Combined ESP / Auto Gas Lift Completions in High GOR / High Sand Wells on the Australian Northwest Shelf
Bibliographic record
Abstract
Abstract The paper presents experience with an electric submersible pump (ESP) / auto gas lift completion design. The design has been developed to overcome the production engineering challenges which have been encountered following the commencement of production from the Stag oil field. The flow from the horizontal section of the wells has a high gas fraction, continuously slugs with a very short frequency, and carries large volumes of sand following the onset of water production. In addition, the reservoir pressure has depleted more rapidly than had originally been expected. These characteristics combine to represent one of the most challenging environments in which a field can be developed using ESP's. The paper describes the evolution of the completion design, which allows natural flow via either the tubing or the annulus, ESP lift via the tubing augmented by auto gas lift via the annulus, or conventional compressor-driven gas lift via the annulus. The measures taken to overcome poor ESP run life as a result of sand-laden slug flow are also described. Finally, operating experience with the improved completions is reviewed. The improved completion has been installed in six wells. In one well, a definitive incremental production rate of 1700 stb/d (60%) was achieved. The other wells are either new wells, or have had other remediation measures applied e.g. additional perforations. It can be demonstrated from their behaviour, though, that incremental production of 40-80% is the direct result of the improved completion design. In addition, ESP run life has been improved by more than 100%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".