Real-Time and Cloud-Based Fiber Optic Well Monitoring, Part 1: Surface Casing Vent Flow
Bibliographic record
Abstract
Abstract Real-Time monitoring surface casing vent flow (SCVF) and enhancing its efficiency through accurate disturbance profiling has traditionally relied dominantly on acoustic logging tools and data. However, with the rise of distributed sensing technology over the past decade, the use of distributed fiber optic sensing (DFOS) for quantitative disturbance profiling has gained significant traction. This technology allows for acquiring high-resolution acoustic data along the wellbore, offering detailed insights into the acoustic signatures linked to potential gas leaks. This work presents an integrated workflow for analyzing distributed acoustic signals, supported by two real world case studies. The application of distributed acoustic sensing (DAS) technology for gas leak detection has the potential to greatly improve the accuracy and reliability of well integrity monitoring. The developed solution enhances our qualitative disturbance profiling software by providing a quantitative assessment of intervals more prone to gas leakage. During development, we utilized two supercomputers to efficiently process large-scale DAS data and apply multiple algorithms to extract key features related to gas leakage. To quantify the DAS responses, we created an algorithm that extracts frequency-domain information, enabling spectral analysis to compare DAS signals across different frequency ranges. By aggregating waterfall plots generated from raw phase data, we were able to identify potential gas leak zones. Our solution was applied in a blind test to two abandoned wells with gas leakage, as identified by the operator's analysis. Potential gas leak intervals were detected in Well #1 and Well #2, all of which were subsequently confirmed by the operator. The integration of DFOS technologies represented a significant advancement in well monitoring and management, offering continuous, high-resolution data while addressing the limitations of traditional methods. The final version of the developed software is optimized for conventional computers. It can efficiently read HDF5 files, reduce the large DAS data size by a factor of 100, and provide real-time visualization. To enhance security, the analysis results are encrypted before being stored in the cloud, ensuring secure remote access for operators to monitor gas leaks and other well activities seamlessly. The novelty of this work lies in the real-time, cloud-based monitoring of SCVF using DAS technology in oil and gas wells, offering quantitative insights into the monitoring process. Real-world case studies demonstrate substantial improvements in SCVF monitoring through the proposed approach. These advancements will enable the industry to improve decision-making strategies related to the integrity and management of abandoned wells.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".