Case History: Expanding the Boundaries of a Hydraulic Workover Unit into New Economic Levels
Bibliographic record
Abstract
Abstract A 600K Hydraulic Workover (HWO) Unit has proven that it has the capability to safely access and deliver offshore gas reserves. This paper identifies the business environment, challenges, learnings, results and the key success factors of a three (3) well project that delivered a total of 250 million standard cubic feet per day (mmsfcd) using a 600K HWO unit. The primary result of the project demonstrated the capability of the HWO unit in handling challenges previously thought better suited for conventional larger rigs. HWO units have proven to be particularly effective in bpTT's offshore operations, however their application had been limited to tubing changeouts, workovers, oil recompletions and short oil sidetracks. This project expanded the capability of the 600K HWO unit to perform high rate uphole gas recompletions. The project consisted of three uphole gas recompletions in 9-5/8 inch casing. Each recompletion was executed at approximately $0.50/barrel of oil equivalent (boe) which made the project extremely attractive and made gas reserves economically viable. Completing the same work with a larger Jackup unit would have cost $3 -4/boe clearly showcasing the economic advantage of the HWO unit. Another major advantage was the ability to move the HWO unit in unfavourable weather conditions when compared to a Jack Up rig. Weather conditions can result in significant and costly delays during the December – April period for jack up units.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".