Interfacial sciences in unconventional petroleum production: from fundamentals to applications
Bibliographic record
Abstract
With the ever increasing demand for energy to meet the needs of growth in population and improvement in the living standards in particular in developing countries, the abundant unconventional oil reserves (about 70% of total world oil), such as heavy oil, oil/tar sands and shale oil, are playing an increasingly important role in securing global energy supply. Compared with the conventional reserves unconventional oil reserves are characterized by extremely high viscosity and density, combined with complex chemistry. As a result, petroleum production from unconventional oil reserves is much more difficult and costly with more serious environmental impacts. As a key underpinning science, understanding the interfacial phenomena involved in unconventional petroleum production, such as oil liberation from host rocks, oil-water emulsions and demulsification, is critical for developing novel processes to improve oil production while reducing GHG emission and other environmental impacts at a lower operating cost. In the past decade, significant efforts and advances have been made in applying the principles of interfacial sciences to better understand complex unconventional oil-systems, while many environmental and production challenges remain. In this critical review, the recent research findings and progress in the interfacial sciences related to unconventional petroleum production are critically reviewed. In particular, the chemistry of unconventional oils, liberation mechanisms of oil from host rocks and mechanisms of emulsion stability and destabilization in unconventional oil production systems are discussed in detail. This review also seeks to summarize the current state-of-the-art characterization techniques and brings forward the challenges and opportunities for future research in this important field of physical chemistry and petroleum.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".