Reservoir souring: sulfur chemistry in offshore oil and gas reservoir fluids
Bibliographic record
Abstract
The injection of sulfate-containing seawater into an oil reservoir, for maintaining the reservoir pressure, can promote the growth of sulfate reducing bacteria and archaea near the injection wells, leading to the formation of sulfides such as hydrogen sulfide. However, intermediate sulfur species with different valence states, such as polythionates and polysulfides have been detected in several produced water samples, likely a result of phase partitioning, and chemical and microbial reactions. These sulfur species could affect the microbial communities (e.g., microbially influenced corrosion) and will impact the efficiency of souring mitigation methods. In addition, the presence of these sulfur species can result in operational, environmental, and treatment problems. Therefore, development and implementation of souring control strategies during production cycle of oil and gas reservoirs require identifying the origins, reactivity, and the partitioning behaviour of these compounds. This paper presents an overview of the known mechanisms responsible for reservoir souring and then focuses on the chemical reactions and sulfur species associated with production and consumption of hydrogen sulfide. In this work we highlight complexity of the sulfur chemistry and that the assumption that all the sulfate is reduced to hydrogen sulfide can lead to inappropriate souring management methods. The paper also reviews the detection and analysis methods used for sulfur compounds. The review demonstrates that there is a gap in the current souring models and methods due to the exclusion of key sulfur compounds and challenges in identifying and quantifying these compounds with respect to speed of analysis and sample stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".