Preparation for Cost Effective Decommissioning and Abandonment of Subsea Pipelines
Bibliographic record
Abstract
Abstract Decommissioning activity will increase in the next nine years with a predicted £1.3 billion spent on decommissioning of subsea pipelines and associated subsea infrastructure in the North Sea from 2014-2023 [REF 1]. Detailed preparation prior to the Cessation of Production (CoP) may significantly reduce this cost. Savings can be made during late in life operations on the platform and preliminary decommissioning planning. A Comparative Assessment tool assists the project team in selecting the most preferred decommissioning and abandonment option for subsea pipelines by using criteria such as safety, environmental factors, technical feasibility, economics and societal issues. These are then ranked by priority through matrix algebra. The introduction of new technology and advanced planning for decommissioning campaigns are the solutions for cost reduction, such as: Several applicable technologies and research areas are recommended as topics for further development, such as laser cutting, subsea lift claws and cutters and long-term effects of pipelines on fish habitats.A thorough checklist to incorporate decommissioning during the design phase would ensure decommissioning is given as much emphasis as input from the operations and maintenance teams during this important phase.Primary cost drivers are identified and include long term liability, cleanliness standards and national requirements for making the pipelines safe for potential re-use.Pipeline preservation for future use and/or leaving pipelines in place are the most cost effective solutions for pipeline decommissioning and if regulatory requirements change where pipelines must be removed, decommissioning costs could skyrocket.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".