Bio‐enhanced oil recovery ( <scp>BEOR</scp> ) methods: All‐important review of the occasions and challenges
Bibliographic record
Abstract
Abstract Bio‐enhanced oil recovery (BEOR) is an advanced and innovative approach in the oil industry that could be a potential solution to increase oil production from existing reservoirs using technologies based on biological methods. Nevertheless, there has been a lack of comprehensive reviews that elucidate the various aspects and potential of different BEOR methods and processes. This review summarizes the recent progress of various methods employed in BEOR, exploring their applications and highlighting their distinct advantages. BEOR employs different techniques, including microorganisms and biomicrobes microbial enhanced oil recovery (MEOR), enzymes, biopolymers, bionanomaterials, alkaline, and biosurfactants to increase oil recovery. Microorganisms contribute significantly to BEOR through metabolic processes that result in the production of gases and acids. The role of enzymes is to enhance the fluid flow and thereby facilitate oil production. Biosurfactants reduce the interfacial tension (IFT) between oil and water and mobilize the trapped oil. Biopolymers are obtained from biological sources such as plants, microorganisms, and algae. Biopolymers can interact with oil, which is well‐used in the process of EOR. Bionano processes represent a fusion of biology and nanotechnology, integrate the advantages of both microorganisms and nanoparticles, and provide a synergic effect for EOR. The BEOR revealed an attractive potential to be an effective approach to maximizing the oil recovery considering environmental, economic productivity, and sustainability issues. Also, this review encourages further studies and development in this field for fully exploiting BEOR's capacity and meeting the ever‐increasing needs of energy resources with a sustainable viewpoint.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".