Souring Remediation by Field-wide Nitrate Injection in an Alberta Oil Field
Bibliographic record
Abstract
Abstract When oil is produced by water injection, sulphide formation (souring) can be stimulated. Souring is often caused by sulphate-reducing bacteria (SRB) which oxidize organic carbon (oil organics) with sulphate in the injection water to CO2 and sulphide. As a result, H2S concentrations in the produced water, oil and gas gradually increase. A consequence can be that the piping infrastructure must be redesigned from sweet to sour service. A relatively novel biotechnology aimed to remedy souring is to add nitrate to the injection water. Nitrate tracks the injection water effectively and its cost allows continuous and field-wide treatment. In a field-wide nitrate injection, the injection water (approximately 3,500 m3/day) was amended continuously with 2.4 mM (150 ppm) nitrate. Three points in the injection water system and 12 production wells were monitored by sampling every 2 to 3 weeks. The concentrations of sulphide, sulphate, nitrate, nitrite and ammonia in injection and produced waters were determined, as well as the activities of nitrate-reducing bacteria (NRB). Field-wide nitrate injection gave a 70% drop of aqueous sulphide within the first 5 weeks, after which the concentration recovered somewhat for the next 20 weeks. The activity of NRB increased throughout this period, indicating the possibility of further decreases in souring in the future. Introduction Microbial production of sulphide by sulphate-reducing bacteria (SRB) in oil reservoirs (i.e. souring) often occurs during secondary oil recovery processes when water is injected to maintain reservoir pressure. Souring is largely perceived to have negative effects because dissolved sulphide (HS−) and precipitated metal sulphides (e.g. FeS) are corrosive towards metal pipes and equipment. Injection of suspended metal sulphides may decrease reservoir injectivity by plugging zones near the injection wellbore, decreasing oil production. Suspended metal sulphides also stabilize oil-water emulsions preventing effective separation of produced water and oil. Hence, souring is highly undesirable from a business and operating point of view, especially when the original facilities were not designed to handle sour production. Souring gives rise to safety concerns regarding the exposure of workers in the field to released hydrogen sulphide and to complaints about odours from surface rights owners and, in urban settings, over the threat to real estate values of a sour service operation on or near residential property.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".