Casing Connection Break Detection Through Tubing in Canadian Thermal Projects. Case Study.
Bibliographic record
Abstract
Abstract Production casings of Cyclic Steam Stimulation (CCS) or steam-assisted gravity drainage wells are exposed to significant temperature variations which in many cases resulted in casing breaks in the weakest part which are typically connection joints. The paper focuses on the new downhole logging approach, in monitoring and detecting production casing connection breaks through tubing without requirement for tubing retrieval. The metal well barriers can be assessed by utilizing electromagnetic (EM) pulse defectoscopy. This is done by running multiple coaxial sensors downhole in tandem. Each sensor generates EM pulse and then records EM decay from surrounding metal tubes. Modeling of recorded EM decay enables precise assessment of metal loss or metal gain in up to four concentric barriers. However, the tool had never been used previously to detect minor defect features as casing breaks through the tubing. To identify casing breaks several yard and field tests have been conducted and new methodologies were developed. The last one included the recognition of specific patterns of raw EM responses, analysis of hole sensors and utilization of data from all coaxial sensors utilized during the downhole survey. The new approach including downhole EM pulse tools and new data analysis have been implemented to detect casing connection breaks in over a hundred Cyclic Steam Stimulation (CCS) and SteamAssisted Gravity Drainage (SAGD) wells. The paper demonstrates the testing of the application feasibility in a comprehensive yard test and extends to real field examples. All detected breaks were confirmed after tubing removal and were successfully repaired. Paper highlights detection challenges due to different casing connection break types: minor breaks, partial breaks (contrary to fully circumferential), and casing breaks aligned with tubing connections. The technology has helped Operators to fulfil the objectives of connection break detection without tubing removal through a non-intrusive, safe, quick and economical approach. Today, CSS and SAGD Operators should confirm casing integrity repeatedly prior to each subsequent steam cycle through the time and resource consuming approach of tubing removal and checking the casing integrity mechanically. Utilizing through tubing electromagnetic diagnostics, enables Operators to pick up multiple casing connection breaks in a single run without tubing retrieval.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".