Bibliographic record
Abstract
Abstract Issaran was discovered on 1981 and the cumulative production up to 1998 was 0.7 MMbbls from 9 wells and the total reserve were 5 MMbbls from 500 MMbbls as original oil in place which result in a recovery factor less than 1% and reflect the big challenge to produce this field. The problem was that each reservoir has its own characteristic, which requires special approach to overcome its problems and optimize its production and reserves. As Zeit Formation contain massive sand, Upper and Lower dolomite due to its shallow depth the oil could not be produced as cold production, Gahrandal formation is consisted of three limestone bodies with permeability below 20 md and the last one is Nukhul zone which give some cold oil production due to its naturally heavy fracture. Theses difference characterization lead to apply different ways of reservoir management for each zone. The challenge of Scimitar is to produce these zones and reach to a production plateau in spite of the aging of the fields and its associated problems (low pressure, water cut increase, wells potential, sand production and corrosion, …. etc). Scimitar dedicated special multi disciplinary teams to propose and implement several strategies to achieve its goal and succeeded to have stable plateau around 6000 BOPD during year 2008. This paper will highlight the results of each zone and the achievement as the total wells drilled on the field up to now is 287 well and the total OOIP increases to 700 MMbbsl, the cumulative oil production on March 2009 is more than 10 MMbbls which gives more confidence about the successful of the new strategies for each zone. The steam project applied on the Lower and Upper Dolomite is consider the first successful cyclic steam project on the Middle East. The results of the producing wells in conjunction with the numerical 3D simulation study lead to prove that the recovery factor can be reach to 45% which gives total reserve around 300 MMbbls.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".