Bibliographic record
Abstract
Abstract In 2004, the large Mangala, Aishwariya, and Bhagyam oilfields were discovered in the remote Barmer Basin of Rajasthan, India. These fields contain light, paraffinic crude oils with a wax appearance temperature approximately 5°C less than reservoir temperature, and in situ viscosities that range from ~8cp to ~250cp. Development plans for these fields are based on hot waterflooding to prevent problems with in situ wax deposition. This paper discusses a few issues associated with waterflooding viscous oils, presents some viscous oil waterflood results from around the world, and benchmarks the expected performance of the Rajasthan fields to this database. Given that the Rajasthan oils have some properties that may be considered "unusual" and potentially troublesome for waterflooding, and that there is no long-term production data or a history-match of waterflood performance in hand, these benchmarks were considered very important reality checks. In actual fact, fields with similar or much higher viscosities are routinely waterflooded with excellent recoveries in Canada, the USA and elsewhere.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".