1D Simulations for Microbial Enhanced Oil Recovery with Metabolite Partitioning

Enhanced Oil Recovery by Potential Biosurfactant-Producing Halo-thermotolerant Bacteria Using Soil Washing and Sand-Packed Glass Column Techniques

Biosurfactants offer numerous advantages over the chemical surfactants, especially in energy and environment-related applications. Microbial enhanced oil recovery (MEOR) is a technique to recover oil from reservoirs by using microbes and their metabolites. In present study, total sixteen morphologically distinct bacterial strains isolated from different salty areas of the district Khairpur Mir's, Pakistan, were investigated for their MEOR potential. Screening assays for thermotolerance and halotolerance declared 7 out of 16 (43.75%) bacterial isolates as thermotolerant (capable of growing in the temperature range 60-70 °C) and halotolerant (tolerating NaCl concentrations up to 17%, w/v). Moreover, five of them were screened as biosurfactant producers. Among, the lowest surface tension reduction was achieved with biosurfactants produced by the strains KJ2MO (27.8 mN/m) and KJ2SK (29.3 mN/m). The biosurfactant activity was found stable at temperature (100-121 °C, 1 h) and pH (4-10). Moreover, maximum oil recovery was obtained with biosurfactant of bacterial strain KJ2MO (54.7%, 51.25%) followed by KJ2SK (44.7%, 40.5%), KJ1WB (37%, 35.5%) and KJ2MD (37.8%, 31.9%) by using either techniques, i.e., soil washing and sand-packed column, respectively. Moreover, the potent species were identified as Pseudomonas pseudoalcaligenes KJ1WB, Bacillus aerius KJ2MD, Bacillus licheniformis KJ2SK, and Bacillus subtilis KJ2MO using 16S rRNA ribo-typing. The investigated species were found to be promising biosurfactants producers having potential for enhanced oil recovery and could be used in other environmental applications like bioremediation.

Potential applications of microbial enhanced oil recovery to heavy oil

Heavy oil accounts for around one-third of total global oil and gas resources. The progressive depletion of conventional energy reserves has led to an increased emphasis on the efficient exploitation of heavy oil and bitumen reserves in order to meet energy demand. Therefore, it is imperative to develop new technologies for heavy oil upgrading and recovery. Biologically-based technology that involves using microorganisms or their metabolites to mobilize heavy oil trapped in reservoir rocks can make a significant contribution to the recovery of heavy oils. Here, the results of laboratory experiments and field trials applying microbial enhanced oil recovery (MEOR) technologies are summarized. This review provides an overview of the basic concepts, mechanisms, advantages, problems, and trends in MEOR, and demonstrates the credibility of MEOR methods for applications in enhanced heavy oil recovery and the petroleum refining processes. This technology is cost-effective and environmentally-friendly. The feasibility of MEOR technologies for heavier oil has not yet been fully realized due to the perceived process complexity and a lack of sufficient laboratory research and field test data. However, novel developments such as enzyme-enhanced oil recovery continues to improve MEOR methods.HighlightsHeavy oil represents the largest known potentially-recoverable petroleum energy resource.Novel biotechnological processes are needed to recover or upgrade heavy oil.Microbial technologies have great potential for heavy oil recovery.Microorganisms can produce metabolic byproducts to mobilize oil trapped in reservoirs.More technological research is needed to develop microbial enhanced oil recovery.

Influence of pH on dynamics of microbial enhanced oil recovery processes using biosurfactant producing Pseudomonas putida: Mathematical modelling and numerical simulation

In present work, the influence of reservoir pH conditions on dynamics of microbial enhanced oil recovery (MEOR) processes using Pseudomonas putida was analysed numerically from the developed mathematical model for MEOR processes. Further, a new strategy to improve the MEOR performance has also been proposed. It is concluded from present study that by reversing the reservoir pH from highly acidic to low alkaline condition (pH 5-8), flow and mobility of displaced oil, displacement efficiency, and original oil in place (OOIP) recovered gets significantly enhanced, resulting from improved interfacial tension (IFT) reduction by biosurfactants. At pH 8, maximum of 26.1% of OOIP was recovered with higher displacement efficiency. The present study introduces a new strategy to increase the recovery efficiency of MEOR technique by characterizing the biosurfactants for IFT_min/IFT_max values for different pH conditions and subsequently, reversing the reservoir pH conditions at which the IFT_min/IFT_max value is minimum.

Stimulation of an indigenous thermophillic anaerobic bacterial consortium for enhanced oil recovery

Production of gases, VFAs, solvents and surfactants was achieved by thermophilic methanogenic consortium TERIL63, showing reduction in surface tension from 69 to 35 dynes cm⁻¹. TERIL63 with an optimized nutrient recipe showed 15.49% EOR at 70 °C in a core flood study.

Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.