Considerations for Field Implementation of Microbial Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract For the last 6 years DuPont with different partners has done extensive fundamental research into the application of Microbial Enhanced Oil Recovery technology (MEOR). We have demonstrated two mechanisms that have shown in the lab, more than a 10% increase in the recovery factor. Increased sweep efficiency by plugging of high permeable zones thereby forcing water to produce oil from previously unswept parts of the reservoir. Reduced oil / rock surface tension resulting in lower residual oil saturation. This paper describes the key laboratory tests and preliminary field data used to evaluate these two mechanisms. Our approach has been to inoculate the reservoir with a microbe that under the optimal nutrient conditions will expressed the needed function -- bioplugging or reduced oil saturation. The microbe and the nutrients are tailored to the conditions of each reservoir thus giving MEOR the greatest chance for success. This paper presents challenges that were raised as a result of extensive lab work that are relevant to the implementation of MEOR on a field level. Our ongoing research has provided many insights into the appropriate application of microbial EOR. The unique aspects of each production area, the nature of the oil, the water, the formation matrix, and the background microbial population and their complex interactions must all be assessed when considering the potential application of microbial EOR. The amount of work described for assessing potential MEOR mechanisms is extensive. However, this process has been streamlined and we have been able to assess new target reservoirs for potential MEOR treatments in about 6 months. We believe that Microbially Enhanced Oil Recovery has the potential to improve the recovery of oil with the potential for very low capital investment and a much smaller environmental footprint compared to other EOR techniques.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".