Field Implementation of DuPont's Microbial Enhanced Oil Recovery Technology
Bibliographic record
Abstract
Abstract For the last 7 years DuPont with different partners has done research into the application of Microbial Enhanced Oil Recovery technology (MEOR). In laboratory tests, we have observed in excess of 15% increased recovery factor. This exceeded our expectations. In a field test, described in this paper, we have observed a ~15–20% increase in production rate. After extensive fundamental research (5) we have learned many critical aspects of microbial EOR. We have demonstrated two mechanisms that exceeded, in the lab, the targeted increase in the recovery factor. Improved 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 a change in the wettabilty of the rock and lower residual oil saturation. This paper describes the field data used to demonstrate the effectiveness of the improved sweep efficiency by using a microbe to plug high permeable zones in a target reservoir - called bioplugging. Our approach has been to inoculate the reservoir with a microbe that under the optimal nutrient conditions will express the needed function - in this case bioplugging for improved sweep efficiency. The microbe and the nutrients are tailored to the conditions of each reservoir thus giving MEOR the greatest chance for success. We have tested the efficacy of the microbial treatment with a series of slim tube tests and interwell tests. Our experience with field implementation has taught us important lessons on how to inoculate and feed microbes in an oil reservoir at a scale needed to support the commercial implementation. Issues that had to be address include assuring the effectiveness of the treatment using realistic lab tests, assuring that the treatments do not bypass the reservoir, assuring that the reservoir is not blinded by the inoculation, and understanding the effects of biocides and corrosion inhibitors that are commonly used in the oil field. Production data for the pilot test has shown a change in the decline curve indicating a significant increase in oil production rate and a corresponding decrease in water cut. Oil production has increased in the field by 15 to 20% with a corresponding reduction in water cut. 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 quickly. We believe that Microbial Enhanced Oil Recovery has the potential to improve the recovery of oil with very low capitol 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".