Downhole Monitoring Using Distributed Acoustic Sensing: Fundamentals and Two Decades Deployment in Oil and Gas Industries
Bibliographic record
Abstract
Abstract Distributed Acoustic Sensing (DAS) through fiber optic has been deployed in downhole monitoring for over two decades. Several technological advancements led to a wide acceptance of this technology as a reliable surveillance technique. This paper presents a comprehensive technical review of all the applications of DAS. The paper starts with the fundamentals of fiber optic deployment. Then, an overview of all the applications of DAS including seismic application (vertical seismic profiling), microseismic (hydraulic fracturing characterization), well and pipe integrity (such as leak detection and cement quality), and well and pipe flow monitoring is provided. Flow monitoring contains injection and production flow estimation, phase determination, gas and water breakthrough identification, gas lift surveillance, pump and flow control device performance evaluation, sand production detection, and flow regime recognition. This paper reviews the basics of DAS, fiber types, installation methods, types of recorded data, data processing, historical development, current applications and limitations. The paper provides a concise review using several field cases from over two hundred published papers of Society of Petroleum Engineering (SPE) and journal databases. The applications of DAS in downhole monitoring can be generally divided into the qualitative and quantitative applications. The study discusses deployment methods, case by case worldwide field performance and interpretation/modeling. It also summarizes main lessons, key results, and challenges including data quality, signal to noise ratio effect, and operational conditions such as the installation of the fiber and the complexity of quantitative production prediction and flow profiling. In addition, a comparison between deployment of DAS and other methods is reviewed. This study is the foundation for an ongoing study on wellbore and reservoir surveillance through real time distributed fiber optic sensing (DAS) records along the wellbore. It summarizes the historical development and current limitations to identify the existing gaps and reviews the lessons learned during the two decades of the application of DAS in downhole monitoring.
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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 | 0.000 |
| 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) | 0.000 | 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".