability heterogeneity of a natural rock and its effect on multiphase fluid flow in a natural rock is limited. This work innovatively models the heterogeneous wettability of a rock as a correlated random field. The realistic wetting condition of a natural rock can be reconstructed with in-situ measurements of wettability on the internal surfaces of the rock. A Bentheimer sandstone was used to demonstrate the workflow to model and reconstruct a wettability field. Relative permeability, capillary pressure-water saturation relation are important continuum-scale properties controlling multiphase flow in porous media. This work employed lattice Boltzmann method to simulate the displacement process. We found that pore-scale surface wettability heterogeneity caused noticeable local scCO2 and water redistributions under less water-wet conditions at the pore scale. At the continuum scale, the capillary pressure-water saturation curve under the heterogeneous wetting condition was overall similar to that under the homogeneous wetting condition. This suggested that the impact of local wettability heterogeneity on the capillary pressure-water saturation curve was averaged out at the entire-sample scale. The only difference was that heterogeneous wettability led to a negative entry pressure at the primary drainage stage under the intermediate-wet condition. The impact of pore-scale wettability heterogeneity was more noticeable on the relative permeability curves. Particularly, the variation of the scCO2 relative permeability curve in the heterogeneous wettability scenario was more significant than that in the homogenous wettability scenario. Results showed that higher wettability heterogeneity (i.e., higher standard deviation and higher correlation length) increased the variations in the CO2/brine relative permeability curves. Dissolution of CO2 into brine is a primary mechanism to ensure the long-term security of GCS. CO2 dissolved in brine increases the CO2-brine solution density and thus can cause downward convection. Onset of density-driven instability and onset of convective dissolution are two critical events in the transition process from a diffusion-dominated regime to a convection-dominated regime. In the laboratory, we developed an empirical correlation between light intensity and in-situ solute concentration. Based on the novel and well-controlled experimental methods, we measured the critical Rayleigh-Darcy number and critical times for the onset of density-driven instability and convective dissolution. To further investigate the impact of permeability heterogeneity on density-driven convection, a three-dimensional (3D) fluidics method was proposed to advance the investigation on density-driven convection in porous media. Heterogeneous porous media with desired spatial correlations were efficiently built with 3D-printed elementary porous blocks. In the experiments, methanol-ethylene-glycol (MEG), was used as surrogate fluid to CO2. The heterogeneous porous media were placed in a transparent tank allowing visual observations. Results showed that permeability structure controlled the migration of MEG-rich water. Permeability heterogeneity caused noticeable uncertainty in dissolution rates and uncertainty in dissolution rates increases with correlation length. To sum up, this work comprehensively employed novel experimental methods and large-scale direct simulations to investigate the sequestration of CO2 in saline aquifers at a pore scale and a continuum scale. The findings advanced our understanding on the role of wettability heterogeneity and permeability heterogeneity on GCS in deep saline aquifers.
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    Multiphysics Transport in Heterogeneous Media: from Pore-Scale Modeling to Deep Learning
    Wu, Haiyi (Virginia Tech, 2020-05-21)
    Transport phenomena in heterogeneous media play a crucial role in numerous engineering applications such as hydrocarbon recovery from shales and material processing. Understanding and predicting these phenomena is critical for the success of these applications. In this dissertation, nanoscale transport phenomena in porous media are studied through physics-based simulations, and the effective solution of forward and inverse transport phenomena problems in heterogeneous media is tackled using data-driven, deep learning approaches. For nanoscale transport in porous media, the storage and recovery of gas from ultra-tight shale formations are investigated at the single-pore scale using molecular dynamics simulations. In the single-component gas recovery, a super-diffusive scaling law was found for the gas production due to the strong gas adsorption-desorption effects. For binary gas (methane/ethane) mixtures, surface adsorption contributes greatly to the storage of both gas in nanopores, with ethane enriched compared to methane. Ethane is produced from nanopores as effectively as the lighter methane despite its slower self-diffusion than the methane, and this phenomenon is traced to the strong couplings between the transport of the two species in the nanopore. The dying of solvent-loaded nanoporous filtration cakes by a purge gas flowing through them is next studied. The novelty and challenge of this problem lie in the fact that the drainage and evaporation can occur simultaneously. Using pore-network modeling, three distinct drying stages are identified. While drainage contributes less and less as drying proceeds through the first two stages, it can still contribute considerably to the net drying rate because of the strong coupling between the drainage and evaporation processes in the filtration cake. For the solution of transport phenomena problems using deep learning, first, convolutional neural networks with various architectures are trained to predict the effective diffusivity of two-dimensional (2D) porous media with complex and realistic structures from their images. Next, the inverse problem of reconstructing the structure of 2D heterogeneous composites featuring high-conductivity, circular fillers from the composites' temperature field is studied. This problem is challenging because of the high dimensionality of the temperature and conductivity fields. A deep-learning model based on convolutional neural networks with a U-shape architecture and the encoding-decoding processes is developed. The trained model can predict the distribution of fillers with good accuracy even when coarse-grained temperature data (less than 1% of the full data) are used as an input. Incorporating the temperature measurements in regions where the deep learning model has low prediction confidence can improve the model's prediction accuracy.
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    Neural network based pore flow field prediction in porous media using super resolution
    Zhou, Xu-Hui X.; McClure, James; Chen, Cheng; Xiao, Heng (2021)
    Previous works have demonstrated using the geometry of the microstructure of porous media to predict the ow velocity fields therein based on neural networks. However, such schemes are purely based on geometric information without accounting for the physical constraints on the velocity fields such as that due to mass conservation. In this work, we propose using a super-resolution technique to enhance the velocity field prediction by utilizing coarse-mesh velocity fields, which are often available inexpensively but carry important physical constraints. We apply our method to predict velocity fields in complex porous media. The results demonstrate that incorporating the coarse-mesh flow field significantly improves the prediction accuracy of the fine-mesh flow field as compared to predictions that rely on geometric information alone. This study highlights the merits of including coarse-mesh flow field with physical constraints embedded in it.