Well Integrity Monitoring & Analysis Using Distributed Acoustic Fiber Optic Sensors
Bibliographic record
Abstract
Abstract Evaluating well integrity (i.e. flow of fluids or gas) from behind casing can be challenging using existing single mode analog sensors; they offer limited representation and data acquisition can be time consuming. Further, traditional processing algorithms such as Fourier Transforms are not responsive to non-stationary, nonlinear events such as random, low volume leak signatures. Recent advancements in both fiber optic Distributed Acoustic Sensors (DAS), and processing algorithms stand to significantly simplify downhole low rate leak detection. This paper will explain the capabilities and limitations of this monitoring approach. Distributed Acoustic Sensors; Proven in demanding applications such as submarine sonar systems, optical fiber can be packaged in such a way that makes it extremely sensitive to acoustic disturbances along its entire length. Using the fiber itself as a sensor has several advantages, some of which include; extreme sensitivity, design simplicity, and the ability to obtain 1000’s of simultaneous measurements with little or no loss of fidelity. Datasets were obtained from both a specifically designed 200 ft vertical controlled test well simulator and actual problematic gas wells in Canada. Processing Algorithms: Using DSP techniques and real time methods, the engineer can tune the system to a specific leak signature which eliminates unwanted events and highlight useful acoustic components pertaining specifically to the leak. Once the data is obtained the high fidelity acoustic data undergoes various filtering and error detection processing. Algorithms were tested in Matlab and converted to executable code once verified. The integrated well monitoring and analysis system offers a more comprehensive, detailed solution. When compared to traditional technologies, future remedial strategies were often strategically more accurate using the fiber based systems, especially when low leak rates were involved. It is anticipated that engineers will be able to locate problematic leaks with higher confidence and save money by reducing the number of failed interventions; similarly, the need for experienced highly trained log analysts will be reduced. Applications for this information may include: low rate leak detection through casing, pipe integrity failures, zonal isolation issues, long term well monitoring, carbon storage and sequestration, evaluating intervention effectiveness, and locating multiple source leaks along a wellbore.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".