Microbial enhanced oil recovery : a pore-scale investigation of interfacial interactions

Current oil production technologies recover only about one‐third to one‐half of the oil originally present in an oil reservoir. Given current oil prices, even a modest increase in oil recovery efficiency is fiscally attractive. One novel approach to increase oil recovery efficiency is a process called microbial enhanced oil recovery (MEOR), where microorganisms are either used as a clogging agent to redirect flow or to produce biosurfactant that reduces interfacial tension. This dissertation aims to understand the MEOR pore‐scale mechanisms relevant to oil recovery by taking a two‐fold approach where transparent 2‐dimensional micromodel experiments imaged with stereo microscopy and 3‐dimensional column experiments imaged with x‐ray computed microtomography (CMT) are utilized. Micromodel experiments allow for direct visualization of the biological phase (i.e. biofilm), however, only 2‐dimensional information is provided. Conversely, CMT experiments provide 3‐dimensional pore‐scale information, but lack the ability to image the biological phase. With this two‐fold approach, it is possible to distinguish multiple fluid interfaces, quantify fluid phase saturations, measure oil blob size distributions, and visualize the biological phase. Furthermore, a method to measure interfacial curvature from 3‐dimensional images is developed, providing researchers a new perspective from which to study multiphase flow experiments. Overall, the presented research utilizes pore‐scale imaging techniques to study the interfacial interactions occurring during MEOR in an effort to better explain the physics, and thus, increase the efficacy of MEOR

Investigating the pore-scale mechanisms of microbial enhanced oil recovery

Microbial Enhanced Oil Recovery (MEOR) is a process where microorganisms are used for tertiary oil recovery. Numerous mechanisms have been proposed in the literature through which microorganisms facilitate the mobilization of residual oil. Herein, we focus on the MEOR mechanisms of interfacial tension reduction (via biosurfactant) and bioclogging in water-wet micromodels, using Shewanella oneidensis (MR-1) that causes bioclogging and Bacillus mojavensis (JF-2) that produces biosurfactant and causes bioclogging. Micromodels were flooded with an assortment of flooding solutions ranging from metabolically active bacteria to nutrient limited bacteria to dead inactive biomass to asses the effectiveness of the proposed MEOR mechanisms of bioclogging and biosurfactant production. Results indicate tertiary flooding of the micromodel system with biomass and biosurfactant was optimal for oil recovery due to the combined effects of bioclogging of the pore-space and interfacial tension reduction. However, biosurfactant was able to recover oil in some cases dependent on wettability. Biomass without biosurfactant that clogged the pore-space also successfully produced additional oil recovery. When analyzing residual oil blob morphology, MEOR resulted in oil blob size and radius of curvature distributions similar to those obtained by an abiotic capillary desaturation test, where flooding rate was increased post secondary recovery. Furthermore, for the capillary number calculated during MEOR flooding with bioclogging and biosurfactant, lower residual oil saturation was measured than for the corresponding capillary number under abiotic conditions. These results suggest that bioclogging and biosurfactant MEOR is a potentially effective approach for pore morphology modification and thus flow alteration in porous media that can have a significant effect on oil recovery beyond that predicted by capillary number.Keywords: bioclogging, micromodel, multiphase flow, microbial enhanced oil recovery, water flooding, biosurfactant, interfacial curvatur

DeepAngle: Fast calculation of contact angles in tomography images using deep learning

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle)

Using synchrotron-based X-Ray microtomography and functional contrast agents in environmental applications

Despite very rapid development in commercial X-ray tomography technology, synchrotron-based tomography facilities still have a number of advantages over conventional systems. The high photon flux inherent of synchrotron radiation sources allows for (i) high resolution to micro- or nanometer scales depending on the individual beamline, (ii) rapid acquisition times that allow for collection of sufficient data for statistically significant results in a short amount of time as well as prevention of temporal changes that would take place during longer scan times, and (iii) optimal implementation of contrast agents that allow us to resolve features that would not be decipherable in scans obtained with a polychromatic radiation source. This chapter highlights recent advances in capabilities at synchrotron sources, as well as implementation of synchrotron-based computed microtomography (CMT) to two topics of interest to researchers in the soil science, hydrology, and environmental engineering fields, namely multiphase flow in porous media and characterization of biofilm architecture in porous media. In both examples, we make use of contrast agents and photoelectric edge-specic scanning (single- or dual-energy type), in combination with advanced image processing techniques

GRB 021004: A Possible Shell Nebula around a Wolf-Rayet Star Gamma-Ray Burst Progenitor

The rapid localization of GRB 021004 by the HETE-2 satellite allowed nearly continuous monitoring of its early optical afterglow decay, as well as high-quality optical spectra that determined a redshift of z3=2.328 for its host galaxy, an active starburst galaxy with strong Lyman-alpha emission and several absorption lines. Spectral observations show multiple absorbers at z3A=2.323, z3B= 2.317, and z3C= 2.293 blueshifted by 450, 990, and 3,155 km/s respectively relative to the host galaxy Lyman-alpha emission. We argue that these correspond to a fragmented shell nebula that has been radiatively accelerated by the gamma-ray burst (GRB) afterglow at a distance greater than 0.3 pc from a Wolf-Rayet star progenitor. The chemical abundance ratios indicate that the nebula is overabundant in carbon and silicon. The high level of carbon and silicon is consistent with a swept-up shell nebula gradually enriched by a WCL progenitor wind over the lifetime of the nebula prior to the GRB onset. The detection of statistically significant fluctuations and color changes about the jet-like optical decay further supports this interpretation since fluctuations must be present at some level due to inhomogeneities in a clumpy stellar wind medium or if the progenitor has undergone massive ejection prior to the GRB onset. This evidence suggests that the mass-loss process in a Wolf-Rayet star might lead naturally to an iron-core collapse with sufficient angular momentum that could serve as a suitable GRB progenitor.Comment: Replaced with version accepted by ApJ; 40 pages, 9 figure

Effective permeability of an immiscible fluid in porous media determined from its geometric state

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to underground $CO_2$ storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with $R^2 = 0.98$.Comment: 6 Pages, 2 Figures, and Supporting Materia