Browsing by Author "Nojabaei, Bahareh"

Now showing 1 - 19 of 19
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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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    CO2 Minimum Miscibility Pressure and Recovery Mechanisms in Heterogeneous Low Permeability Reservoirs
    Zhang, Kaiyi (Virginia Tech, 2019-09-16)
    Benefited from the efficiency of hydraulic fracturing and horizon drilling, the production of unconventional oil and gas resources, such as shale gas and tight oil, has grown quickly in 21th century and contributed to the North America oil and gas production. Although the new enhancing oil recover (EOR) technologies and strong demand spike the production of unconventional resources, there are still unknowns in recovery mechanisms and phase behavior in tight rock reservoirs. In such environment, the phase behavior is altered by high capillary pressure owing to the nanoscale pore throats of shale rocks and it may also influence minimum miscibility pressure (MMP), which is an important parameter controlling gas floods for CO2 injection EOR. To investigate this influence, flash calculation is modified with considering capillary pressure and this work implements three different method to calculate MMP: method of characteristics (MOC); multiple mixing cell (MMC); and slim-tube simulation. The results show that CO2 minimum miscibility pressure in nanopore size reservoirs are affected by gas-oil capillary pressure owing to the alternation of key tie lines in displacement. The values of CO2-MMP from three different methods match well. Moreover, in tight rock reservoirs, the heterogeneous pore size distribution, such as the ones seen in fractured reservoirs, may affect the recovery mechanisms and MMP. This work also investigates the effect of pore size heterogeneity on multicomponent multiphase hydrocarbon fluid composition distribution and its subsequent influence on mass transfer through shale nanopores. According to the simulation results, compositional gradient forms in heterogeneous nanopores of tight reservoirs because oil and gas phase compositions depend on the pore size. Considering that permeability is small in tight rocks and shales, we expect that mass transfer within heterogeneous pore size porous media to be diffusion-dominated. Our results imply that there can be a selective matrix-fracture component mass transfer during both primary production and gas injection secondary recovery in fractured shale rocks. Therefore, molecular diffusion should not be neglected from mass transfer equations for simulations of gas injection EOR or primary recovery of heterogeneous shale reservoirs with pore size distribution.
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    Data Analyses of Quarry Operations and Maintenance Schedules: A Production Optimization Study
    George, Brennan; Nojabaei, Bahareh (MDPI, 2023-06-15)
    In this research, data analytics and machine learning were used to identify the performance metrics of loaders and haul trucks during mining operations. We used real-time collected data from loaders and haul trucks operating in multiple quarries to broaden the scope of the study and remove bias. Our model indicates relationships between multiple variables and their impacts on production in an operation. Data analysis was also applied to ground engagement tools (GET) to identify key preventative maintenance schedules to minimize production impact from capital equipment downtime. Through analysis of the loader’s data, it was found there is an efficient cycle time of around 35 s to 40 s, which yielded a higher payload. The decision tree classifier algorithm created a model that was 87.99% accurate in estimating the performance of a loader based on a full analysis of the data. Based on the distribution of production variables across each type of loader performing in a similar work environment, the Caterpillar 992K and 990K were the highest-yielding machines. Production efficiency was compared before and after maintenance periods of ground engaging tools on loader buckets. With the use of maintenance and production records for these tools, it was concluded that there was no distinguishable change in average production and percentage change in production value before and after maintenance days.
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    Development of Potential Remote Coal Mine Fire Response Measures: Use of Multiple Passive Source Tracers and Simulation of High Expansion Foam Flow in Simulated Gob Material
    Watkins, Eric Andrew (Virginia Tech, 2018-06-26)
    This thesis examines potential improvements to current coal mine fire response measures. In the event of a fire scenario, indirect testing and analysis of the exhausting air is needed to characterize changes in the fire. The application of multiple passive source tracers provides improved detail of complex ventilation interactions over an extended period of time. The first work in this thesis details the testing of the passive release rates for three Perfluorocarbon tracer compounds over a 180-day period. The results of this study demonstrate the ability for the permeation plug release vessel design to release Perfluorocarbon tracers at a steady rate. Current response methods for a fire in a coal mine gob consist of injection of inert gas and sealing of the mine openings. Injection of high expansion foam into the gob from the surface has potential to improve extinguishment of the fire and reduce the time needed to bring the mine back to an operational state. The applicability of this method requires computational modeling and field testing. The second part of this thesis determines the Darcy and Forchheimer values for high expansion foam flow in simulated gob material with a lab experiment. The experiment was replicated in the CFD software, OpenFOAM, to validate the methods for calculation of the Darcy and Forchheimer values. The results of this study provide a tested methodology for a future full scale modeling of high expansion foam injection in a coal mine gob.
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    Effect of Pore Size Heterogeneity on Hydrocarbon Fluid Distribution, Transport, and Primary and Secondary Recovery in Nano-Porous Media
    Zhang, Kaiyi; Du, Fengshuang; Nojabaei, Bahareh (MDPI, 2020-04-03)
    In this paper, we investigate the effect of pore size heterogeneity on fluid composition distribution of multicomponent-multiphase hydrocarbons and its subsequent influence on mass transfer in shale nanopores. The change of multi-contact minimum miscibility pressure (MMP) in heterogeneous nanopores was investigated. We used a compositional simulation model with a modified flash calculation, which considers the effect of large gas–oil capillary pressure on phase behavior. Different average pore sizes for different segments of the computational domain were considered and the effect of the resulting heterogeneity on phase change, composition distributions, and production was investigated. A two-dimensional formulation was considered here for the application of matrix–fracture cross-mass transfer and the rock matrix can also consist of different segments with different average pore sizes. Both convection and molecular diffusion terms were included in the mass balance equations, and different reservoir fluids such as ternary mixture syntactic oil, Bakken oil, and Marcellus shale condensate were considered. The simulation results indicate that oil and gas phase compositions vary in different pore sizes, resulting in a concentration gradient between the two adjacent pores of different sizes. Given that shale permeability is extremely small, we expect the mass transfer between the two sections of the reservoir/core with two distinct average pore sizes to be diffusion-dominated. This observation implies that there can be a selective matrix–fracture component mass transfer as a result of confinement-dependent phase behavior. Therefore, the molecular diffusion term should be always included in the mass transfer equations, for both primary and gas injection enhanced oil recovery (EOR) simulation of heterogeneous shale reservoirs.
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    Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States
    Shahdi, Arya; Lee, Seho; Karpatne, Anuj; Nojabaei, Bahareh (2021-07-02)
    Geothermal scientists have used bottom-hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost and Random Forest result in the highest accuracy for subsurface temperature prediction. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for 58 wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost-predicted temperatures along the depth. Finally, through our analysis, the most favorable geological locations are suggested for potential future geothermal developments.
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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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    Fundamental Studies on the Extraction of Rare Earth Elements from Ion Adsorption Clays
    Onel, Oznur (Virginia Tech, 2023-10-12)
    Rare earth elements (REEs) are critically important for high-tech, renewable energy and defense industries. However, rare earth minerals (REMs) are stable compounds, requiring aggressive conditions to decompose them for their extraction and use. One exception is the ion-adsorption clays (IACs) that are mined in South China. They were formed in nature via the adsorption of the REE ions on clay minerals; therefore, they can be readily extracted into solution under mild conditions using the ion-exchange leaching process using (NH4)2SO4 as lixiviant. It also happens that IACs are the largest source of the heavy rare earth elements (HREEs) that are critical, especially for the defense industry. At present, more than 80% of the HREEs are produced commercially from the IACs mined in Southeast Asia. The objective of the present research was to study the fundamental mechanisms involved in the formation and processing of IACs using the ion-change leaching process. The first part of the project was the synthesis of IACs by contacting kaolinite samples with known concentrations of rare earth chloride (REECl3) solutions at different pHs and analyzing the synthetic IACs for XPS studies. It was found that the REE adsorption on kaolinite stays constant in acidic pHs. At pH 7 and above, adsorption density increases sharply, possibly due to the formation of REE(OH)3 and/or REE(OOH). The IACs formed under these conditions responded well to the ion-exchange leaching process by reducing the pH to below 7. In the second part of the study, the effect of iron (Fe3+) species co-adsorbing with REEs on the kaolinite surface was studied. Unlike the colloidal phases of IACs formed at pH > 7, the synthetic IACs formed in the presence of iron did not respond to the ion-exchange leaching process using (NH4)2SO4 as lixiviant. This problem has been solved by subjecting the synthetic IACs to a reducing condition to convert the Fe3+ to soluble Fe2+ species at pH < 7. The driving force for the standard exchange leaching process is the large differences between the hydration enthalpies of the Ln3+ ions that are in the range of -3,400 kJ/mole and that of the NH4+ ions (-320 kJ/mole). In the present work, alkylammonium ions (CnH2nNH4+) of varying chain lengths were used as novel lixiviants and obtained excellent results. Since these are surface active species, their concentrations in the vicinity of the clay minerals that are negatively charged would be substantially higher than in the bulk. As a result, it was possible to achieve the same level of leaching efficiencies as obtained using ammonium sulfate at approximately ten times lower reagent dosages. One of the problems associated with extracting REEs from coal-based clays is that the REE concentrations are typically in the range of 300 to 600 ppm, which makes it difficult to extract the critical materials economically using ion-exchange leaching and other processes. As a means to overcome this issue, the REE-bearing particles, including IACs and REMs, were liberated by blunging and subsequently upgraded using the hydrophobic-hydrophilic separation (HHS) process. The results showed that blunging outperformed grinding in liberating the REE-bearing particles from the clayey materials in coal. It was shown that one can improve blunging by increasing the disjoining in the thin liquid films present between clay and other minerals by controlling the double-layer (EDL) forces. These findings should enhance our understanding of the fundamental mechanisms involved in upgrading critical materials and thereby increase the economic viability of REE recovery from coal-based materials.
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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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    Molecular Dynamics Simulation of Forsterite and Magnesite Mechanical Properties: Does Mineral Carbonation Reduce Comminution Energy?
    Talapatra, Akash; Nojabaei, Bahareh (MDPI, 2023-08-09)
    This work compares the mechanical properties of two geomaterials: forsterite and magnesite. Various physical conditions are considered to investigate the evolution of stress–strain relationships for these two polycrystals. A molecular-scale study is performed on three-dimensional models of forsterite and magnesite. Three different temperatures (300 K, 500 K, and 700 K) and strain rates (0.001, 0.01, and 0.05 ps−1) are considered to initiate deformation in the polycrystals under tensile and compressive forces. The polycrystalline structures face deformation at lower peaks at high temperatures. The Young’s modulus values of forsterite and magnesite are found to be approximately 154.7451 GPa and 92.84 GPa under tensile forces and these values are found to be around 120.457 GPa (forsterite) and 77.04 GPa (magnesite) for compressive forces. Increasing temperature reduces the maximum strength of the polycrystalline structures, but forsterite shows higher ductility compared to magnesite. Strain rate sensitivity and the effect of grain size are also studied. The yield strengths of the forsterite and magnesite drop by 7.89% and 9.09% when the grain size is reduced by 20% and 15%, respectively. This study also focuses on the changes in elastic properties for different pressures and temperatures. In addition, from the radial distribution function (RDF) results, it was observed that the peak intensity of pairwise interaction of Si–O is higher than that of Mg–O. Finally, it is found that the formation of magnesite, which is the product of mineral carbonation of forsterite, is favorable in terms of mechanical properties for the comminution process.
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    Molecular Dynamics Simulation of Forsterite and Magnesite Mechanical Properties: Effect of Carbonation on Comminution Energy
    Talapatra, Akash (Virginia Tech, 2024-10-09)
    Mineral carbonation contributes to CO2 reduction, and it may also reduce the cost of mineral processing by improving the mechanical properties of rock/ore. Here, we study and compare the mechanical properties of two minerals, forsterite (Mg2SiO4) and magnesite (MgCO3) using molecular dynamics (MD) simulation. The goal is to understand whether carbonation results in hardness reduction of rock and subsequently comminution energy during the crushing and processing of the ore. We investigated how these materials respond to different physical conditions, such as temperature and strain rate, to understand their behavior under stress. By examining the molecular structure of forsterite and magnesite at temperatures ranging from 300K to 700K and strain rates of 0.001, 0.01, and 0.05ps-1, we observed how they deform when subjected to both tensile and compressive forces. This study has shown that at higher temperatures, both forsterite and magnesite monocrystals undergo deformation more easily under pressure. Forsterite is found relatively hard and shows maximum strength before deformation compared to magnesite. The stiffness of magnesite decreases at elevated temperatures which reduces the energy requirement for the comminution process. We also looked at how pressure and temperature changes affected their elasticity. Ultimately, our findings suggest that magnesite may be more suitable for processes like comminution, which involves breaking down materials, compared to forsterite. This insight into the effects of mineral carbonation on geomaterials contributes to our understanding of how these minerals behave under different conditions and could have implications for various industries.
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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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    Numerical Simulation of High Expansion Foam Into Conduits and Mine Openings
    Barros Daza, Manuel Julian (Virginia Tech, 2018-06-19)
    High expansion foam (Hi-Ex) is a firefighting technology that has been widely used for fire suppression in underground locations. Hi-ex foam can be applied remotely through boreholes from the surface reducing firefighter exposure to fires. Despite the experimental studies that have been carried out there are still some uncertainties about foam behavior in underground locations. For this reason, the main objective of this thesis was to estimate Hi-Ex foam flow behavior in different underground configurations using computational fluid dynamics (CFD) simulations. An experimental apparatus was built to study the foam rheology in order to determine the rheological model parameters to simulate foam as a continuous Non-Newtonian fluid. Furthermore, numerical and experimental results of Hi-Ex foam flowing in a pipe were compared with the objective of validating numerical results. Results of this study show that Hi-Ex foam with an expansion ratio between 1:250 and 1:1280 behaves as a shear thinning fluid represented by the power law model. Numerical simulations results were between 0.06% and 14% of experimental results for Reynolds numbers between 200 and 1700. Finally, numerical simulations of Hi-Ex foam in different mine entry slopes were carried out and compared with qualitative results of prior field work. This work generates some of the necessary numerical parameters for the simulation of Hi-Ex foam flow in mines. Furthermore, results of this work and the methodology used can allow for improved predictions of foam flow in in underground mine fires, while improving safety for mine workers
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    Optimization of Quarry Operations and Maintenance Schedules
    George, Brennan Kelly (Virginia Tech, 2023-06-28)
    New technologies such as the Internet of Things are providing newer insights into the health, performance, and utilization of mining equipment through the collection of real-time data with sensors. In this study, data is utilized from multiple quarries and a surface coal mine collected through the software CAT Productivity and CAT MineStar Edge to analyze the performance of loaders and haul trucks. This data consists of performance metrics such as truck and loader cycle time, payload per loader bucket, total truck payload, truck plan distance, and loader dipper count. This study uses data analysis and machine learning techniques to analyze the performance of loaders and haul trucks in the mining operations used in the scope of this study. Data analysis of cycle time and payload show promising results such that there is an optimum cycle time for multiple loaders between 30-40 seconds that show a high average production. Furthermore, the distribution of production variables is analyzed across each set of loaders to compare the performance. The Caterpillar 992K machine in the rock quarries data set seemed to be the highest-yielding machine while the two Caterpillar 993K machines performed similarly in the surface coal mine data set. The Neural Network algorithm created a model that predicted the loader from the performance metrics with 90.26% accuracy using the CAT Productivity data set, while the Random Forest algorithm achieved a 79.82% accuracy using the CAT MineStar Edge data set. Furthermore, the use of preventative maintenance is investigated in the process of replacing Ground Engaging Tools on loader buckets to determine if maintenance was effective. Additionally, data analysis is applied to Ground Engagement Tools maintenance to identify key preventative maintenance schedules to minimize production impact from equipment downtime and unnecessary maintenance. Production efficiency is compared before and after maintenance on Ground Engaging Tools and concluded that there was no material change in the average production of the mine based on that analysis. The insights gained from this study can inform future research and decision-making and improve operational efficiency.
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    Phase Behavior and Composition Distribution of Multiphase Hydrocarbon Binary Mixtures in Heterogeneous Nanopores: A Molecular Dynamics Simulation Study
    de Andrade, Deraldo de Carvalho Jacobina; Nojabaei, Bahareh (MDPI, 2021-09-18)
    In this study, molecular dynamics (MD) simulation is used to investigate the phase behavior and composition distribution of an ethane/heptane binary mixture in heterogeneous oil-wet graphite nanopores with pore size distribution. The pore network system consists of two different setups of connected bulk and a 5-nm pore in the middle; and the bulk connected to 5-nm and 2-nm pores. Our results show that nanopore confinement influences the phase equilibrium of the multicomponent hydrocarbon mixtures and this effect is stronger for smaller pores. We recognized multiple adsorbed layers of hydrocarbon molecules near the pore surface. However, for smaller pores, adsorption is dominant so that, for the 2-nm pore, most of the hydrocarbon molecules are in the adsorbed phase. The MD simulation results revealed that the overall composition of the hydrocarbon mixture is a function of pore size. This has major implications for macro-scale unconventional reservoir simulation, as it suggests that heterogenous shale nanopores would host fluids with different compositions depending on the pore size. The results of this paper suggest that modifications should be made to the calculation of overall composition of reservoir fluids in shale nanopores, as using only one overall composition for the entire heterogenous reservoir can result in significant error in recovery estimations.
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    Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction
    Shahdi, Arya (Virginia Tech, 2021-06-03)
    In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree-based models (e.g., XGBoost and Random Forest). We found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model.
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    Rare Earth Extraction from Clayey Waste Materials by Alkali Pretreatment
    Liu, Wei (Virginia Tech, 2023-04-12)
    The increasing demand for rare earth elements (REEs) and the depletion of conventional rare earth deposits have enabled secondary REE resources to be promising feedstocks for REEs. Studies have been conducted in developing technologies that can physically preconcentrate and/or chemically extract REEs from low-REE-grade clayey waste materials (e.g., coal-based clays). However, the low REE grades and poor leachability of REE-bearing species still make the recovery of REEs from coal-based clays challenging. The primary objective of this study is to develop leaching technologies that can extract REEs from clayey waste materials under mild conditions (<100 oC). In the first part of this work, a novel leaching process consisting of NaOH pretreatment followed by ammonium sulfate leaching has been proposed to recover REEs from monazite, which served as a proxy for the rare earth phosphates identified in coal-based clays. In this process, monazite can be decomposed at 80 oC. The following ammonium sulfate leaching was conducted under less aggressive conditions (i.e., pH 4 and room temperature) to recover REEs. After releasing RE3+ ions from RE(OH)3(s) by acid, the role of ammonium sulfate in the leaching process may be explained by an ion exchange mechanism. Sulfate ions also benefit the leaching process by complexing with RE3+ ions. The influences of temperature and particle size on the leaching kinetics of REEs from the NaOH-treated monazite by ammonium sulfate were also investigated based on the shrinking core model. It was found that the leaching process is controlled by a chemical reaction with an activation energy of 61.28 kJ/mol. Besides ammonium sulfate, ammonium formate is a promising lixiviant for NaOH-treated monazite. However, other carboxylate ligands tested were inefficient at room temperature, mainly due to the slow dissolution kinetics of RE(OH)3(s) resulting from the passivation of the binuclear surface complexes. Subsequently, the feasibility of decomposing rare earth phosphates by NaOH in the presence of ethylenediaminetetraacetic acid (EDTA) was explored by constructing the stability diagrams for La-, Nd-, and Y-PO4-H2O systems, respectively. The simulation results were validated using three coal-based clay samples. The leaching results of both HCl and ammonium sulfate indicated that the pretreatment conducted by combining EDTA with dilute NaOH solutions (5-10%) could significantly enhance the REE leachability of the clay samples, with the light REEs (LREEs) being preferentially extracted compared to heavy REEs (HREEs). Under optimal conditions, the co-extraction of Al and Si can be significantly reduced. Besides liberated phosphate mineral particles, X-Ray photoelectron spectroscopy (XPS) analyses conducted on the synthetic ion adsorption clay samples revealed that phosphate could also passivate the REEs adsorbing on the surface of clay minerals in the form of “clay-RE-PO4”. This finding may partially explain the poor ion exchangeability of REEs in coal-based clays. After subjecting to the proposed NaOH pretreatment technique, the passivated REEs on the surface of clay can be effectively removed. Lastly, the possibility of preconcentrating REEs from a kaolinite flotation reject material was explored by froth flotation and the hydrophobic-hydrophilic separation (HHS). A final concentrate assaying 10,765 ppm of REEs and 71% of recovery was obtained by the HHS process, which was superior to flotation in dealing with ultrafine particles. The microscopic characterization of the concentrate revealed that rare earth phosphates were the major REE-bearing species. The leaching results showed that the proposed NaOH pretreatment followed by ammonium sulfate leaching was also an effective method to recover REEs from the upgraded clayey waste material under mild conditions (<100 oC).
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    A Review of Gas Injection in Shale Reservoirs: Enhanced Oil/Gas Recovery Approaches and Greenhouse Gas Control
    Du, Fengshuang; Nojabaei, Bahareh (MDPI, 2019-06-19)
    Shale oil and gas resources contribute significantly to the energy production in the U.S. Greenhouse gas emissions come from combustion of fossil fuels from potential sources of power plants, oil refineries, and flaring or venting of produced gas (primarily methane) in oilfields. Economic utilization of greenhouse gases in shale reservoirs not only increases oil or gas recovery, but also contributes to CO2 sequestration. In this paper, the feasibility and efficiency of gas injection approaches, including huff-n-puff injection and gas flooding in shale oil/gas/condensate reservoirs are discussed based on the results of in-situ pilots, and experimental and simulation studies. In each section, one type of shale reservoir is discussed, with the following aspects covered: (1) Experimental and simulation results for different gas injection approaches; (2) mechanisms of different gas injection approaches; and (3) field pilots for gas injection enhanced oil recovery (EOR) and enhanced gas recovery (EGR). Based on the experimental and simulation studies, as well as some successful field trials, gas injection is deemed as a potential approach for EOR and EGR in shale reservoirs. The enhanced recovery factor varies for different experiments with different rock/fluid properties or models incorporating different effects and shale complexities. Based on the simulation studies and successful field pilots, CO2 could be successfully captured in shale gas reservoirs through gas injection and huff-n-puff regimes. The status of flaring gas emissions in oilfields and the outlook of economic utilization of greenhouse gases for enhanced oil or gas recovery and CO2 storage were given in the last section. The storage capacity varies in different simulation studies and is associated with well design, gas injection scheme and operation parameters, gas adsorption, molecular diffusion, and the modelling approaches.