Feasibility of Air Injection in a Light Oil Field of Western India
Bibliographic record
Abstract
Abstract Overall field settings like dip of 80, partial water drive, permeability of less than 50md and with 350 API oil make Field - A in Western India an ideal choice for gas injection. Non-availability of hydrocarbon / non-hydrocarbon gas makes air injection a preferred alternative. Viability of air injection process in laboratory was established through displacement tests in 1.83m long & 100mm diameter combustion tube using synthetic and natural core at IRS, ONGC. In view of limitations of laboratory generated oil reactivity data for carrying out prediction in STARS, following workflow was adopted to estimate recovery from air injection. Predicting recovery by immiscible gas injection – Pressure maintenance and Immiscible displacement Predicting Volumetric Sweep Efficiency – From miscible Gas Injection. It is felt that Miscible process mimics closely air injection as both these processes have displacement efficiency of more than 90% Predicting Recovery by Air Injection – Integrating immiscible flue gas with Nelson & McNeil derived profile after considering volumetric sweep from miscible gas displacement process. Air injection has the potential to enhance recovery from 19% to 62%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".