Microbial EOR—Critical Aspects Learned From The Lab
Bibliographic record
Abstract
Abstract DuPont and BP have been working together to develop Microbial EOR targeted at viscous oil in the Schrader Bluff formation on the North Slope of Alaska. The goal of this program was a 5% increase in the recovery factor. Mechanisms to be assessed in the original agreement included Viscosity reduction of the oil by transformation or degradation of heavy components in the oil – thus improving the oil – water mobility ratio.Drastic reduction (to ~<0.01 dynes/cm) in the interfacial tension between water and the oil After extensive fundamental research we have learned many critical aspects of microbial EOR that made the application of these two mechanisms to the Schrader Bluff formation impractical. Instead, we have demonstrated two site appropriate mechanisms that achieved, in the lab, the targeted increase in the recovery factor. Improved flow conformance and increased sweep efficiency by preferential plugging of high permeable zones thereby forcing water to produce oil from previously unswept parts of the reservoir.Reduced oil / rock surface tension and a subsequent reduction in the oil "wetting" the rock. This results in changes in the relative permeability of the oil and the water and ultimately lower residual oil saturation. This paper describes the key laboratory tests used to evaluate these four mechanisms. The cornerstones of our work have been the detailed characterization of the waters, the oil, the formation matrix and the microbial community. In addition we describe our search for useful microbes isolated from a variety of environmental samples collected from the Milne Point Unit (MPU) of the Alaskan North Slope. These samples were taken over several years and included injection, production and power fluid waters. These samples were used to understand the temporal changes in the microbial populations and to provide inoculum for our enrichment cultures. 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 discribed below 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.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".