Modeling Flow Profile Using Distributed Temperature Sensor DTS System
Bibliographic record
Abstract
Abstract Distributed Temperature Sensing (DTS) technology uses fiber-optic cable to measure continuous temperature profile along the wellbore. Measurement interpretation can provide valuable information, and one of them is real time flow profiling that helps to monitor the fluid flow in wells. This valuable information can assist real time production decision with no well intervention. However, the complexity of the data analysis limits the use of DTS as a flow allocation technique. This paper presents a new flow-profiling model using DTS technology. The model is based on steady-state energy balance equation and it handles multiple production zones with its own zonal fluid properties. The model is applicable for gas and oil wells in onshore and offshore environment. The model is integrated into easy-to-use software and it can be run in two modes: forward simulation and flow profiling. The forward simulation calculates temperature distribution along the wellbore for any given production profile, and this mode is critical for the model calibration. It is also very useful for emulating what-if scenarios, like water breakthrough. The flow profiling estimates production profile based on measured temperatures, which is the base for the real time well monitoring. Our studies with the model show that geothermal profile, fluid properties, formation properties, well completion, and deviation as well as Joule-Thomson effect all play key roles for the model accuracy. Joule Thomson gas cooling effect only occurs at lower pressure while reversal appears at higher pressure region. The model is tested against synthetic, literature and field examples and good agreements have been obtained. Test results have been presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".