Further model development and application of UTCHEM for microbial enhanced oil recovery and reservoir souring
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This research presents an improved simulator to predict the enhanced oil recovery after applying microbial enhanced oil recovery (MEOR) technique and the onset of reservoir souring in sea-water injected reservoirs. The model is developed to study the effect of temperature, salinity, and pH on the growth of bacteria which are responsible for producing in-situ bioproducts in MEOR and causing microbial reservoir souring. The effects of environmental factors (i.e., pH, salinity, and temperature) are implemented into a four-phase chemical flooding reservoir simulator (UTCHEM). In the MEOR process, nutrients and natural bacteria are injected into a reservoir and both indigenous and injected microorganisms are able to react and then generate bioproducts based on in-situ reactions. In this study, we considered three different mechanisms proposed for MEOR: biosurfactant-dominated MEOR, biopolymer-dominated MEOR, and biomass-dominated MEOR. Results show that in-situ bioproduct generation rates can be thoroughly modeled based on environmental factors. Simulation results show 10-15% incremental oil recovery using in-situ biosurfactant compared to waterflooding, biopolymer can increase the oil recovery by 3%, and biomass can contribute to oil production by increasing the recovery by 6%. The simulation results show that nutrient concentration, salinity, and temperature are the most significant parameters influencing oil recovery, whereas pH has an insignificant effect. Reservoir souring is a phenomenon that occurs because of in-situ biodegradation reactions and is modeled in the present study. Sulfate-reducing bacteria (SRB) can convert sulfate ions into hydrogen sulfide by oxidizing a carbon source. This phenomenon is called reservoir souring when it occurs in water-flooded reservoirs. The generated H2S content affects the properties of rocks, reduces the value of produced hydrocarbon, causes corrosion in production facilities, and has health and safety issues. Because of the severity of the problem, several attempts have been made to model and predict the onset of souring. However, there are high uncertainties because of many inestimable and uncertain parameters (e.g., biodegradation parameters, sulfate concentration, reservoir pH, salinity, and temperature). Therefore, the capability of UTCHEM for calculating the maximum growth rate in terms of temperature, salinity, and pH helped us to show the environmental effect on the process. We also investigated the effect of maximum growth rate and available sulfate on the biodegradation process that leads to reservoir souring. In summary, our results show that the microbial reservoir souring process can be modeled based on environmental factors. More importantly, the results show the high sensitivity of the process to different parameters.