The Effect of Previous Counter-flow Production on the Interpretation of Velocity String Gas Wells Using DTS Temperature Surveys
Bibliographic record
Abstract
Abstract Over the past 3 years fiber optic slickline distributed temperature measurements (DTS) have become a commonplace method of monitoring Canada’s Deep Basin commingled gas wells produced through velocity string completions. The use of velocity string completions prohibits conventional production logging, so the wells are flowed up their annulus for a short period of time and a DTS slickline is used to monitor the flowing temperature profile. This temperature profile is then interpreted to give the flow from each reservoir zone. DTS is a much more cost effective solution than having to pull the tubing in order to run a conventional production log and allows testing of lower rate wells that would otherwise liquid load. The analysis technique conventionally assumes that during the annular flow period, where the DTS is used to acquire the flowing temperature, all the thermal effects of the previous counter-flow production period have dissipated and the problem can be solved by an upward flow thermal model only. This paper evaluates the magnitude of the residual thermal effect of a period of counter-flow on the annular flow response over the timescales typical for DTS monitoring. A counter-flow thermal model has been developed for typical well scenarios and the shut-in decay of the thermal response of this model is superposed on the conventional annular flow model to highlight the magnitude of influence of previous counter-flow production. The model is used to interpret the counter-flow response of annular flowing gas wells using real well DTS data and demonstrates the magnitude of the effect and how to use this method to improve the accuracy of the resulting flow analysis.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".