Modelling Reservoir Souring Mitigation Strategy Based on Dynamic Microorganisms Interactions

Abstract

Abstract Managing reservoir souring is on of the challenge in oil and gas industry, especially fields without previous records of H2S productions. Due to activities such as waterflooding, reservoirs’ conditions were changed, which indirectly inducing the environment to start producing H2S gas. In low temperature fields, main contributor to the H2S production was identified as biogenic process, where microorganisms catalyzed the sour gas production. Conventionally, sulphate reducing microorganism was always blamed as the culprit in contributing towards H2S production. However, abundance of literatures discussed about contribution of other microorganisms towards souring processes. Due to the complexity of their interactions, current approach to treat or control biogenic souring became one of the most challenging issues. This study will focus on the laboratory studies of sulphide production by microorganisms and modelling various microorganisms interactions towards chemical treatment introduced to mitigate it. Started with microorganisms sampling from fields with high SRB, the samples were then enriched in the laboratory. To identify microorganismss from samples, cultures were sent for PCR and DNA sequencing. Based on the results, microorganisms were profiled. Batch test were conducted by dosing pre-determined dosage of biocide and nitrate. Production of sulphide were monitored up to 92days. Based on the sulphide production, effectiveness of the treatments were determined. A model, which previously developed to determine the potential of reservoir souring, enhanced with addition of dynamic interaction of microorganisms. Factors such as nutrients, type of microorganisms, treatment chemicals, and their byproducts contributed towards the model. microorganisms. In the batch test, chemicals were dosed once into culture. Results obtained shows that nitrate treatment suppressed the sulphide production for ashort term period, where after the nitrate depleted, the number of microorganisms and sulphide productions were bounced back. Biocidetreatment, in contrast, generally suppressed all microorganisms in the cultures, effectively control the microorganisms number and maintaining low sulphide production for the entire duration of the experiment. The model that being developed in this study tested with synthetic data that mimick to field conditions, type of microorganisms and chemical treatments to observe their output pattern. It was found that the pattern output from the synthetic data matched with experimental results, which shows the model was sensitive and reliable to model the mitigation and control strategy for biogenic reservoir souring. The model based on dynamic interactions of microorganisms towards chemical treatments (biocide and/or nitrate) is the novel element in this study. Past studies were always focus on single population model, which SRB is the main input for the model, while this study enhanced its accuracy by introducing multi-population factor.