An Industry Overview of Downhole Monitoring Using Distributed Temperature Sensing: Fundamentals and Two Decades Deployment in Oil and Gas Industries
Bibliographic record
Abstract
Abstract Distributed Temperature Sensing (DTS) system using optical fiber has been deployed for downhole monitoring 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 the DTS, with focus on oil and gas industrial deployments. The paper starts with the advantages of the DTS over other methods and an overview of the DTS basics, including theory, the DTS components, deployment types, fiber types, design and limitations. Then, it is followed by the oil and gas applications of the DTS including hydraulic fracturing (during and after fracturing), well treatment/stimulation (acid injection, fluid distribution, diversion monitoring), inorganic (scaling) and organic (wax/asphaltene/hydrate) deposition detection, leak detection (in well and pipeline), flow monitoring (rate monitoring, water/steam injection and SAGD monitoring, CO2 storage monitoring, zonal contribution determination, gas lift optimization) and reservoir/fluid characterization (facies, porosity, permeability and fluid composition determination). This study reviews the historical development, applications and limitations of the DTS systems. The paper mainly focusses on deployment techniques, the application of the DTS for the prediction and surveillance of the non-thermal and thermal producer/injector wells, hydraulically fractured wells and those wells with treatments. 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 application of the DTS in downhole monitoring can be divided into the qualitative and quantitative applications. In quantitative approaches, numerical models should be combined with the DTS data. This study discusses case by case worldwide field applications of DTS along with proposed modeling methods and interpretations. It also summarizes main challenges, including the fiber reliability, longevity, and operational limitations such as the installation and the complexity of quantitative approaches. This study is the foundation for an ongoing study on wellbore and reservoir surveillance through real-time distributed fiber optic sensing recordings along the wellbore. It summarizes the historical development and limitations to identify the existing gaps and reviews the lessons learned through the two decades of the application of the DTS in production performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".