Numerical Prediction of Reservoir Souring under the Effect of Temperature, Ph, and Salinity on the Kinetics of Sulfate-Reducing Bacteria

Abstract

This paper presents an improved numerical model to predict 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 sulfate-reducing bacteria (SRB), which are responsible for hydrogen sulfate generation. Furthermore, we investigate the influence of the environmental factors on the prediction of reservoir souring. First, we model the mechanisms involved in reservoir souring and then identify the parameters that have the crucial effects on the prediction. Previous models assumed that the maximum growth rate is independent and represented by a constant value. They also neglected the competition between different types of substrates. Therefore, we introduce a new mathematical model to express the maximum growth rate of SRB as a function of temperature, salinity, and pH in the first-order Monod kinetics equation. Then, we discuss the modeling of the competition between different types of substrates. Finally, we implement the model into a 3D finite difference non-isothermal reservoir simulator. The new model results are compared to previous model results and validated against experimental data. Next, a comprehensive sensitivity analysis is performed to show the effect of temperature, salinity, and pH on bacterial growth and the prediction of souring in a numerical reservoir (or core) model. Taking the effect of environmental parameters into account shows the deficiency of previous models to estimate the reservoir souring. Previous models underestimate the concentration of hydrogen sulfate generation per mass of biodegraded substrate (H2S generation coefficient). The results suggest that the hydrogen sulfide concentration increases as the reservoir temperature, salinity, and pH reach the optimum temperature, pH, and salinity required for SRB to reach its maximum growth rate. The present work differs from earlier works by explicitly determining parametric values required for a kinetic model. This model is as an effective approach to accurately predict the concentration of hydrogen sulfide and thus facilitating accurate decisions on remedial actions.

Microbial Sulfate Reduction and Perchlorate Inhibition in a Novel Mesoscale Tank Experiment

“…Numerical models are useful tools in exploring questions related to oil reservoir souring. Process-based models differentiate the key governing processes and provide systematic views that integrate disparate processes and data sets. ,− A multiphase/component general-purpose adaptive reservoir simulator (GPAS) was developed to simulate souring and nitrate treatment at both the column and field scales . Reactive transport models of perchlorate treatment were developed with data from column experiments to gain insight into the underlying competing mechanisms controlling the action of perchlorate. , In all, most modeling studies on souring and perchlorate treatment to date focused on the representation of biogeochemical processes in systems with relatively simple flow and transport.…”

Assessment of the Biogenic Souring in Oil Reservoirs Under Secondary and Tertiary Oil Recovery

“…A summary of these attempts is given in Table 3. Numerical simulators UTCHEM and TOUGH2 were adapted successfully for modelling the various aspects of H2S formation and transport at laboratory scale [57][58][59]. Field modelling efforts are given by various authors starting with [48].…”

Assessment of the Biogenic Souring in Oil Reservoirs under Secondary and Tertiary Oil Recovery

“…A summary of these attempts is provided in Table 3. Numerical simulators UTCHEM and TOUGH2 were adapted successfully for modeling the various aspects of H2S formation and transport at the laboratory scale [57][58][59]. Field modeling efforts are provided by various authors starting with [48].…”