Automated Anomaly Detection Applied to DTS Data: A Case Study from Quest CCS
Bibliographic record
Abstract
Summary The applicability of an automated machine learning (ML) detection workflow applied on Distributed Temperature Sensing (DTS) as a technology to monitor CO2 containment, is presented in this paper. Quest, an onshore CCS commercial facility site in Alberta, Canada, that has received about 8.8 million metric tons of CO2 is the setting for this study. Temperature measurements are acquired in the three CO2 injection wells since 2015, across all depths, with a fiber optic system deployed behind casing. Over two hundred million temperature measurements were recorded during this time. Our analysis includes the training and evaluation of the ML model to predict the temperature response and detect anomalous trends in the data. The workflow’s sensitivity is assessed using a pseudo-empirical model, where field data are combined with synthetic generated anomalies. Deviations as small as 0.5 degrees, aggregate over target depths, are expected to be detectable. Additionally, we discuss the implementation of the workflow on real time data and the insights gained from this process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".