The Effectiveness of MEOR Permeability Modification Beyond the Well Bore
Bibliographic record
Abstract
Abstract Biofilms produced from microbes have been shown to reduce the permeability of high perm streaks which can result in improved sweep efficiency. The rate of permeability modification is very reproducible but can vary depending on the specific treatments used. Our approach has been to inoculate the reservoir with a microbe that under the optimal nutrient conditions will express a biopolymer as a film, reduce the size of pore throats and reduce the apparent permeability. The microbe and the nutrients are tailored to the conditions of each reservoir thus giving MEOR the greatest chance for success. This paper presents data that shows modification of permeability can be accomplished away from the injector well bore by microbial production of a biofilm using a proprietary feed protocol (8). These lab scale experiments show that it is possible to do MEOR without affecting the near well bore region of an injector well. A unique set of slim tube experiments are described and an outline of the experimental procedure is provided. This work is a continuation of tests described in earlier papers (SPE129657, SPE146483 and SPE159128 – references 4, 5 and 6). The zone of permeability modification was measured in a specially designed hydraulically constrained and segmented slim tube. The permeability was modified in later segments of the slim tube using a proprietary protocol. This demonstrates the ability to modify the permeability and control its placement well beyond the injector well bore where it can have a greater effect and avoid near well bore formation damage. In other tests, this permeability modification was shown to change composite slim tubes with high permeability contrast to a system of slim tubes that showed no permeability contrast.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".