Use of Technical Design and Surveillance Tools to Minimize Operating Integrity Risk in High Temperature Wells
Bibliographic record
Abstract
Abstract Shell Canada has conducted thermal recovery operations in the Peace River area of Alberta for over 50 years, using a combination of vertical, deviated and horizontal wells. During this time, many different recovery schemes, well designs, and operating practices have been used and assessed to determine the best approach to minimizing well integrity risk from safety, technical performance, and cost standpoints. The cumulative experience has allowed Shell to have an in-depth understanding of the most appropriate casing and connections for specific thermal service that offer the best long-term performance and integrity. Casing cement design and placement practices are a key component in well construction to obtain superior, long-term, hydraulic isolation performance. Well operations must be monitored though an effective surveillance process to obtain not only periodic assurance of mechanical integrity of well components, but also detection of inter-well formation anomalies that may lead to well failure or loss of hydraulic isolation if left unidentified. Monitoring and observation wells can offer key additional insights on sub-surface events and changes, and instrumentation techniques can flag anomalies, as soon as detected, for further assessment and action. This can protect not only the wellbores in use, but also assess the effect of project operations on boundary areas and previously abandoned wells.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".