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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".