Maximising ESP Uptime in Forties Field—Introducing Successful Frac Packs to the North Sea
Bibliographic record
Abstract
Abstract This paper demonstrates, via a comprehensively described recent case history, the transfer of established completion technology into the North Sea. Further, in performing the process the technique has been successfully extended beyond its normal criteria of application. Today artificial lift in the Forties Field is provided by a large number of electric submersible pumps (ESPs) lifting from shale inter-bedded sandstone sequences. Overall production is affected by the achieved ESP run-life in a field where sand-face failure is not uncommon. ESP failure in the Forties Delta well FD5-1 was found to be due to pack-off from a high proportion of fine shale/siltstone in addition to formation sand. Although simultaneous fracturing and gravel packing are commonly performed in other hydrocarbon producing regions none had been directed at controlling the mechanism of flowing shales and/or claystones. The well intervention was designed to protect the ESP from shale or sand grain production and provide minimal impairment to well productivity. Both objectives were achieved by increasing the formation face area for inflow and in so doing physically support the formation face in the first such frac and pack treatment executed in the UK Sector of the North Sea. ESP protection was achieved and subsequent well productivity was show to be minimally affected by the presence of the gravel pack. This case history describes the formation failure mechanism; the detailed corrective well intervention and the resulting well productivity in bring well FD5-1 successfully back into full production up-time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".