Biodegradation of Polymers Used in Oil and Gas Operations: Towards Enzyme Biotechnology Development and Field Application
Bibliographic record
Abstract
Linear and crosslinked polymers are commonly used in the oil and gas industry. Guar-derived polymers have been extensively utilized in hydraulic fracturing processes, and recently polyacrylamide and cellulose-based polymers have also found utility. As these polymers are used during various phases of the hydraulic fracturing process, they can accumulate at formation fracture faces, resulting in undesired filter cakes that impede oil and gas recovery. Although acids and chemical oxidizers are often added in the fracturing fluids to degrade or 'break' polymer filter cakes, the constant use of these chemicals can be hazardous and can result in formation damage and corrosion of infrastructure. Alternately, the use of enzymes is an attractive and environmentally friendly technology that can be used to treat polymer accumulations. While guar-linkage-specific enzyme breakers isolated from bacteria have been shown to successfully cleave guar-based polymers and decrease their molecular weight and viscosity at reservoir conditions, new enzymes that target a broader range of polymers currently used in hydraulic fracturing operations still require research and development for effective application. This review article describes the current state-of-knowledge on the mechanisms and enzymes involved in biodegradation of guar gum, polyacrylamide (and hydrolyzed polyacrylamide), and carboxymethyl cellulose polymers. In addition, advantages and challenges in the development and application of enzyme breaker technologies are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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 | 0.002 |
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".