Velocity Tracking for Flow Monitoring and Production Profiling Using Distributed Acoustic Sensing
Bibliographic record
Abstract
Abstract In this paper, we will share the recent work that was done to understand how bulk flow rates and fluid composition may be derived in single-phase and multi-phase flow by tracking the slopes (velocities) of coherent features detected using Distributed Acoustic Sensing (DAS). Both laboratory experiments and real field examples will be presented to demonstrate how velocity features can be detected and attributed to events such as slug flow or sound waves. Speed of Sound (SoS) analysis can in principle be used for determining changes in the fluid composition in multiphase flows, which provides opportunities to detect fluid interfaces and water or gas breakthrough. On the other hand, slowly moving features such as slugs or turbulent eddies can be used to derive bulk flow velocities, which may be used for injection or production profiling. The evaluation method directly derives velocities by Fourier transforming the raw DAS data in the temporal and spatial domains without applying any calibration steps. It can therefore be used to monitor flow in wells on a drive-by or continuous basis without a need for reference flow data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".