Lessons Learned From Applications of a New Organic-Oil-Recovery Method That Activates Resident Microbes
Bibliographic record
Abstract
Summary Using a breakthrough process, which does not require microbes to be injected, more than 100 microbial enhanced-oil-recovery (MEOR) treatments were conducted from 2007 to the end of 2010 in oil-producing and water-injection wells in the United States and Canada. On average, these treatments increased oil production by 122%, with an 89% success rate. This paper reviews the MEOR process, reviews the results of the first 100+ treatments, and shares what has been learned from this work. Observations and conclusions include the following: Screening reservoirs is critical to success. Identifying reservoirs where appropriate microbes are present and oil is movable is the key. MEOR can be applied to a wide range of oil gravities. MEOR has been applied successfully to reservoirs with oil gravity as high as 41° API and as low as 16° API. When microbial growth is appropriately controlled, reservoir plugging or formation damage is no longer a risk. Microbes reside in extreme conditions and can be manipulated to perform valuable in-situ "work." MEOR has been applied successfully at reservoir temperatures as high as 200°F and salinities as high as 140,000 ppm total dissolved solids (TDS). MEOR can be applied successfully in dual-porosity reservoirs. A side benefit of applying MEOR is that it can reduce reservoir souring. An oil response is not always observed when treating producing wells. MEOR can be applied to many more reservoirs than thought originallys with little downside risk. This review of more than 100 MEOR well treatments expands the types of reservoirs in which MEOR can be applied successfully. Low-risk and economically attractive treatments can be accomplished when appropriate scientific analysis and laboratory screening are performed before treatments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".