Evolution of Bacchus Subsea Well Interventions Using Rig and Light Well Intervention Vessels
Bibliographic record
Abstract
Abstract A suite of subsea intervention case histories at the Bacchus oil field in the North Sea will demonstrate how one operator matured intervention planning to address well entry challenges using learnings gained over the course of successive jobs. This contributed to better management and mitigation of potential risks leading to slickline performance improvement for gas lift valve reconfiguration, the successful deployment of coiled tubing to clean out asphaltene deposits in a live subsea oil well from a monohull vessel and setting of a retrofit gas lift straddle to optimize and secure production. The paper outlines intervention asset selection, work programme development and risk mitigation measures related to subsea tree valve function issues and loss of full bore access caused by asphaltene and wax deposits. Light well intervention vessel and mobile rig operations using deployment methods including slickline, digital slickline, electric line and coiled tubing are described. The role of production technology work undertaken to better understand the nature of organic deposits in the wells and how that contributed to anticipating well access risks and inform intervention planning will be highlighted. These real field examples add to the knowledge base of well services and production technology challenges faced during subsea well intervention and highlights approaches to overcome them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".