Stag Development - Challenges and Successes in the First Two Years
Bibliographic record
Abstract
Abstract Stag oilfield commenced production in May of 1998 and reached peak rates in excess of 30,000 barrels of oil per day in July 2000. Stag development is an $A200 million oil development on the Northwest Shelf of Western Australia (Fig 1) designed to produce in excess of 50 million barrels of oil over the life of the field. The field was discovered in mid-1993, delineated in 1994 to 1996, and tested with a horizontal well production test in 1996. The development consists of a Central Production Facility containing wells and processing equipment, a 2km subsea line to a CALM buoy and FSO storage and export tanker. Designed to have five producers and three water injectors producing around 30,000bopd oil, the field did not meet initial expectations. Gas-oil ratios were higher than forecast, peaking in excess of 1000scf/bbl and causing significant operating problems for the process plant and electrical submersible pumps. Oil production was lower in the east of the field than anticipated, due to the thinning or absence of high productivity sand units, reservoir pressure support was poorer than modelled leading to more rapid pressure decline than anticipated, and one well produced significant quantities of sand and clayey solids. These challenges led to a significant re-work of the development. Subsea water injectors were added, wells were recompleted in better quality reservoir, pump designs were significantly changed, the reservoir model was rebuilt and more producers were added (Fig 2). At year-end 1999, Stag came of age - production reached design basis levels, reliability approached 100% and overall reservoir performance met expectations. The paper reviews three key elements of the development - Reservoir Description and Performance, Completion Design and, Facility Design and Performance - and presents the lessons learned.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".