Quantifying the reduction of rehabilitation needs and safety risks in the use of innovative well and flowline decommissioning tools
Bibliographic record
Abstract
This paper identifies, measures and demonstrates both the reduction in safety risk and in Environmental Social and Governance (ESG) factors, associated with the traditional cut and cap methods for gas/oil wells and flowlines compared to new innovative internal cutting methods. In 2017 Summit Canada recognised the need to improve the safety of personnel in the oil and gas fields of Canada, during decommisioning of both wellheads and flowlines. The traditionally established method was to excavate a bellhole around the infrastructure and to subsequently cut the wellhead or flowline at 1.5 m below ground level using an oxy-torch. This usually involved an individual standing on a step ladder under a suspended load in an excavation which was often a confined space, not adequately benched or sloped. Comparative data from 5 years of North American and 2 years of Australian experience in decommissioning old wells using internal cuts as opposed to the excavation method are used in the study. The safety risk associated with each method can generally be quantified and given a Hazard Ranking established from crew sizes, kilometres driven and work methods undertaken. Similarly, the ESG factors associated with each method can be quantified in terms of area of disturbance, as well as landholder inconvenience and emissions resulting from number of trucks and personnel movements. A significant reduction in safety risk was the key finding, with the silver lining of also reducing the ESG impacts of gas well and flowline decommissioning activities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".