Simulation of Bacterial Souring Control in an Alberta Heavy-Oil Reservoir
Bibliographic record
Abstract
Summary This paper presents the development of a simulation model describing the bacterial-induced souring of an Alberta heavy-oil producing field and its remediation via injection of nitrate. An area of the field with extensive bacterial activity was selected for the simulation study. The primary production and water-flood injection phases were history matched with basic reservoir maps and injection/production operating conditions adjusted via an automatic history match algorithm. Extensive chemical and microbiological compositional data for injected and produced waters were collected and analyzed at the University of Calgary and a mathematical model of the souring process and its remediation treatment was developed based on the information collected. Simulation indicated the volumetric distribution of the hydrogen sulfide over time and how the injected nitrate was distributed and acted to achieve souring control. Predicted individual production well responses were shown to be consistent with field observations, and issues regarding improved monitoring and design of laboratory experiments for future field operations are highlighted. Hence, simulation can be a useful tool in understanding and designing remedial treatments to bacterial souring in the Western Canada Sedimentary Basin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".