Recent Submissions

  • Transport Properties of Oil-Co2 Mixtures in Nanopores: Physics and Machine Learning Models

    Zhang, Hongwei; Wang, Xin; Kang, Qinjun; Yan, Bicheng; Sun, Shuyu; Qiao, Rui (Elsevier BV, 2023-09-22) [Preprint]
    Fundamental understanding and quantitative models of the transport properties of oil-CO2 mixtures in nanopores are indispensable for physics-based models of CO2-enhanced oil recovery in unconventional oil reservoirs. This study determines the Maxwell-Stefan (M-S) diffusivities of CO2-decane (1: CO2; 2: decane /C10) mixtures in calcite nanopore with compositions relevant to CO2 Huff-n-Puff by molecular dynamics (MD) simulations. In the compositional space explored, D12 characterizing CO2-C10 interactions is relatively insensitive to composition, in contrast to that of bulk mixtures with similar compositions. D1,s characterizing CO2-wall interactions increases sharply with CO2 loading in the nanopore. In contrast, D2,s characterizing C10-wall interactions shows a nonmonotonic dependence on C10 loading. In addition, surprisingly, D2,s is negative, opposite to the expectations for dense fluid mixtures or pure decane confined in nanopores. These features of the M-S diffusivities can ultimately be traced to the fact that CO2 molecules adsorb far more strongly on pore walls than the C10 molecules, which leads to significantly heterogeneous distribution of CO2 and C10 in the nanopore and a low mobility of the adsorbed CO2 molecules. As MD simulations are computationally expensive, a non-parametric machine learning technique called the multitask Gaussian process regression method, is used to build a surrogate model to predict M-S diffusivities based on limited MD data. The surrogate model performs well in the compositional space it was trained with a relative root mean square error less than 10%.
  • Holocene Deformations at the Po Plain–Southern Alps Transition (Lake Maggiore, Italy): Inferences on Glacially vs. Tectonic-Induced Origin

    Menegoni, Niccolo; Maino, Matteo; Toscani, Giovanni; Mordeglia, Lucia Isabella; Valle, Gianfranco; Perotti, Cesare (Geosciences, MDPI AG, 2023-09-21) [Article]
    The investigation of deformations in Quaternary deposits holds primary importance in understanding recent geological history and natural hazards in highly populated areas, such as the Po Plain. While civil excavations and trenches possess the potential to be pivotal in identifying and characterizing these deformations, they often remain underused due to the stringent regulation framework and timetables governing civil construction works. In this study, we demonstrate how digital photogrammetry and digital outcrop modelling (DOM) are useful techniques for obtaining a permanent digital representation of a trench situated in Castelletto Ticino (Po Plain–Southern Alps transition). This trench exhibits Holocene deformational structures: (i) an overall tilting of sedimentary deposits towards the SW; (ii) folds with a NE–SW trend; (iii) slumping and other soft-sediment deformations structures; and (iv) reverse faults with NE–SW and NW–SE directions. Using radiocarbon and archeological dating, we are able to confidently constrain the age of these deformations to between 8760 and 400 years BC, suggesting recent tectonic activity related to buried thrust faults.
  • Capillary-Sealing Efficiency of Mica-Proxy Caprock for CO2/H2 Geologic Storage in the Presence of Organic Acids and Nanofluids

    Al-Anazi, Amer; Ali, Muhammad; Mowafi, Mahmoud; Bawazeer, Saleh; Kaidar, Ziyad K.; Hoteit, Hussein (SPE Journal, Society of Petroleum Engineers (SPE), 2023-09-21) [Article]
    Toward a diversified low-carbon future, the geological storage of carbon dioxide (CO2) and hydrogen (H2) is regarded as a key enabler for an industrial-scale implementation. However, many geological formations, such as depleted oil and gas reservoirs, can contain inherent traces of organic molecules that dramatically affect their storage capacities and caprock sealing efficiency. Hence, using the right analysis to accurately determine the caprock sealing efficiency and storage capacity in the presence of organics is crucial for a secure and safe storage process. This study analyzed the sealing potential of a proxy caprock (mica) by calculating the capillary entry pressure and static column height of CO2 and H2 using previously published contact angle measurements. In addition, the effects of key parameters such as pressure (up to 25 MPa), temperature (308 K, 323 K, and 343 K), and pore throat size (r = 5 nm and 10 nm) were demonstrated, along with those of organic acids (lignoceric acid C24, stearic acid C18, lauric acid C12, and hexanoic acid C6) and alumina nanofluids, on the wettability, capillary sealing efficiency, and static column height of the gas. The results indicated that the sealing efficiency and storage capacity for CO2 and H2 decrease with the increase in pressure and surface concentration of organic acid but increase with the increase in temperature. The analysis demonstrated a theoretical inverse relationship between the capillary entry pressure and the pore throat radius. Thus, the smaller the pore size, the more suitable the conditions for sealing and storage capacity. Furthermore, the wettability and sealing efficiency of the organic-aged mica/CO2 system were improved by the addition of nanoalumina, with an optimal nanofluid concentration of 0.25 wt%. In a nutshell, this work has provided a detailed theoretical workflow for assessing the influence of various parameters on the wettability, sealing efficiency, and storage capacity of mica substrates (as a proxy caprock) for the safe and secure geological storage of H2 and CO2.
  • Coupled fluid flow, solute transport and dissolution processes in discrete fracture networks: an advanced Discontinuous Galerkin model

    Tabrizinejadas, Sara; Younes, Anis; Hoteit, Hussein; Carrayrou, Jerome; Fahs, Marwan (Advances in Water Resources, Elsevier BV, 2023-09-17) [Article]
    Modeling dissolution processes in discrete fracture networks (DFNs) is a challenging task. Challenges are related to the highly nonlinear coupling between flow, mass transport, and reactive processes associated with fracture aperture evolution by dissolution. Further, advection-dominated transport due to fast fluid flow in fractures renders the problem more complex from a computational point of view, as traditional numerical methods may introduce unphysical oscillations or excessive numerical diffusion. The Discontinuous Galerkin (DG) method is known to be suitable for the simulation of advection-dominated transport. In this work, an advanced DG model is developed to model transport with dissolution in DFNs. We propose an upwind formulation to deal with the upstream concentration at the intersection of several fractures. The upstream concentration at an intersection node is calculated based on the average nodal concentrations of all the fractures having an inflow at that node, weighted by the volumetric fluxes of these fractures. The dispersion term is discretized with the Mixed Finite Element (MFE) method, which ensures the continuity of the dispersive flux at the intersection of fractures with different apertures. The obtained nonlinear coupled flow-transport-dissolution equations are discretized in time with a high-order scheme via the method of lines (MOL). Numerical examples and comparisons with standard finite element (FE) and finite volume (FV) solutions are performed to investigate the correctness and efficiency of the developed model. Results show that the new DG-DFN model avoids unphysical oscillations encountered with the standard FE method and strongly reduces the numerical diffusion observed with the upwind FV scheme. The DG-DFN model is then used to investigate the effect of the dissolution rate on the flow, transport, and aperture evolution processes for a single fracture and for a DFN. A quasi-linear evolution of the fracture aperture is observed for low dissolution rates. For high dissolution rates, a funnel-shaped enlargement is observed with a significant widening for the fractures near the inlet and minor effects for those away from the injection location.
  • Sands Subjected to Repetitive Loading Cycles and Associated Granular Degradation

    Park, Junghee; Santamarina, Carlos (Journal of Geotechnical and Geoenvironmental Engineering, American Society of Civil Engineers (ASCE), 2023-09-14) [Article]
    This study examines the load-deformation response of sands subjected to high- and low-stress cycles, i.e., both ends of the Wöhler’s fatigue curve. At high peak cyclic stress σf, the terminal void ratio decreases with σf due to crushing-dependent densification, and it can be smaller than emin when the peak stress approaches the yield stress σf→σy. When σf≪σy, the soil retains memory of the initial fabric even after a very large number of cycles, and the terminal void ratio correlates with the initial void ratio eo. Data show that the maximum change in relative density leads to simple strategies to estimate the maximum settlement for first-order engineering analyses. In agreement with Wöhler’s fatigue, tipping points in void ratio and stiffness trends occur at a small number of high-stress cycles or after a large number of small-stress cycles. During repetitive loading, sands stiffen with the number of cycles to reflect increased interparticle coordination following crushing, as well as contact flattening due to asperity breakage and fretting. The strong correlation between the resilient modulus Mr and the maximum shear modulus Gmax suggests the possible application of geophysical methods based on shear wave propagation to monitor geosystems subjected to repetitive loading cycles.
  • Rapid Inference of Reservoir Permeability from Inversion of Traveltime Data Under a Fast Marching Method-Based Deep Learning Framework

    Li, Chen; Yan, Bicheng; Kou, Rui; Gao, Sunhua (SPE Journal, Society of Petroleum Engineers (SPE), 2023-09-14) [Article]
    The fast marching method (FMM) is a highly efficient numerical algorithm used to solve the Eikonal equation. It calculates traveltime from the source point to different spatial locations and provides a geometric description of the advancing front in anisotropic and heterogeneous media. As the Eikonal solution, the diffusive time of flight (DTOF) can be used to formulate an asymptotic approximation to the pressure diffusivity equation to describe transient flow behavior in subsurface porous media. For the infinite-acting flow that occurs in porous media with smoothly varying heterogeneity, traveltime of the pressure front from the active production or injection well to the observation well can be directly estimated from the DTOF using the concept of radius (or depth) of investigation (ROI or DOI), which is defined as the moment when a maximum magnitude of the partial derivative of pressure to time occurs. Based on the ROI or DOI definition, we propose a deep neural network called the inversion neural network (INN) to inversely estimate heterogeneous reservoir permeability by inverting the traveltime data. The INN is trained by traveltime data created for a large data set of distinct permeability fields from FMM simulations, which can be two orders of magnitude faster than conventional reservoir simulators. A convolutional neural network (CNN), the U-Net architecture, is incorporated into the INN, which establishes a nonlinear mapping between the heterogeneous permeability fields and the traveltime data collected at sparse observation wells. The loss function used for the INN is defined as the root mean square error (RMSE) between the logarithm of the predicted permeability and the logarithm of the true permeability. The performance of the INN is tested on reservoir models with both smoothly varying heterogeneity and high-contrast media properties. For the 2D smoothly varying heterogeneous models with a grid size of 49×49, the permeability predicted by the INN has an average estimation error of 8.73% when a set of 7×7 uniformly distributed observation wells is used to collect “observational” traveltime data from the FMM simulation. For models with the same grid size and observation well density but with high-contrast media properties, the INN can still capture the general heterogeneity distribution, although with reduced prediction accuracy. Using a graphics processing unit (GPU) for training and prediction allows the entire inverse modeling process for a 2D 49×49 reservoir model to be completed within 7 minutes.
  • Revised Soil Classification System: Implementation and Engineering Implications

    Castro, Gloria M.; Park, Junghee; Santamarina, Carlos (Journal of Geotechnical and Geoenvironmental Engineering, American Society of Civil Engineers (ASCE), 2023-09-13) [Article]
    Soil classification systems help geotechnical engineers anticipate soil properties and provide early guidance for engineering analyses. Current soil classification systems recognize the central role of particle size and inherent differences between coarse- and fine-grained fractions. However, they adopt fixed classification boundaries irrespective of a broad range of fines plasticity and particle shape, disregard the distinct fines thresholds for mechanical and hydraulic properties, and overlook pore-fluid chemistry effects on fines behavior. The Revised Soil Classification System (RSCS) addresses these limitations and benefits from published data and physical insights gained during the last century. By comparison, the classification boundaries in the prevailing Unified Soil Classification System (USCS) resemble those in the RSCS only for the case of angular sands and gravels mixed with low-plasticity fines; in all other cases, extensive data sets corroborate the transition thresholds adopted in the RSCS. The complete logic tree for the RSCS facilitates its implementation; it is available as user-friendly Excel macro and a mobile application that automatically produce the soil-specific classification charts and show the soil classification in terms of the controlling fraction for both mechanical and hydraulic properties. Multiple studies have demonstrated the predictive power of the RSCS in terms of soil properties (e.g., compressibility, strength, hydraulic conductivity, and capillarity), soil phenomena (e.g., fines migration and bioactivity), and the preliminary selection of geotechnical solutions (e.g., soil improvement).
  • Doublet Huff and Puff (Dhp): A New Technology Towards Optimum Scco2 Sequestration with Stable Geothermal Recovery

    Gudala, Manojkumar; Yan, Bicheng; Tariq, Zeeshan; Sun, Shuyu (Elsevier BV, 2023-09-11) [Preprint]
    The heat energy extracted from the geothermal reservoirs is clean and plays an important role in decarbonizing the energy sector. Carbon emissions increase in day-to-day operations due to the utilization of hydrocarbons, which contributes to global warming. Therefore, it is crucial to capture and storeSCCO2 while developing clean energy technologies. In this study, we develop a new technology that stores SCCO2 while extracting clean energy from geothermal reservoirs. Our goal is to achieve sustainable thermal recovery and maximize SCCO2 storage capabilities in geothermal reservoirs. To investigate the thermal recovery and SCCO2 storage behavior of geothermal reservoirs, we use a thermo-hydro (two-phase) mechanical (THM) model. This technology is adapted from conventional Huff-Puff technology used in the hydrocarbon industry and applied using well pairs with different injection and perforation operating cycles. We also compared the numerical results with the CO2 plume geothermal (CPG) models. The levelized cost of energy (LCOE) analysis is conducted and compared with the CPG models. The numerical results show that the reduction in production temperature is less than 10 % of the original temperature (base case), the injected SCCO2 accumulates at the top of the reservoir, and the cold front progresses in the vicinity of wells over time. We also investigate the sensitivity of the rock and operating parameters on the heat power and the amount of SCCO2 stored. The implementation of DHP technology is more economical than CPG in geothermal reservoirs (LCOEDHP

    Benitez, Marcelo (2023-08-29) [Dissertation]
    Advisor: Hoteit, Hussein
    Committee members: Mishra, Himanshu; Finkbeiner, Thomas; Espinoza, Nicolas; Santamarina, Carlos; Liu, Qi
    The energy demand has increased dramatically in the last century, and so to have global CO2 emissions. Two critical challenges for the geo-energy sector are to develop different approaches for harvesting energy and to actively decrease atmospheric CO2 emissions. Addressing these challenges requires efficient, sustainable, and affordable technical solutions. Complex fluids are ubiquitous and offer great potential for geo-engineering applications such as the development of geo-energy, enhanced oil recovery and CO2 geological sequestration and utilization. This thesis will present new results on interfacial phenomena in CO2-fluid-mineral systems, including interfacial tension hysteresis, the effects of surface-active components on interfacial tension (surfactants, nanoparticles, organo-bentonites and asphaltenes), and the interfacial pinning of immiscible fluids on substrates. Pore-scale phenomena come together in the study of the physical properties of CO2 and its implication for both storage and assisted gravity oil drainage. Finally, we provide a better understanding of the interfacial phenomena of complex fluids and their interactions within porous media that can lead to efficient and sustainable geo-energy systems.
  • A single-molecule study on polymer fluid dynamics in porous media.

    Sugar, Antonia; Serag, Maged F.; Buttner, Ulrich; Habuchi, Satoshi; Hoteit, Hussein (Lab on a chip, Royal Society of Chemistry (RSC), 2023-08-23) [Article]
    Understanding the dynamic behavior of polymeric fluids in porous media is essential for vast geoscience applications, particularly enhanced oil recovery and polymer-enhanced soil washing, to clean up soil contamination. During the past decades, the behavior of polymeric fluids in microscopic space has only been investigated using ensemble-averaged experimental methods in which a bulk phase behavior of the fluids characterizes flow mechanisms. Multiple flow mechanisms have been proposed based on ensemble-averaged data; however, microscale characterization of the interactions between polymers and solid surfaces and the mechanisms governing polymer retention and permeability reduction as well as the reversibility of polymer retention are lacking, resulting in a limited understanding of the flow mechanisms. Here we report direct visualization and multi-scale characterization of the dynamic behavior of polymer molecules in a representative porous medium by integrating microfluidics with single-molecule imaging. We demonstrate that the polymers' adsorption, entrapment and hydrodynamic retention contribute to their overall retention in porous media. Our study illustrates how microfluidics can help in understanding the dynamic behavior of polymers, their interactions with the solid/fluid interface and their effects on flow properties. Additionally, it demonstrates the role of microfluidic platforms in providing a more representative and accurate model for polymer retention and permeability reduction in porous media. The obtained insights encourage the development of improved models that better capture the behavior of complex fluids in confined environments and have significant implications for a wide range of applications in geoscience, materials science, and rheology.
  • Morphological evidence of the extension of the Zabargad Transform Fault Zone to the Saudi Arabian Red Sea margin

    Petrovic, Alexander; Panara, Yuri; Vahrenkamp, Volker (Journal of the Geological Society, Geological Society of London, 2023-08-22) [Article]
    Fault locations and orientation of the Zabargad Transform Fault Zone, also called the Zabargad Fracture Zone (ZFZ) have, so far, only been delineated by satellite-based geophysical data, causing intense debate over the last decades. Newly recognized geomorphological features identified in bathymetry and lidar data from the NE Red Sea margin present the first ground evidence for the northern extent of the ZFZ. The features are aligned over 84 km starting from the Mabahiss Deep, near the spreading axis, and continue to the shallow Saudi Arabian shelf, along the northern termination of the Al Wajh carbonate platform. Analysis of the seafloor morphology revealed three geomorphic terrains: (1) a deep incised canyon feeding into the Mabahiss Deep, which is characterized by dozens of amphitheatre-shaped scarps, (2) a 22 km-wide head-scarp that follows the Al Wajh platform edge, (3) and multiple fault scars and graben-like structures on the shallow shelf. We interpret these morphological features as deformation indicators in association with the deformation processes in the ZFZ, and postulate that they represent the northern end of the ZFZ. In addition, the fault zone delineates the northwest margin of the Al Wajh carbonate platform, and most likely continues to shape it. This paper gives new insights in the interaction between fracture zones and continental margins and their role in the seafloor morphogenesis.
  • Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization

    Othman, Amro; Tariq, Zeeshan; Aljawad, Murtada Saleh; Yan, Bicheng; Kamal, Muhammad Shahzad (Energy & Fuels, American Chemical Society (ACS), 2023-08-21) [Article]
    Optimizing fracture fluid viscosity in a high salinity medium (i.e., seawater and produced water) is challenging. Hence, we conducted numerous rheology experiments utilizing an Anton Paar rheometer to generate viscosity data. We have experimented with different types and concentrations of polymers, crosslinkers, and chelating agents in different water salinities at different shear rates, temperatures, pressures, and mixing orders. After data cleaning, the study generated 645 data from 86 experiments, which were fed to the machine learning (ML) models such as fully connected neural networks (FCNN), gradient boosting (GB), adaptive gradient boosting (AdaBoost), extreme GB (XGB), random forest (RF), and decision trees (DT). The hyper-parameters of these models were optimized using a grid search optimization approach during the training phase. Additionally, the K-fold cross-validation technique was utilized to enhance the models’ performance of the ML. The performance of the ML models was assessed through various assessment tests, such as root mean square error (RMSE), coefficient of determination (R2), average absolute percentage error (AAPE), and cross-plots. The outcomes of the predictions indicated that the feedforward neural network (FCNN) outperformed the DT, RF, GB, AdaBoost, and XGB models. These techniques yielded remarkably low error rates. With the optimal settings, the fracturing fluid viscosity was predicted with 95% accuracy. In addition, the fracturing fluid viscosity was maximized using the particle swarm optimization algorithm by optimizing the input parameters where the FCNN model was trained. The proposed methodology of predicting the fracturing fluid viscosity could minimize the experimental cost of measuring fracturing fluid rheology.
  • Evaluation of geological CO2 storage potential in Saudi Arabian sedimentary basins

    Ye, Jing; Afifi, Abdulkader M.; Rowaihy, Feras H.; Baby, Guillaume Jean Baptiste; De Santiago, Arlette; Tasianas, Alexandros; Hamieh, Ali Imad Ali; Khodayeva, Aytaj; Al-Juaied, Mohammed; Meckel, Timothy A.; Hoteit, Hussein (Earth-Science Reviews, Elsevier BV, 2023-08-16) [Article]
    Carbon capture and storage (CCS) technologies are needed as a crucial technology for Saudi Arabia to reach its net-zero goal by 2060. This study represents the first comprehensive evaluation of geological CO2 storage capacities in the sedimentary basins of Saudi Arabia. Our study relied on collecting and analyzing hundreds of data sets from public domains, which were carefully selected based on their quality and relevance to ensure reliability. We evaluated the suitability and storage capacity of 17 basins and sub-basins throughout the country for CO2 storage in deep saline aquifers as well as future depleted oil and gas reservoirs using the CO2-SCREEN tool. Our evaluation shows that the most suitable basins are located in the eastern part of the country, including the Eastern Arabian Basin and the Interior Homocline-Central Arch. On the other hand, Western Saudi Arabia is characterized by less favorable basins, except for the three moderately suitable onshore basins, namely the Umm Luj, Yanbu, and Jeddah basins. Uncertainties were considered by performing Monte Carlo simulations. At the 50th percentile uncertainty, the estimated total effective storage capacities in deep saline aquifers, future depleted oil reservoirs, and non-associated gas reservoirs are ∼432, ∼5, and ∼ 9 gigatons (Gt), respectively. Most of the country's storage capacity is located in the eastern region, displaying an uneven distribution of storage resources nationally. For full transparency, we share all the calculation sheets utilized. The methodology adopted for estimating the potential capacity aligns with the effective storage capacities defined by the Carbon Sequestration Leadership Forum (CSLF). The capacity estimates account for technical, geological, and engineering constraints. Nevertheless, it is crucial to note that the practical storage capacity estimate requires the incorporation of additional societal, economic, and regulatory factors, which were not taken into consideration in this study. Consequently, there is a pressing need for additional research, particularly prospect-level evaluations entailing drilling, testing evaluation, and monitoring wells. These comprehensive investigations will significantly contribute to our comprehension of reservoir and seal characteristics, assess injectivity performance, and provide profound insights into CO2 behavior, effectively mitigating uncertainties. The storage capacity estimates presented in this study furnish crucial information for policymakers and industry leaders engaged in addressing carbon emissions in Saudi Arabia.
  • Technoeconomic assessment of hydrogen production from natural gas pyrolysis in molten bubble column reactors

    Angikath Shamsudheen, Fabiyan; Abdulrahman, Faseeh; Yousry, Ahmed; Das, Ratul; Saxena, Saumitra; Behar, Omar; Alhamed, Haytham; Altmann, Thomas; Dally, Bassam; Sarathy, Mani (International Journal of Hydrogen Energy, Elsevier BV, 2023-08-12) [Article]
    Natural gas pyrolysis to produce carbon and hydrogen, known as “turquoise hydrogen, using molten metals and salts is a promising route to clean hydrogen production. Methane pyrolysis produces significantly less or near-zero CO2. Additionally, the solid carbon can be separated and sold as a valuable co-product, making industrial-scale production of hydrogen via pyrolysis economically attractive. To better understand the potential of turquoise hydrogen, this study presents a comparative techno-economic assessment of molten media pyrolysis processes in bubble column reactors using Ni0.27Bi0.73, Ga, and KCl–MnCl2 (33:67). The study evaluates techno-economic assessment with a clear understanding of kinetics of natural gas pyrolysis and reactor process modeling. According to the modeling results, Ga had the lowest reactor cost and total bare erected cost. Nevertheless, the pyrolysis process that used inexpensive KCl–MnCl2 molten salt was more economically advantageous. Due to uncertainties in the density separation of solid carbon in the molten salt at temperatures above 1000 °C, Ni0.27Bi0.73 was selected as the promising molten medium for the long term. Sensitivity analyses were carried out to assess the impact of the costs of natural gas, hydrogen, and electricity on the industrial concept. In regions where gas is produced, such as Saudi Arabia, mature molten pyrolysis plants that use grid electricity at a rate of $48/MWh and solar photovoltaic systems with storage at a rate of $24/MWh can be economically feasible at natural gas prices of $132/ton and $198/ton, respectively, even if carbon is not sold. The cost of producing turquoise H2 is comparable to the global average for grey H2 and selling carbon can generate additional revenue or improve the profitability of the plant. The net energy demand of the molten pyrolysis plant is found to be approximately 21% of the energy requirement for current methods of green hydrogen production through PEM (proton exchange membrane) electrolysis.
  • Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems

    Tariq, Zeeshan; Ali, Muhammad; Yekeen, Nurudeen; Baban, Auby; Yan, Bicheng; Sun, Shuyu; Hoteit, Hussein (Fuel, Elsevier BV, 2023-08-04) [Article]
    The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological conditions. The results revealed that the ML models accurately captured the wettability behavior at different geo-storage conditions by yielding less than 5% mean absolute percent error and above 0.95 coefficient of determination values. The partial dependency or sensitivity plots were generated to evaluate the impact of individual features on the trained models. These plots revealed that the models accurately captured the physics behind the problem. Furthermore, a mathematical equation is derived from the trained ML model to predict the wettability behavior without using any ML software. The accuracy of the predictions of the ML model can be beneficial for exactly predicting the H2 geo-storage capacities and assessing of H2 containment security of storage and caprocks for large-scale geo-storage projects.
  • Effect of temperature on convective-reactive transport of CO2 in geological formations

    Tabrizinejadas, Sara; Fahs, Marwan; Hoteit, Hussein; Younes, Anis; Ataie-Ashtiani, Behzad; Simmons, Craig T.; Carrayrou, Jerome (International Journal of Greenhouse Gas Control, Elsevier BV, 2023-07-31) [Article]
    Geological CO2 sequestration (GCS) remains the main promising solution to mitigate global warming. Understating the fate of CO2 behavior is crucial for securing its containment in the reservoir and predicting the impact of dissolved CO2 on the host formation. Most modeling-based studies in the literature investigated the convective-reactive transport of CO2 by assuming isothermal conditions. The effect of temperature on the convective-reactive transport of CO2 is still poorly understood, particularly at the field scale. The objective of this study is to provide an in-depth understanding of CO2-related reactive thermohaline convection (RTHC) processes at field scale. Thus, a new numerical model based on advanced finite element formulations is developed. The new model incorporates an accurate time integration scheme with error control. Numerical experiments confirm high accuracy and efficiency of the newly developed model. The effect of temperature on CO2 transport is investigated for a field case in the Viking reservoir in the North Sea. Results show that including the temperature effect intensifies the fingering processes and, consequently, CO2 dissolution. Neglecting the thermal convection processes and the impact of temperature on the dissolution rate can significantly impact the model predictions. A sensitivity analysis is developed to understand the effect of parameters governing the dissolution rate on the fingering phenomenon and the total CO2 flux.
  • Experimental and Numerical Investigation of Spontaneous Imbibition in Multilayered Porous Systems

    Alaamri, Jamal; Chandra, Viswasanthi; Addassi, Mouadh; Hoteit, Hussein (Energy & Fuels, American Chemical Society (ACS), 2023-07-26) [Article]
    Spontaneous imbibition is a fundamental fluid flow mechanism that plays a significant role in various applications of multiphase fluid flow in porous media, including oil extraction from subsurface reservoirs and underground carbon dioxide storage. Understanding the dynamics of imbibition, driven by capillary forces across multilayered systems, is essential for designing and optimizing field applications. Laboratory experiments with the traditional Amott cell, commonly used to quantify the imbibition performance by immersing an oil-saturated core plug in water and measuring the extracted oil, do not fully replicate actual reservoir conditions. Under reservoir conditions, imbibition occurs within the porous formations across different rock types, while in the Amott cell, imbibition occurs between the rock and the open surrounding water medium. This misrepresentation of field conditions may not replicate the true potential of imbibition. In this study, we use micro-CT and dynamic pore-scale imaging as an alternative approach to visualize and quantify rock-to-rock imbibition within heterogeneous porous media, which cannot be achieved with traditional methods. This work aims at introducing a new concept to evaluate the imbibition mechanism across different porous formations, reflecting the conditions of multilayer systems in the subsurface.
  • The Impact of Secondary Silicate Mineral Precipitation Kinetics on Co2 Mineral Storage

    Addassi, Mouadh; Hoteit, Hussein; Oelkers, Eric (Elsevier BV, 2023-07-24) [Preprint]
    The rates of subsurface mineral carbonation are commonly considered to be limited by the dissolution rates of the silicate minerals originally present in the target reservoir rocks. Nevertheless, the rates of secondary silicate precipitation can influence this rate by changing the fluid phase composition during reactive rock-CO2-water interaction. The degree to which secondary silicate mineral precipitation rates influence the extent and efficiency of mineral carbonation in the subsurface is explored via a suite of geochemical modelling calculations. Calculations were performed using the PHREEQC computer code either by assuming local equilibrium for secondary silicate minerals or calculating their precipitation rates using Transition State Theory-based equations. Calculated carbonation rates of fresh basaltic glass are found to be slower when accounting for the sluggish precipitation rates of secondary silicates, including clay minerals and zeolites. The slower precipitation rates of secondary aluminosilicate minerals, however, may result in less flow path clogging, leading to an overall larger total mineral storage capacity over time. The rates of fresh crystalline basalt carbonation are found to be relatively unchanged as these rates are dominated by the rapid dissolution of highly reactive olivine. In contrast, the sluggish precipitation rates of secondary silicate minerals accelerate significantly the carbonation rate of altered basalts as a larger percentage of the liberated divalent metals are available for carbonate mineral precipitation. Taken together these results illustrate the importance of considering the rates of secondary silicate precipitation rates to accurately predict the rate and extent of subsurface mineral carbonation efforts.
  • Residual trapping of CO2, N2, and a CO2-N2 mixture in Indiana limestone using robust NMR coreflooding: Implications for CO2 geological storage

    Alanazi, Amer; Baban, Auby; Ali, Muhammad; Keshavarz, Alireza; Iglauer, Stefan; Hoteit, Hussein (Fuel, Elsevier BV, 2023-07-19) [Article]
    Carbon capture and sequestration (CCS) in geological formations is a prominent solution for reducing anthropogenic carbon emissions and mitigating climate change. The capillary trapping of CO2 is a primary trapping mechanism governed by the pressure difference between the wetting and nonwetting phases in a porous rock, making the latter a key input parameter for dynamic simulation models. During the CCS operational process, however, the CO2 is prone to contamination by impurities from various sources such as surfaces (e.g., pipelines and tanks) and the subsurface (e.g., existing natural gas). Such contamination can strongly influence the overall CO2 wettability, storage capacity, and containment security. Hence, the present study uses the nuclear magnetic resonance (NMR) core flooding technique to investigate and compare the residual saturations of pure CO2, pure N2, and a 50:50 CO2/N2 mixture in an Indiana limestone. The longitudinal and transverse relaxation times (T1 and T2) are measured to examine the displacement process of the pore network, and the trapping mechanism is evaluated at the pore scale as a determinant of the field-scale flow behavior. The NMR T1-T2 and 2D maps are used to observe the fluid configurations in the pore network, and the T1/T2 ratios are used to evaluate the microscopic wettability of the limestone grains by the pore-space fluids following each drainage/imbibition process step. The results indicate substantial residual gas trapping in the rock for the CO2-brine, N2-brine, and CO2/N2-brine systems, corresponding to gas saturations of 25%, 27%, and 26%, respectively. In the CO2-brine system, the intermolecular interplay between the CO2-enriched brine and limestone grains results in a higher T1/T2 ratio and significantly reduces the hydrophilicity of the limestone. Furthermore, the NMR T2 distribution reveals the occurrence of preferential water displacement into the large pores (r > 1 µm) and from the intermediate pores (0.03 µm < r < 1 µm), whereas water remains immobile in the smaller pores (r < 0.03 µm). The insignificant difference in residual trapping saturation between pure CO2 and the CO2-N2 mixture indicates the potential to allow for impurities in the CO2 phase in CCS without reducing the residual trapping capacity. Thus, the present work provides comprehensive information on the impact of gas injection on residual gas trapping in subsurface geological formations at the pore scale, thereby aiding in the development of CCS and other potential applications in enhanced oil recovery (EOR).
  • RockAVO: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data

    Corrales Guerrero, Miguel Angel; Hoteit, Hussein; Ravasi, Matteo (arXiv, 2023-07-16) [Preprint]
    The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface. We propose a novel, data-driven approach named RockAVO that utilizes optimal basis functions learned from well log information to directly link band-limited petrophysical reflectivities to pre-stack seismic data. RockAVO is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic AVO gathers created from measured or rock-physics synthesized elastic well-logs. In inference, seismic pre-stack data are first projected into a set of band-limited petrophysical properties using the previously computed basis functions; this is followed by regularized post-stack seismic inversion of the individual properties. In this work, we apply the RockAVO methodology to a synthetic dataset based on the Smeaheia reservoir model and the open Volve field dataset. Numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time-lapse, pre-stack seismic data for water saturation changes.

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