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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
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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 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 .Comment: 6 Pages, 2 Figures, and Supporting Materia
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