Browsing by Subject "Subsurface"
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Item Geologic characterization and modeling for quantifying CO₂ storage capacity of the High Island 10-L field in Texas state waters, offshore Gulf of Mexico(2019-09-12) Ramirez Garcia, Omar; Chuchla, Richard J. (Richard Julian); Meckel, Timothy AshworthCarbon dioxide capture and storage (CCS) is a promising technology for mitigating climate change by reducing CO₂ emissions to the atmosphere and injecting captured industrial emissions into deep geologic formations. Deep subsurface storage in geologic formations is similar to trapping natural hydrocarbons and is one of the key components of CCS technology. The quantification of the available subsurface storage resource is the subject of this research project. This study focuses on site-specific geologic characterization, reservoir modeling, and CO₂ storage resource assessment (capacity) of a depleted oil and gas field located on the inner continental shelf of the Gulf of Mexico, the High Island 10L field. lower Miocene sands in the Fleming Group beneath the regional transgressive Amphistegina B shale have extremely favorable geologic properties (porosity, thickness, extent) and are characterized in this study utilizing 3-D seismic and well logs. Key stratigraphic surfaces between maximum flooding surfaces (MFS-9 to MFS-10) demonstrate how marine regression and transgression impact the stacking pattern of the thick sands and overlying seals, influencing the overall potential for CO₂ storage. One of the main uncertainties when assessing CO₂ storage resources at different scales is to determine the fraction of the pore space within a formation that is practically accessible for storage. The goal of the modeling section of this project is to address the uncertainty related to the static parameters affecting calculations of available pore space by creating facies and porosity geostatistical models based on the spatial variation of the available data. P50 values for CO₂ storage capacity range from 37.56 to 40.39 megatonnes (Mt), showing a narrow distribution of values for different realizations of the geostatistical models. An analysis of the pressure build-up effect on storage capacity was also performed, showing a reduction in capacity. This research further validates the impact of the current carbon tax credit program (45Q), applied directly to the storage resources results for the High Island field 10L using a simple NPV approach based on discounted cash flows. Several scenarios are assessed, where the main variables are the duration of the applicability of the tax credit, number of injection wells, and total storage capacity. Results are measured in terms of the cost of capture required for a project to be economic, given previous assumptions.Item In-Situ Reductive Dehalogenation of Tce: Laboratory Evaluation of Ti(III) Citrate and Tetrasulfonated Co(II) Phthalocyanine(2004-12) Kohl, Christy Michelle; Pope, Gary A.; Britton, LarryThe research presented in this report is the second project of ongoing research on this subject at the University of Texas and is focused on in-situ remediation of dense chlorinated contaminants in the subsurface. The purpose of the first project was to find the most effective oxidation/reduction (redox) catalyst combination to dehalogenate TCE to less hazardous products. The focus of this second project is to test the selected redox catalyst, tetrasulfonated Co(II) phthalocyanine, and the reductant (Ti(III) citrate) in TCE-contaminated sand-packed columns. Laboratory evaluation of Ti(III) citrate and CoPTS included both column and batch experiments. There were three series of initial column experiments to establish operational conditions, four series of batch experiments to set chemical conditions and the final column experiment that observed successful reductive dehalogenation of DNAPL in a TCE-contaminated sand-packed column within 10-17 PV of remedial solution injected.Item Interactions between turbidity currents, turbidites and topography generated by a mobile substrate(2016-12) Minton, Brandon Wade; Mohrig, David; Kim, Wonsuck; Buttles, JamesModels for development and filling of submarine minibasins remain incomplete for the following reasons: (1) they seldom account for growth of seafloor topography via subsurface salt motion that coincides with turbidite sedimentation; (2) they seldom account for interactions between turbidity currents and seafloor topography that influence subsequent sedimentation patterns; and (3) they seldom consider the degree to which the evolution of seafloor topography associated with any single minibasin is affected by its neighboring minibasins. These points have now been addressed through a novel set of laboratory experiments. In the suites of experiments, turbidity currents consisting of 1.5% sediment by volume were released onto a 1.2 m x 1.2 m x 0.05 m platform filled by a composite layer of PDMS polymer and pliable putty (Silly Putty™). Interactions between pre-existing bed topography and turbidity currents result in differential loading of the substrate and influence depositional patterns. These interactions are achieved through a combination of blocking and focusing of currents by topography and by remobilization and removal of deposits from steeply sloping surfaces. Spatially varying deposit thicknesses generate locations that exceed the threshold load and begin to deform the mobile substrate. Turbidites of insufficient thickness are simply “along for the ride” and do not contribute to substrate deformation. Additionally, the tendency of the far-field surface to uplift or subside is preconditioned by the topography of the initial surface. These findings represent contributions towards the goal of better defining the important transition from turbidite sedimentation on an unconfined slope to deposition in minibasins.Item Multifunctional foams and emulsions for subsurface applications(2017-12) Singh, Robin, Ph. D.; Mohanty, Kishore Kumar; DiCarlo, David; Huh, Chun; Saleh, Navid; Sepehrnoori, KamyFoams and emulsions hold immense potential in assisting in the different stages of oil recovery processes such as enhanced oil recovery, drilling, and completion. This work is focused on developing robust, multifunctional foams or emulsions for subsurface applications, which offer unique advantages over conventional methods. The first half of the dissertation is focused on investigating novel foams stabilized using nanoparticles and/or surfactants to improve the gas enhanced oil recovery process. Gas flooding often has poor volumetric sweep efficiency due to viscous fingering, channeling, and gravity override. Foam is a promising tool to improve sweep efficiency in gas floods. It can reduce the mobility of gas by several orders of magnitude by increasing its apparent viscosity while keeping the liquid phase mobility unchanged. For sandstone reservoirs, which are typically water-wet in nature, two different approaches of foam stabilization using nanoparticles were developed. In the first approach, synergistic stabilization of foams with a mixture of hydrophilic nanoparticles and an anionic surfactant was investigated. Foam stability experiments in bulk and porous media tests showed that adding hydrophilic nanoparticles to surfactant formulations increases the foam stability. Microscopy revealed that nanoparticles are trapped in lamellae as well as at the Gibbs-Plateau borders. These nanoparticles act as physical barriers and retard the liquid drainage and the Ostwald ripening process. To fundamentally understand the role of nanoparticles in altering the foam dynamics in porous media, a high-pressure visualization experiment was performed in a 2D layered, heterogeneous porous media. This experiment showed that immiscible foams can result in significant incremental oil recovery of 25% to 34% OOIP (over waterflood). In the second approach, foam stabilized using in-situ surface-activated nanoparticles without any surfactant was explored as an EOR agent. The surface chemistry of the hydrophilic nanoparticles was tailored by adsorption of a small amount of short-chain surface modifiers to obtain surface-modified nanoparticles (SM-NP). Foam stabilization using these SM-NP was compared with that using a conventional surfactant to evaluate the potential of these SM-NP to act as an effective foaming agent. Carbonate reservoirs, which are typically highly heterogeneous and oil-wet in nature, pose additional challenges for an effective foam EOR process. Crude oils are typically detrimental to foam stability. An oil-wet carbonate will have a thin oil film on the surface and thus foam lamellae stabilization is challenging. Therefore, wettability-alteration of rock matrix toward water-wet condition using a surfactant is required to favor the in-situ foam stability. This work demonstrated for the first time a synergistic approach of using foams with wettability-altering capabilities for oil-wet systems. It was shown that optimal surfactant formulations can not only alter the wettability of a carbonate core from oil-wet to water-wet conditions, but also can significantly increase the in-situ foam stability even in presence of crude oil. The second half of the dissertation is focused on developing novel microencapsulation techniques using the concept of water-in-air powders for subsurface applications. A facile, one-step method was reported to encapsulate micro- or nano-sized hydrophilic particles using silica nanoparticles. The encapsulated particles can be released based on an external stimulus, such as a change in pH of the external continuous phase. The use of this novel carrier system was demonstrated for the delayed release of PPG particles for conformance control. The application of this technology was then explored for microencapsulating highly concentrated acids (~10 wt.% HCl) for acid treatment of shales. The advantages of these novel acid-in-air powders over conventional acid-in-oil emulsions (which are typically used for shale acidization processes) were illustrated in terms of the thermal stability, corrosion inhibition efficiency, and shale surface reactivity.Item Spatial modeling and uncertainty analysis for subsurface feature mapping : integration of geostatistical concepts and image-based machine learning model validation(2023-08) Ksiazek, Blazej; Pyrcz, MichaelSpatial modeling of subsurface features and uncertainty analysis plays a pivotal role in the integration of data analytics and machine learning techniques in the petroleum industry. As the energy landscape is always changing, and new technologies are emerging, the demand for accurate assessments of uncertainty to inform high-value decision-making is of utmost importance. Nonetheless, the same longstanding methods are used due to their simplicity and the lack of immediate necessity for change. However, with improvements and the implementation of proper workflows, the current methods for calculating uncertainties and validating machine learning models can be more effectively addressed. We developed multiscale methods for data analytics and machine learning. These approaches integrate geostatistical concepts to enhance the precision and reliability of subsurface modeling techniques. We address the challenge of integrating multiple datasets with varying accuracies and volume support sizes. We emphasize the importance of accounting for different sources of uncertainty in spatial modeling workflows. Leveraging geostatistical concepts, such as semivariograms, and dispersion variance, a novel approach is introduced to calculate a more precise measure of error when imputing smaller scale datasets with larger scale datasets. This refined measure of error allows for the direct integration of these datasets in spatial modeling workflows. Once all the uncertainty in our models is accounted for, we must check if our models are accurate. Therefore, we focus on the validation of machine learning models, particularly those tailored for image data. Image-based models often necessitate pre-processing steps, such as resizing and augmentation, to improve data quality for training. To ensure the performance and suitability of these models for real-world datasets, proper validation techniques are imperative. We propose integrating the concept of minimum acceptance criteria with the multi-scale Structural Similarity Index (MS-SSIM) for improved model checking. This enables a more accurate evaluation of model performance in reproducing original images and predicting new ones, surpassing conventional approaches such as mean squared error (MSE) and single-scale SSIM. Our multiscale approaches for data analytics and machine learning establish a comprehensive framework for addressing uncertainty and validating image-based models. The incorporation of geostatistical principles in calculating uncertainty and proper selection criteria for image-based model validation are showcased on subsurface data; however, they are versatile and applicable across various domains. Ultimately, they contribute to the safe and effective deployment of machine learning models for spatial modeling, advancing the field towards more reliable and informed decision-making.