The Impact of Dissolved Organic-Carbon Type on the Extent of Reservoir Souring
Bibliographic record
Abstract
Abstract A reservoir souring forecasting model was presented previously. This model utilized a generic algorithm to determine, via history match, the extent of H2S biogeneration by sulfate-reducing bacteria (SRB) within the waterflooded reservoir such that the production to the surface could be forecasted. The generic algorithm assumed a decline in SRB-usable organic nutrients as a function of water flow within the reservoir. However, nutrient utilization by non-SRB (e.g., nitrate-reducing bacteria) could not be differentiated from the SRB with this model. The current study has incorporated stoichiometry of microbial sulfate and nitrate reduction utilizing both volatile fatty acids (VFA) and BTEX components as dissolved organic carbon (DOC) substrates. This paper presents the updated algorithms and discusses partitioning of the DOC components between oil and water within the reservoir. VFAs such as acetate have historically been assumed to be the favored nutrient source by SRB, but recent field experience has suggested that other DOC sources are contributors. BTEX components, especially toluene, are shown with the model to potentially have a large impact on souring in spite of their limited solubility in the reservoir water. While nitrate usage to control reservoir souring is becoming a "standard" practice, H2S generation in reservoirs with conditions that support BTEX as an SRB nutrient might not be sufficiently inhibited with nitrate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".