Application of Thermophilic Microbes In Waxy Oil Reservoirs at Elevated Temperature
Bibliographic record
Abstract
Abstract For the last decade, some specifically selected microbes have been successfully used to improve oil recovery in some waxy or heavy oil reservoirs. The main feature of this new technology is that the applied microbes can degradate heavy oil components to improve oil properties, for example, reduction in oil viscosity or cloud pour point. However, successful application of microbial technology in high temperature reservoirs has been remaining a challenge due to quick disactivate of microbes at elevated temperature. In this work, we present the result of application of self-incubated thermophilic microorganisms that are growing at a reservoir temperature of 73°C.This microbes can self-colony and quickly grow up at the reservoir conditions. The analysis of the crude oil on samples before and after treated with microorganisms showed that (1) oil viscosity decreased by 34%, (2) pour point decreased 3°C, (3) the weigh of heavy oil components decreased significantly, and (4) the interfacial tension between oil and formation water decreased about 31%. In addition, the pH value of the crude oil samples decreased from 7.3 to 5.5. The results from coreflooding tests showed that the oil recovery could be improved by 6.6%(OOIP) by injection the microbes. We also performed a‘ huff and puff pilot test in a production well that having 91% of water cut before the test. The results showed that after the well was stimulated with the microbes, the oil production rate increased from 20 to 40 barrels per day, and the water cut decreased from 91% to 87.8%. The effect of the stimulation with the microbes continued more than 6 months and the total oil incremental was than 2400 barrels.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".