Recent Successes: MEOR Using Synergistic H2S Prevention and Increased Oil Recovery Systems
Bibliographic record
Abstract
Abstract Contemporary investigations of Microbial Enhanced Oil Recovery (MEOR) technologies began in earnest in the 1980’s. Since then, numerous programs have examined the reliability of the concepts, processes, and technical efficacy of recovery biosystems. Despite reported successes, MEOR technologies have not been widely accepted by the oil and gas industry as a means to recover residual oil. The Bio-Competitive Exclusion (BCX) biological process has successfully demonstrated significant oil recovery results in mature waterfloods from the deliberate alteration of indigenous reservoir microflora to an anaerobic denitrifying bacteria (DNB) population through the use of nitrate-based formulae as alternate electron acceptors and microbial nutrient. These environmentally safe inorganic salts complement the naturally occurring volatile fatty acids (VFA) in the reservoir and produced waters, selectively stimulating and increasing the targeted DNB. This designed and controlled manipulation of an indigenous DNB population initiates and increases the production of multiple gas, solvent, and surfactant bio-products which have known and demonstrable oil recovery properties. The BCX technology offers a feasible, practical, and very cost effective, tertiary oil recovery system that concurrently controls reservoir and surface system souring. Field data from California, Oklahoma, China, and Canada are presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".