SAGD Production Casing Failure Diagnosis and Repair
Bibliographic record
Abstract
Abstract The operator experienced an unusual casing failure at a producing SAGD (steam assisted gravity drainage) oil well in summer of 2017. The subject well in the Firebag SAGD field of NE Alberta, Canada had operated successfully for over 11 years. Once the problem was identified, the well was shut in to determine the nature of the failure and options for repair and recovery so it could be returned to operation as soon as possible. Tasks included identifying and isolating the failure, establishing the cause and nature of the failure, and determining viable repair options. Logging diagnostics to measure/image the failure were performed, which included new ultra-sonic logging imaging technology, high-resolution multi-finger caliper logging, a downhole camera run and conventional eddy flux casing inspection log. Historical log data was also reviewed to assess whether the failure evolved over time, or if the mechanism was acute. Once the nature of the failure was established, the optimal repair method was chosen, planned and carried out. Sophisticated analysis of multi-finger caliper log data, camera images and new technology in the form of an ultrasonic imaging tool for the casing were utilized and are presented. A discussion of potential root cause mechanisms for thermal wells is provided, including a variety of failure modes that could be ruled out. Confidence in the failure mode specific to this well was increased by considering information acquired from multiple diagnostic tools. The nature of the connection failure determined from this process is outlined, along the rationale behind the repair method selected to remediate the well.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".