The Maule Field: An Economic Small Field Development
Bibliographic record
Abstract
Abstract Apache discovered the Maule field in October 2009 with the 21/10-A52 wellbore drilled from the Forties Alpha production platform. The discovery of a 14m TVD net oil column in Eocene aged Brimmond sands led to the development of the Maule field under the UK government's small field allowance scheme. The field was taken from completion of the discovery well to first oil from a horizontal production well in under 9 months. The Maule field is located on the western margin of the Brimmond Formation turbidite fairway where re-mobilised sands are also present. Steep dips as seen in the development well and steep seismic features indicate that the Maule field reservoir was formed by remobilsation of Brimmond sands. The A52 exploration well was drilled on the basis of a seismic amplitude anomaly. This seismic data along with LWD density image data was used to successfully place the horizontal production well which accessed a 114m MD pay section and flowed in excess of 11,500 bopd. A 2010 4D snapshot taken in early July 2010, 5 weeks after production started, identified the source of the well's rapidly increasing water-cut and identified further infill locations. Despite modest production for an offshore North Sea development the first Maule producer has been an economic success for Apache as a result of integrated subsurface technical work, drilling performance, small field allowance incentives and a sense of urgency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".