Cluster Flow Identification During Multi-Rate Testing Using a Wireline Tractor Conveyed Distributed Fiber Optic Sensing System With Engineered Fiber on a HPHT Horizontal Unconventional Gas Producer in the Liard Basin
Bibliographic record
Abstract
Abstract This paper investigates the flow performance of a horizontal unconventional gas producer where cluster flow has been detected and quantified. The method employed is the first use of a wireline deployed engineered fiber optic Distributed Acoustic Sensing (DAS) system for the purpose of production monitoring. It further describes the technological advancement of the DAS system. While distributed fiber optics sensors have been used in unconventional wells for over ten years, the focus has been on stimulation monitoring, where relatively large thermal and acoustic signatures are easily measured. There has been limited success on the use of the same systems for production monitoring in horizontal wells since the thermal and acoustic flow signatures are very small. The recent technological advancements achieved in the area of Distributed Acoustic Sensing, using engineered fibers with dramatic improvements in the signal-to noise ratio, now make it possible to sense low levels of inflow noise with DAS systems. This study presents two distinct ways of analysing DAS data to extract production information and demonstrates the analysis using real field data. The well is an unconventional dry gas producer in the Liard Basin, Canada, which was completed in 18 stages, with 3 cluster per stage, a total of 54 clusters along a horizontal section of ~2000m. The top 11 stages were logged on tractor conveyed wireline with DAS and DTS data acquired over a 19-hour period. The acquisition covered an initial shut-in period, followed by a ramp up to a maximum test rate, and a final shut-in period. Data from the full acquisition period was analysed and is presented in the paper. The data acquired at various points during the acquisition period was used to detect and quantify inflow. It was found that one of the clusters was active throughout the shut-in period and inactive during the maximum test rate. We were also able to identify more clusters being activated as the production rate increased. The increase in the number of active clusters was not proportional to the increase in the surface gas rate, with heel side clusters becoming active before the toe side clusters as the production rate was increased. The results of this survey yielded remarkable insights that informed the operator on the completion efficiency and the relationship between the total production rate and cluster activity.
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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".