Mechanistic Modelling of H2S Souring Treatments by Application of Nitrate or Nitrite
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Bibliographic record
Abstract
Abstract The activity of sulfate-reducing bacteria (SRB) in oil field brines causes numerous problems associated with reservoir souring. Biological solutions based on stimulating nitratereducing bacteria provide novel, inexpensive, and environmentally friendly alternatives to the use of biocides andcorrosion inhibitors. Experiments have been designed to understand and optimize mechanisms associated with this novel treatment and provide a sound technical basis for the application of this technology. Here, appropriate microbial growth and metabolite production kinetic models are employed in the STARS reservoir model to simulate laboratory continuous up-flow packed-bed bioreactor tests. Observed compositional changes along the length of the bioreactor are matched for various injected treatment scenarios. Sensitivity to factors affecting growth and production rates are examined. A stratified, dipping reservoir model with properties typical of North Sea conditions is then investigated as a field rototype. The complications introduced by multiphase flow effects and sweep to the basic process are indicated, and sensitivities to the amount of biodegradable carbon, assumed microbe growth/decay levels, and fractions of sessile/liquidphase bacteria on process evolution are illustrated. The simulations provide a dramatic demonstration of how this novel H2S souring treatment can be applied to field problem wells. Introduction Secondary oil recovery by water flooding (injection of water to maintain pressure and create reservoir sweep) is a common oil recovery technique practiced worldwide with a high percentage of success. Frequently associated with this technology is the problem of reservoir souring whereby H2S is generated following the mixing of sulfate-laden injection waters and in situ sulfate-reducing bacteria (SRB) [1]. In addition to the resulting contamination of produced oil, gas and water, corrosion of wellbores, pipelines, and processing equipment is a common consequence of souring. Because of the great economic consequences of souring, numerous control strategies have been attempted to alleviate this problem. Methods include eliminating sulfate from water prior to injection, using biocides to suppress microbial activity, stripping the H2S by caustic washing and chemical oxidation of H2S to elemental sulfur. More recently, biological approaches have been applied to control souring that involve the stimulation of various nitrate-reducing bacteria which results in souring control according to different mechanisms [2–13] that are described below in detail. Several numerical simulation models at the laboratory and field scale have been developed previously for bacterial enhanced oil recovery methods [14–17]. Numerical models for bacterial souring and treatment are less common, with the models of Ligtheim et al. [18] and Sunde et al. [19] being the best examples to date. All of these models, however, are limited in application and very process specific. In this work we will apply a commercial, fully-featured, thermal-compositional simulator STARS with microbial kinetics capabilities to nitrate- and nitritebased souring control for the first time. Models are based on extensive laboratory experiments performed at the University of Calgary [10–13]. Section II summarizes work that has contributed towards understanding souring mechanisms. Section III turns to a description of the numerical model used to match a particular set of these experiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it