Bibliographic record
Abstract
Abstract The impetus for this work stemmed from the growing demand in the industry for Carbon Dioxide sequestration during incremental oil recovery and gas or solvent injection for heavy oil recovery from naturally fractured reservoirs. A thorough research and assimilation of available field data led us to believe that a better understanding of the diffusion coefficients and their impact on the overall recovery will go a long way in providing viable techniques in the Improved Oil Recovery sector. This knowledge will further enhance the understanding of the mass transfer process and mechanisms at a molecular scale in the reservoirs. Remarkable amount of experimental and computational work has been carried out to study the effect of diffusion and dispersion mechanisms in naturally fractured reservoirs. However, what's missing is an extensive and critical review of the efforts in this direction. The synchronization of similar efforts and recognition of all the independent studies in a common direction would not only highlight the deficiencies and limitations of present scenario in the field, it will also give meaningful insight into further developments. We have investigated methods employed by various authors to arrive at concurrent values for diffusion and dispersion coefficients. The main focus during our work was to, i) evaluate the role of diffusion and the factors that critically affect diffusion and oil recovery, ii) investigate current methods for calculation of the coefficients and iii) various methods/models to incorporate diffusion. Our work includes significant technological contributions and findings of experimental and simulation studies in the field to this day. It is our understanding that our research will bridge the gap between the fundamental understanding and application of related processes and serve as a useful reference for engineers and researchers for present applications as well as the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".