Full-Scale Laboratory Test of Distributed Fiber Optic System for Well Integrity Monitoring
Bibliographic record
Abstract
Abstract Distributed fiber optic (FO) technology offers the benefit of continuous downhole monitoring without frequent well entries for conventional well inspections, which not only introduce additional well integrity risk due to the invasive nature of the workover activities but also add significant cost to operators. A comprehensive research program sponsored by the Pipeline Research Council International, Inc., the Solution Mining Research Institute, and the US Department of Transportation, Pipeline and Hazardous Materials Safety Administration was executed to assist underground gas storage (UGS) operators in improving their ability to make informed decisions regarding incorporating FO technology into UGS well integrity monitoring. As a major component in this research program, full-scale laboratory tests were executed to experimentally evaluate the performance of FO monitoring systems in detecting various downhole events that pose well integrity threats. Distributed strain sensing (DSS) and distributed acoustic sensing (DAS) systems provided by two commercial vendors were tested. Two test wells with cemented pipe-in-pipe configuration were purposely built for the DSS and DAS tests. FO cables were attached to the inner pipe before they were cemented in-place. The DSS test specimen was subjected to various axial tensile loads. Strain gauge measurements along the inner pipe were used to evaluate the DSS measurements. The DAS test well was designed with features that allowed for simulating gas leaks through various leak paths. The DAS measurements were compared with the known leak events to assess the DAS system’s performance. Overall, the test results provide a comprehensive understanding of the capabilities and limitations of the DSS and DAS systems for monitoring various downhole anomalies. The outcome of this research program has established a technical basis for future implementation of distributed FO monitoring system as an alternative well integrity monitoring method to enhance the safety and efficiency of operations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".