The Laboratory Evaluation of Seawater Injection on H2S Production, Incorporating Several Different Treatment Strategies, Utilizing Fixed Film Upflow Bioreactors
Bibliographic record
Abstract
Abstract Reservoir Souring is the unplanned production of increased concentrations of hydrogen sulfide (H2S) in well-stream fluids from production wells that are subjected to water-injection. The consequences of souring with respect to safety, corrosion and environmental risk can be significant. This is typically associated with the activity of a specialized group, the Sulfate-reducing bacteria (SRB). However, in recent years, various other micro-organisms are believed to be involved in souring, e.g. Sulfate reducing archaea (SRA). In this study, fixed film up flow bioreactors (FFUBR) were utilized to assess the potential for H2S production or changes in such H2S production, when seawater is injected into a North Sea oil reservoir. The study has demonstrated how changes in fundamental parameters (e.g. bacterial nutrients, shut-in periods) can impact sulfide production and alter the microbial communities. The FFUBR’s were soured to create a ‘worst case’ scenario and different nutrient additions or remediation treatments were applied to represent either near injection wellbore or deep field conditions. Typical oil field practice is to measure H2S in the gas phase. Partition modelling of H2S between water, oil and gas phase was applied to the measured sulfide data to give a real-world indication of the effect of H2S in gas when resuming production following a shut-in. The following parameters were measured during the testing period: sulfide generation, volatile fatty acid organic carbon sources (VFA), iron, nitrate and nitrite concentrations. The microbiology of the system was evaluated both by traditional culture techniques and molecular methods, such as fluorescence in situ hybridization (FISH) analysis and other DNA-based analysis. Results indicate that when sulfide generation had reached 1.5 mM, and the nutrient source was changed, almost complete cessation of sulfide generation resulted for a period of 7 days. Whereas, following shut-in period, sulfide generation recommenced after re-starting the flow and reached a concentration of 4.4 mM immediately and rose even higher to 5.0 mM over the first days of flow. However, sulfide concentrations returned to 2.0 mM again within 7 days after restart. However, the changes in the microbial community were found to be somewhat selective to certain SRB families. The various effects of the different treatments and conditional changes are discussed further in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".