Case Study: Liner Integrity Failure Identification and Repair in a SAGD Production Well at MacKay River Commercial Project
Bibliographic record
Abstract
Abstract The Mackay River Commercial Project (MRCP), operated by PetroChina Canada Ltd. (PCC) in the Fort McMurray region of Northern Alberta, utilizes Steam Assisted Gravity Drainage (SAGD) for commercial bitumen extraction. This case study outlines the successful identification and remediation of a liner failure in a SAGD production well, leveraging an integrated approach using real-time surveillance and wellbore integrity diagnostics. An anomaly in the well's temperature profile was first detected through Distributed Temperature Sensing (DTS), prompting further investigation. Distributed Acoustic Sensing (DAS) data were analyzed, confirming a shift in the inflow profile at the same depth, indicating a potential breach. To validate the findings, a wellbore integrity log was conducted using caliper and electromagnetic tools, which confirmed a liner failure at the previously identified depth. Following confirmation, a targeted remediation plan was developed and executed. The well was successfully returned to production and has since operated without signs of distress. The integration of DTS, DAS, and integrity logging proved to be a reliable diagnostic methodology, enabling a focused and efficient remediation process. This case underscores the value of integrating real-time surveillance with diagnostic logging in SAGD operations, particularly for mature wells with increased failure risk. Such a systematic approach can help extend asset life, reduce downtime, and avoid costly interventions like full wellbore redrills, contributing to improved operational efficiency and cost control.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".