Recent Submissions

  • Carbon dioxide thickening: A review of technological aspects, advances and challenges for oilfield application

    Pal, Nilanjan; Zhang, Xuan; Ali, Muhammad; Mandal, Ajay; Hoteit, Hussein (Fuel, Elsevier BV, 2022-01) [Article]
    The relatively low density and viscosity of carbon dioxide (CO2) in supercritical state create several drawbacks, including gravity override, viscous fingering, water production/treatment problems, and poor proppant transport for the petroleum industry. The introduction of CO2 thickeners offers a promising additive technology with sufficient solubility and viscosity enhancement attributes. The current article reviews the technical advances, challenges, and applicability of thickened CO2, particularly for hydrocarbon recovery. Different types of thickeners, including polymers, tailor-made surfactants, and small associating compounds, were investigated in terms of their nature, physicochemical traits, cost, and applications. The molecular weight and concentration, shear rate, co-solvent composition, temperature, and pressure play a significant role in the intermolecular forces and miscibility effect of thickeners in the presence of dense CO2. Binary co-polymers (non-fluorinated non-siloxane materials) and small molecule (associating) compounds are promising options for CO2 thickening owing to their enhanced performance, cost-effectiveness, and low ecological footprint. This study provides a comprehensive review of existing technologies, outline the gaps, potential, and required area for improvement.
  • Fractal and multifractal characterization of stochastic fracture networks and real outcrops

    Zhu, Weiwei; Lei, Gang; He, Xupeng; Patzek, Tadeusz; Wang, Moran (Journal of Structural Geology, Elsevier BV, 2022-01) [Article]
    The fractal dimension and multifractal spectrum are widely used to characterize the complexity of natural fractures. However, systematic investigations, considering impacts of different fracture geometrical properties (fracture lengths, orientations, center positions) and system sizes, on the fractal and multifractal characterization of complex fracture networks are insufficient. Here, we utilize an in-house developed DFN modeling software, hatchfrac, to construct stochastic fracture networks with prescribed distributions and systematically study the impact of three geometrical properties of fractures and system sizes on the fractal and multifractal characterization. We calculate the single fractal dimension and multifractal spectrum with the box-counting method. The single fractal dimension, D, and the difference of singularity exponent, Δα, are used to represent the fractal and multifractal patterns, respectively. We find that fracture lengths, orientations and system sizes positively correlate with D and Δα, while the system size has the most significant impact among the four parameters. D is uncorrelated with fracture positions (FD), which means that a single fractal dimension cannot capture the complexity caused by clustering effects. However, Δα has a strong negative correlation with FD, implying that clustering effects make fracture networks more complex, and Δα can capture the difference. We also digitize 80 outcrop maps with a novel fracture detection algorithm and calculate their fractal dimension and multifractal spectrum. We find wide variations of D and Δα on those outcrop maps, even for outcrops at similar scales, indicating that a universal indicator for characterizing fracture networks at different scales or the same scale is almost impossible. D and Δα have negligible correlations with scales, supporting the self-similarity patterns of natural fracture networks.
  • Chemical Compositions in Modified Salinity Waterflooding of Calcium Carbonate Reservoirs: Experiment

    Yutkin, Maxim; Radke, C. J.; Patzek, Tadeusz (Transport in Porous Media, Springer Science and Business Media LLC, 2022-01-01) [Article]
    Modified or low-salinity waterflooding of carbonate oil reservoirs is of considerable economic interest because of potentially inexpensive incremental oil production. The injected modified brine changes the surface chemistry of the carbonate rock and crude oil interfaces and detaches some of adhered crude oil. Composition design of brine modified to enhance oil recovery is determined by labor-intensive trial-and-error laboratory corefloods. Unfortunately, limestone, which predominantly consists of aqueous-reactive calcium carbonate, alters injected brine composition by mineral dissolution/precipitation. Accordingly, the rock reactivity hinders rational design of brines tailored to improve oil recovery. Previously, we presented a theoretical analysis of 1D, single-phase brine injection into calcium carbonate-rock that accounts for mineral dissolution, ion exchange, and dispersion (Yutkin et al. in SPE J 23(01):084–101, 2018. Here, we present the results of single-phase waterflood-brine experiments that verify the theoretical framework. We show that concentration histories eluted from Indiana limestone cores possess features characteristic of fast calcium carbonate dissolution, 2:1 ion exchange, and high dispersion. The injected brine reaches chemical equilibrium inside the porous rock even at injection rates higher than 3.5 × 10−3 m s−1 (1000 ft/day). Ion exchange results in salinity waves observed experimentally, while high dispersion is responsible for long concentration history tails. Using the verified theoretical framework, we briefly explore how these processes modify aqueous-phase composition during the injection of designer brines into a calcium-carbonate reservoir. Because of high salinity of the initial and injected brines, ion exchange affects injected concentrations only in high surface area carbonates/limestones, such as chalks. Calcium-carbonate dissolution only affects aqueous solution pH. The rock surface composition is affected by all processes.
  • Fluids, soils and rocks: Electromagnetic and NMR signatures

    Hakiki, Farizal; Santamarina, Carlos (Society of Exploration Geophysicists, 2021-12-24) [Conference Paper]
    Electromagnetic phenomena support the development of exceptional methods for non-destructive, non-contact geomaterial characterization and subsurface process monitoring. Such phenomena include Ohmic conduction, electromagnetic wave propagation and nuclear magnetic resonance. The physical parameters involved include DC conductivity σ, complex permittivity ε*(ω) and complex permeability μ*(ω) spectra, and nuclear magnetic resonance relaxation. These parameters reflect mineralogy, particle and pore size distributions, pore network topology and anisotropy, fluid composition, state and fluid-mineral interaction. Proper data interpretation requires careful understanding of underlying physical concepts and measurement methods.
  • Forecast of Economic Tight Oil and Gas Production in Permian Basin

    Saputra, Wardana; Kirati, Wissem; Patzek, Tadeusz (Energies, MDPI AG, 2021-12-22) [Article]
    We adopt a physics-guided, data-driven method to predict the most likely future production from the largest tight oil and gas deposits in North America, the Permian Basin. We first divide the existing 53,708 horizontal hydrofractured wells into 36 spatiotemporal well cohorts based on different reservoir qualities and completion date intervals. For each cohort, we fit the Generalized Extreme Value (GEV) statistics to the annual production and calculate the means to construct historical well prototypes. Using the physical scaling method, we extrapolate these well prototypes for several more decades. Our hybrid, physico-statistical prototypes are robust enough to history-match the entire production of the Permian mudstone formations. Next, we calculate the infill potential of each sub-region of the Permian and schedule the likely future drilling programs. To evaluate the profitability of each infill scenario, we conduct a robust economic analysis. We estimate that the Permian tight reservoirs contain 54–62 billion bbl of oil and 246–285 trillion scf of natural gas. With time, Permian is poised to be not only the most important tight oil producer in the U.S., but also the most important tight gas producer, surpassing the giant Marcellus shale play.
  • Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation

    Santoso, Ryan; He, Xupeng; Alsinan, Marwa; Figueroa Hernandez, Ruben; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LHS) to construct the fine low-fidelity model to be deployed in the Bayesian runs, where we use the Metropolis-Hastings algorithm. Finally, the posterior is fed into the high-fidelity model to evaluate the matching quality. This work demonstrates the importance of including uncertainties in history matching. Bayesian provides a robust framework to allow uncertainty quantification within the reservoir history matching. Under uniform prior, the convergence of the Bayesian is very sensitive to the parameter ranges. When the solution is far from the mean of the parameter ranges, the Bayesian introduces bios and deviates from the observed data. Our results show that updating the prior from the coarse low-fidelity model accelerates the Bayesian convergence and improves the matching convergence. Bayesian requires a huge number of runs to produce an accurate posterior. Running the high-fidelity model multiple times is expensive. Our workflow tackles this problem by deploying a fine low-fidelity model to represent the high-fidelity model in the main runs. This fine low-fidelity model is fast to run, while it honors the physics and accuracy of the high-fidelity model. We also use ANOVA sensitivity analysis to measure the importance of each parameter. The ranking gives awareness to the significant ones that may contribute to the matching accuracy. We demonstrate our workflow for a geothermal reservoir with static and operational uncertainties. Our workflow produces accurate matching of thermal recovery factor and produced-enthalpy rate with physically-consistent posteriors. We present a novel workflow to account for uncertainty in reservoir history matching involving multi-resolution interaction. The proposed method is generic and can be readily applied within existing history-matching workflows in reservoir simulation.
  • High-Resolution Micro-Continuum Approach to Model Matrix-Fracture Interaction and Fluid Leakage

    He, Xupeng; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Understanding the fundamental mechanism of fracture-matrix fluid exchange is crucial for the modeling of fractured reservoirs. Traditionally, high-resolution simulations for flow in fractures often neglect the matrix-fracture leakage influence on the fracture hydraulic properties, i.e., assuming impermeable fracture walls. This work introduces a micro-continuum approach to capture the matrix-fracture leakage interaction and its effect on the rock fractures’ hydraulic properties. Because of the multiscale nature of fractured media, full physics Navier-Stokes (NS) representation everywhere in the whole domain is not feasible. We thus employ NS equations to describe the flow in the fracture, and Darcy’s law to model the flow in the surrounding porous rocks. Such hybrid modeling is achieved using the extended Darcy-Brinkman-Stokes (DBS) equation. With this approach, a unified conservation equation for flow in both media is applied by choosing appropriate parameters (e.g., porosity and permeability) for the corresponding domains. We apply an accurate Mixed Finite Element approach to solve the extended DBS equation. Various sensitivity analyses are conducted to explore the leakage effects on the fracture hydraulic properties by varying surrounding matrix permeability, fracture roughness, and Reynolds number (Re). Streamline profiles show the presence of back-flow phenomena, where in-flow and out-flow are possible between the matrix and the fractures. Further, zones of stagnant (eddy) flow are observed around locations with large asperities of sharp corners under high Re conditions. Numerical results show the significant effects of roughness and inertia on flow predictions in fractures for both impermeable and leaky wall cases. Besides, the side-leakage effect can create non-uniform flow behavior within the fracture that may differ significantly from the case with impermeable wall conditions. And this matrix-fracture leakage influence on hydraulic properties of rock fractures matters especially for cases with high matrix permeability, high fracture roughness, and low Re values. In summary, we present a high-resolution micro-continuum approach to explore the flow exchange behavior between the fracture and rock matrix, and further investigate the static and dynamic effects, including variable Reynold numbers, mimicking flow near and away from the wellbore. The approach and results provide significant insights into the flow of fluids through fractures within permeable rocks and can be readily applied in field-scale reservoir simulations.
  • Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

    Santoso, Ryan; He, Xupeng; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.
  • Novel Analytical Solution and Type-Curves for Lost-Circulation Diagnostics of Drilling Mud in Fractured Formation

    Albattat, Rami; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Loss of circulation is a major problem that often causes interruption to drilling operations, and reduction in efficiency. This problem often occurs when the drilled wellbore encounters a high permeable formation such as faults or fractures, leading to total or partial leakage of the drilling fluids. In this work, we present a novel semi-analytical solution and type-curves that offer a quick and accurate diagnostic tool to assess the lost-circulation of Herschel-Bulkley fluids in fractured media. Based on the pressure and mud loss trends, the tool can estimate the effective fracture conductivity, the cumulative mud-loss volume, and the leakage period. The behavior of lost-circulation into fractured formation can be assessed using analytical methods that can be deployed to perform flow diagnostics, such as the rate of fluid leakage and the associated fracture hydraulic properties. In this study, we develop a new semi-analytical method to quantify the leakage of drilling fluid flow into fractures. The developed model is applicable for non-Newtonian fluids with exhibiting yield-power-law, including shear thickening and thinning, and Bingham plastic fluids. We propose new dimensionless groups and generate novel dual type-curves, which circumvent the non-uniqueness issues in trend matching of type-curves. We use numerical simulations based on finite-elements to verify the accuracy of the proposed solution, and compare it with existing analytical solutions from the literature. Based on the proposed semi-analytical solution, we propose new dimensionless groups and generate type-curves to describe the dimensionless mud-loss volume versus the dimensionless time. To address the non-uniqueness matching issue, we propose, for the first time, complimentary derivative-based type-curves. Both type-curve sets are used in a dual trend matching, which significantly reduced the non-uniqueness issue that is typically encountered in type-curves. We use data for lost circulation from a field case to show the applicability of the proposed method. We apply the semi-analytical solver, combined with Monte-Carlo simulations, to perform a sensitivity study to assess the uncertainty of various fluid and subsurface parameters, including the hydraulic property of the fracture and the probabilistic prediction of the rate of mud leakage into the formation. The proposed approach is based on a novel semi-analytical solution and type-curves to model the flow behavior of Herschel-Bulkley fluids into fractured reservoirs, which can be used as a quick diagnostic tool to evaluate lost-circulation in drilling operations.
  • An Accurate Cubic Law for the Upscaling of Discrete Natural Fractures

    He, Xupeng; AlSinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Abstract Modeling fluid flow in fractured reservoirs requires an accurate evaluation of the hydraulic properties of discrete fractures. Full Navier-Stokes simulations provide an accurate approximation of the flow within fractures, including fracture upscaling. However, its excessive computational cost makes it impractical. The traditionally used cubic law (CL) is known to overshoot the fracture hydraulic properties significantly. In this work, we propose an alternative method based on the cubic law. We first develop geometric rules based on the fracture topography data, by which we subdivide the fracture into segments and local cells. We then modify the aperture field by incorporating the effects of flow direction, flow tortuosity, normal aperture, and local roughness. The approach is applicable for fractures in 2D and 3D spaces. This paper presented almost all existing CL-based models in the literature, which include more than twenty models. We benchmarked all these models, including our proposed model, for thousands of fracture cases. High-resolution simulations solving the full-physics Navier-Stokes (NS) equations were used to compute the reference solutions. We highlight the behavior of accuracy and limitations of all tested models as a function of fracture geometric characteristics, such as roughness. The obtained accuracy of the proposed model showed the highest for more than 2000 fracture cases with a wide range of tortuosity, roughness, and mechanical aperture variations. None of the existing methods in the literature provide this level of accuracy and applicability. The proposed model retains the simplicity and efficiency of the cubic law and can be easily implemented in workflows for reservoir characterization and modeling.
  • Recent advances in carbon dioxide geological storage, experimental procedures, influencing parameters, and future outlook

    Ali, Muhammad; Jha, Nilesh Kumar; Pal, Nilanjan; Keshavarz, Alireza; Hoteit, Hussein; Sarmadivaleh, Mohammad (Earth-Science Reviews, Elsevier BV, 2021-12-14) [Article]
    The oxidation of fossil fuels produces billions of tons of anthropogenic carbon dioxide (CO2) emissions from stationary and nonstationary sources per annum, contributing to global warming. The natural carbon cycle consumes a portion of CO2 emissions from the atmosphere. In contrast, substantial CO2 emissions accumulate, making it the largest contributor to greenhouse gas emissions and causing a rise in the planet's temperature. The Earth's temperature was estimated to be 1 °C higher in 2017 compared to the mid-twentieth century. A solution to this problem is CO2 storage in underground formations, abundant throughout the world. Millions of tons of CO2 are stored underground into geological formations annually, including deep saline aquifers. However, these geological formations have minute concentrations of organic material, significantly influencing the CO2 containment security, fluid dynamics, and storage potential. Examining the wetting characteristics and influencing parameters of geological formations is pertinent to understanding the supercritical CO2 behavior in rock/brine systems. Wettability is an important parameter governing the ability of injected CO2 to displace formation water and determine the containment security and storage capacity. Previously, many studies have provided comprehensive overviews of CO2-wettability depending on various factors, such as pressure, temperature, salinity, formation type, surfactants, and chemicals. However, mineral surfaces in these wettability studies are chemically cleaned, and natural geological storage conditions are anoxic (containing organic molecules) where reductive conditions ensue. A severe gap exists in the literature to comprehend the effects of organic material for determining the CO2 storage capacities and how this effect can be reversed using nanomaterial for increased CO2 storage potential. Therefore, we conducted a thorough literature review to comprehend the recent advances in rock/CO2/brine and rock/oil/brine systems containing organic material in different geo-storage formations. We also present recent advances in anoxic rock/CO2/brine and rock/oil/brine systems that have employed nanomaterial for wettability reversal to be more water-wet. This comprehensive review is divided into four parts: 1) reviewing CO2 emissions and geological systems, 2) recent advances in direct quantitative experimental procedures in anoxic rock/CO2/brine systems and effects of organic contaminations on experimental methodology and their controls, 3) effects of organics and nanomaterial in rock/CO2/brine and rock/oil/brine systems, and 4) the future outlook of this study.
  • Optimization and uncertainty quantification model for time-continuous geothermal energy extraction undergoing re-injection

    Hoteit, Hussein; He, Xupeng; Yan, Bicheng; Vahrenkamp, Volker (arXiv, 2021-12-10) [Preprint]
    Geothermal field modeling is often associated with uncertainties related to the subsurface static properties and the dynamics of fluid flow and heat transfer. Uncertainty quantification using simulations is a useful tool to design optimum field-development and to guide decision-making. The optimization process includes assessments of multiple time-dependent flow mechanisms, which are functions of operational parameters subject to subsurface uncertainties. This process requires careful determination of the parameter ranges, dependencies, and their probabilistic distribution functions. This study presents a new approach to assess time-dependent predictions of thermal recovery and produced-enthalpy rates, including uncertainty quantification and optimization. We use time-continuous and multi-objective uncertainty quantification for geothermal recovery, undergoing a water re-injection scheme. The ranges of operational and uncertainty parameters are determined from a collected database, including 135 geothermal fields worldwide. The uncertainty calculation is conducted non-intrusively, based on a workflow that couples low-fidelity models with Monte Carlo analysis. Full-physics reservoir simulations are used to construct and verify the low-fidelity models. The sampling process is performed with Design of Experiments, enhanced with space-filling, and combined with analysis of covariance to capture parameter dependencies. The predicted thermal recovery and produced-enthalpy rates are then evaluated as functions of the significant uncertainty parameters based on dimensionless groups. The workflow is applied for various geothermal fields to assess their optimum well-spacing in their well configuration. This approach offers an efficient and robust workflow for time-continuous uncertainty quantification and global sensitivity analysis applied for geothermal field modeling and optimization.
  • Solvable model of gas production decline from hydrofractured networks

    Marder, M.; Eftekhari, Behzad; Patzek, Tadeusz (Physical Review E, American Physical Society (APS), 2021-12-09) [Article]
    We address questions that arose from studying gas and oil production from hydrofractured wells. Does past production predict the future? This depends on deducing from production as much as possible about the plausible geometries of the fracture network. We address the problem through a solvable model and use kinetic Monte Carlo and Green's function techniques to solve it. We have three main findings. First, at sufficiently long times, the production from all compact fracture networks is described by a universal function with two scaling parameters, one of which is the diffusivity of unbroken rock α and the second of which is a parameter Vext with units of volume. Second, for fracture networks where the power-law distribution of fracture spacings falls below a critical value (and this appears to be the case in practice), early-time production always scales as one over the square root of time. Third, the diffusivity α that sets the scale for late-time production is inherently difficult to estimate from production data, but the methods here provide the best hope of obtaining it and thus can determine the physics that will govern the decline of unconventional gas and oil wells.
  • Discrete Fracture Model for Hydro-Mechanical Coupling in Fractured Reservoirs

    He, Xupeng; Qiao, Tian; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-09) [Conference Paper]
    The process of coupled flow and mechanics occurs in various environmental and energy applications, including conventional and unconventional fractured reservoirs. This work establishes a new formulation for modeling hydro-mechanical coupling in fractured reservoirs. The discrete-fracture model (DFM), in which the porous matrix and fractures are represented explicitly in the form of unstructured grid, has been widely used to describe fluid flow in fractured formations. In this work, we extend the DFM approach for modeling coupled flow-mechanics process, in which flow problems are solved using the multipoint flux approximation (MPFA) method, and mechanics problems are solved using the multipoint stress approximation (MPSA) method. The coupled flow-mechanics problems share the same computational grid to avoid projection issues and allow for convenient exchange between them. We model the fracture mechanical behavior as a two-surface contact problem. The resulting coupled system of nonlinear equations is solved in a fully-implicit manner. The accuracy and generality of the numerical implementation are accessed using cases with analytical solutions, which shows an excellent match. We then apply the methodology to more complex cases to demonstrate its general applicability. We also investigate the geomechanical influence on fracture permeability change using 2D rock fractures. This work introduces a novel formulation for modeling the coupled flow-mechanics process in fractured reservoirs, and can be readily implemented in reservoir characterization workflow.
  • Corrosion of Buried Metals: Soil Texture and Pore Fluid Saturation

    Castro, Gloria M. (2021-12) [Dissertation]
    Advisor: Santamarina, Carlos
    Committee members: Alshareef, Husam N.; Burns, Susan; Ahmed, Shehab
    The corrosion of buried metals affects geosystems that range from pipelines and nuclear waste disposal to reinforced concrete and archeology. Associated costs exceed 1 trillion dollars per year worldwide, yet current classification methods for soil corrosivity have limited predictive capacity. This study -triggered by the recent development of the Revised Soil Classification System RSCS- seeks to identify the critical soil and environment properties that can improve the prediction of buried metal corrosion. The experimental studies conducted as part of this research recognize the inherently electro-chemo-transport coupled nature of buried metal corrosion, and places emphasis on phenomena that have been inadequately captured in previous studies, such as the effect of soil texture and fines plasticity, partial saturation and moisture cycles, and conditions in Sabkha environments. The comprehensive experimental program involves detailed protocols for specimen preparation, advanced visualization (X-ray micro-CT), corrosion residual characterization (XRD), and detailed image analyses of extracted coupons. Experiments include both laboratory mixtures and a wide range of field specimens gathered throughout Saudi Arabia; furthermore, field observations expand soil assessment to native environmental conditions. Theoretical analyses based on mass conservation and electrochemical phenomena complement the experimental study. Experimental and analytical results lead to new soil corrosivity assessment guidelines. Results show the relevance of the sediment pore fluid saturation, sediment texture, air and water connectivity, active corroding areas, the effect of environmental cycles on buried metal corrosion and evolving backfill contamination.
  • The Effect of Hydraulic Fracture Geometry on Well Productivity in Shale Oil Plays with High Pore Pressure

    Arias, Daniela; Klimkowski, Lukasz; Finkbeiner, Thomas; Patzek, Tadeusz (Energies, MDPI AG, 2021-11-18) [Article]
    We propose three idealized hydraulic fracture geometries (“fracture scenarios”) likely to occur in shale oil reservoirs characterized by high pore pressure and low differential in situ stresses. We integrate these geometries into a commercial reservoir simulator (CMG-IMEX) and examine their effect on reservoir fluids production. Our first, reference fracture scenario includes only vertical, planar hydraulic fractures. The second scenario has stimulated vertical natural fractures oriented perpendicularly to the vertical hydraulic fractures. The third fracture scenario has stimulated horizontal bedding planes intersecting the vertical hydraulic fractures. This last scenario may occur in mudrock plays characterized by high pore pressure and transitional strike-slip to reverse faulting stress regimes. We demonstrate that the vertical and planar fractures are an oversimplification of the hydraulic fracture geometry in anisotropic shale plays. They fail to represent the stimulated volume geometric complexity in the reservoir simulations and may confuse hydrocarbon production forecast. We also show that stimulating mechanically weak bedding planes harms hydrocarbon production, while stimulated natural fractures may enhance initial production. Our findings reveal that stimulated horizontal bedding planes might decrease the cumulative hydrocarbon production by as much as 20%, and the initial hydrocarbon production by about 50% compared with the reference scenario. We present unique reservoir simulations that enable practical assessment of the impact of varied hydraulic fracture configurations on hydrocarbon production and highlight the importance of constraining present-day in situ stress state and pore pressure conditions to obtain a realistic hydrocarbon production forecast.
  • The creation of calcite microcrystals and microporosity through deep burial basinal flow processes driven by plate margin obduction – A realistic model?

    Wei, Wenwen; Whitaker, Fiona; Hoteit, Hussein; Vahrenkamp, Volker (Marine and Petroleum Geology, Elsevier BV, 2021-11-15) [Article]
    Calcite microcrystals and associated microporosity are ubiquitous and extensively developed in Jurassic and Cretaceous carbonate sequences in the Middle East. Clumped isotope analyses of calcite microcrystals in the Lower Cretaceous Thamama-B strata in UAE reservoirs indicate temperatures of 60–90 °C and burial of 1.5–2.5 km suggesting formation synchronous with and updip of Late Cretaceous ophiolite obduction at the Eastern Arabian continental margin. Assuming that recrystallization of precursor calcite to calcite microcrystals requires initially undersaturation to drive dissolution/re-precipitation a basin-scale 2D reactive transport model (RTM) was constructed. The model is constrained by hydro-mechanical simulations and used to quantitatively evaluate the hypothesis that the formation of calcite microcrystals and associated microporosity is driven by expulsion of compaction fluids during rapid burial. The combined influence of fluid flux and cooling results in trace net calcite dissolution (porosity increase <0.1 vol %) focused at depths of 2.7–5.1 km. The presence of even minor amounts of minerals with common ions (dolomite and anhydrite) induces additional dissolution but does not change its’ spatial distribution. Whilst RTMs only yield a minimum estimate of the degree of recrystallization that likely occurs driven by calcite disequilibrium, simulations suggest these reactions occur at temperatures of 95–170 °C, markedly higher than those estimated from clumped isotopes of the calcite microcrystals. Mixing of fluids leaked from underlying strata up faults into the lateral flow system could play an important role in burial diagenesis, with the slow leakage dissolving up to ten times greater mass of calcite than a shorter pulse of equivalent fluid volume. A previously unrecognized effect of the introduction of H2S(aq)-rich fluids, derived from accelerated thermal maturation or thermochemical sulphate reduction (TSR), into formation water with high CO2(aq) concentration is CO2 degassing that drives net calcite precipitation (<3 vol %). This suite of numerical simulations suggest that calcite disequilibrium will have occurred in fluids expelled during ophiolite obduction, but that any associated recrystallization will have occurred at depths greater than those inferred from temperatures measured in the calcite microcrystals. Recrystallization at shallower depths may have been associated by fluid-mixing around the injection points, but reaction kinetics suggests that laterally pervasive alteration would require very rapid flow rates. These results suggest that alternative mechanism(s) are needed to be considered to explain the extensively developed calcite microcrystals and associated microporosity in Mesozoic carbonates of the Middle East.
  • Co-Optimization of CO2 Storage and Enhanced Gas Recovery Using Carbonated Water and Supercritical CO2

    Omar, Abdirizak; Addassi, Mouadh; Vahrenkamp, Volker; Hoteit, Hussein (Energies, MDPI AG, 2021-11-10) [Article]
    CO2-based enhanced gas recovery (EGR) is an appealing method with the dual benefit of improving recovery from mature gas reservoirs and storing CO2 in the subsurface, thereby reducing net emissions. However, CO2 injection for EGR has the drawback of excessive mixing with the methane gas, therefore, reducing the quality of gas produced and leading to an early breakthrough of CO2. Although this issue has been identified as a major obstacle in CO2-based EGR, few strategies have been suggested to mitigate this problem. We propose a novel hybrid EGR method that involves the injection of a slug of carbonated water before beginning CO2 injection. While still ensuring CO2 storage, carbonated water hinders CO2-methane mixing and reduces CO2 mobility, therefore delaying breakthrough. We use reservoir simulation to assess the feasibility and benefit of the proposed method. Through a structured design of experiments (DoE) framework, we perform sensitivity analysis, uncertainty assessment, and optimization to identify the ideal operation and transition conditions. Results show that the proposed method only requires a small amount of carbonated water injected up to 3% pore volumes. This EGR scheme is mainly influenced by the heterogeneity of the reservoir, slug volume injected, and production rates. Through Monte Carlo simulations, we demonstrate that high recovery factors and storage ratios can be achieved while keeping recycled CO2 ratios low.
  • HatchFrac: A fast open-source DFN modeling software

    Zhu, Weiwei; Khirevich, Siarhei; Patzek, Tadeusz (Wiley, 2021-11-09) [Preprint]
    This paper introduces a comprehensive C++ software package, HatchFrac, for stochastic modelling of fracture networks in two and three dimensions. Two main methods, the inverse CDF method and acceptance-rejection method, are applied to generate random variables from the stochastic distributions commonly used in discrete fracture network (DFN) modelling. The multilayer per-ceptron (MLP) machine learning approach, combined with the inverse CDF method, is implemented to generate random variables following any sampling distribution. To make the code faster, we extend the Newman-Ziff to determine clusters in the fracture networks. When combined with the block method, the Ziff algorithm improves the coding efficiency significantly. The software generates the T-type fracture intersections in the network, which can be used in applications involving fracture growth or incorporating geomechanics. We introduce three applications of HatchFrac that demonstrate the versatility of our software: percolation analysis, fracture intensity analysis, and flow and connectivity analysis.
  • Holocene sediment distribution in the Al Wajh platform lagoon (northern Red Sea, Saudi Arabia), a modern analogue for large rift basin carbonate platforms

    Petrovic, Alexander; Fuentes, Manuel Ariza; Putri, Indah; Yahaya, Liyana Nur; Khanna, Pankaj; Purkis, Sam J.; Vahrenkamp, Volker (Sedimentology, Wiley, 2021-11-06) [Article]
    Sedimentary patterns and hydrodynamic transport processes on modern carbonate platforms in arid climates are understudied compared to platforms in humid-tropical climates. The Al Wajh platform – located in the Arabian–African desert belt – is a large land-attached carbonate platform in the Red Sea providing an excellent opportunity to fill this gap. The platform covers some 1800 km² and is almost completely enclosed by a 115 km long reef-shoal belt. More than 200 sediment surface samples were analyzed in order to investigate the lateral sediment distribution within the lagoon. The seafloor map was refined integrating sample depths with previous published bathymetric information. Conductivity and temperature profiles were measured to study the lagoonal water body. The lagoon is dominated by poorly sorted, sand-sized sediments with low total organic carbon content, while carbonate fines content shows significant lateral variation. Aragonite dominates sediment mineralogy with high-Mg calcite and low-Mg calcite being significant admixtures. Fine-grained siliciclastics are found across the entire lagoon, with angular quartz locally enriched in near-shore and distal areas. Seven component assemblages are defined ranging from benthic foraminifera and mollusc-rich to reef debris-rich component assemblages. Platform-interior ooids are for the first time documented from the modern Red Sea. The heterogeneous distribution of carbonate fines shows no water depth related trends, while the component assemblage arrangement is depth related. Hydrodynamics are interpreted to be the main mechanism controlling carbonate fines distribution in the lagoon. A near-shore enrichment of angular sand-sized quartz suggests influx through wadis during flash floods, while an almost even distribution of fine-grained siliciclastics possibly indicate aeolian import. These findings provide new insights to the importance of hydrodynamic transport processes for sediment distribution in a land-attached platform lagoons in an arid climate. Finally, this study presents a comparison with other modern platforms and discusses implications for improving strategies of hydrocarbon field development in rift-basin carbonates.

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