A Novel Anti-Foam Chemical Application As Contributor To The Successful Start-Up Of The Majnoon Oilfield
Bibliographic record
Abstract
Abstract The Majnoon oilfield in Southern Iraq, one of the largest in the world, recommenced production in September 2013. The field was developed by the Iraqi South Oil Company in the 1980s and originally produced around 50,000 BOPD. As result of Iraq's second licensing round a consortium of Shell, Petronas and Missan Oil Company worked closely with SOC and redeveloped Majnoon to increase its daily production from 50,000 BOPD to more than 175,000 BOPD. Over a period of several days the production increased from 15,000 to 40,000 BOPD, when several plant trips occurred due to excessive liquid carry over from the separators into the flare system. At first this was attributed to unstable flow and tuning of the production facilities. However, more plant trips followed while ramping up to more than 100,000 BOPD as debris accumulated in the flare system which resulted in multiple blockages. A root-cause analysis was carried out by a team from production chemistry, operations and production support before piloting an anti-foam chemical solution which proved to be an immediate success and production stabilised within hours. After three days the pilot project was converted into a permanent solution and since the introduction of the anti-foam chemical Majnoon hasn't suffered any further plant trips due to liquid carry over. In fact, the chemical has allowed production to be increased by at least 15,000 BOPD through improved flow through the separators. This paper will explain the theory behind foaming and highlight the process of fluid testing. We will present the results of the plant trips root-cause analysis and describe in detail how the anti-foam trial developed. A novel monitoring program has been developed that allows for immediate correction in case of changes to production or chemical injection, which has greatly contributed to the safe and successful Majnoon start-up.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".