Monitoring Inflow Distribution in Multi-zone, Velocity String Gas Wells Using Slickline Deployed Fiber Optic Distributed Temperature Measurements
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Bibliographic record
Abstract
Abstract Fiber-optic systems are able to generate a temperature log along an optical fiber using a laser source and analysis of the backscattered light. This paper details a novel application of this technology using an optic fiber embedded in a 1/8th inch slickline cable to calculate the inflow distribution of multi-zone gas wells with velocity strings. EnCana's multi-zone gas wells in the Deep Basin of Western Canada are often completed with production tubing landed near the lowest perforated interval to act as a velocity string and lift produced water to surface. This completion technique makes spinner production logs impossible to run without initially performing a wellsite operation to lift the tubing shoe above the reservoir, requiring either a workover rig or a snubbing unit. Running a slickline containing an optical fiber to the bottom of the tubing and producing the well up the annulus for a short period allows the temperature profile of the well to be measured and therefore, the inflow distribution of the well can be calculated. Determining the inflow distribution of multi-zone gas wells now becomes a simple slickline operation with no tubing shift required. Additional benefits are the detection of crossflow on shut-in and the measurement of flowing bottomhole pressure when a gauge is run at the end of the slickline. The process is cost effective, less risky than conventional production logging, and the slickline can be safely employed where there is significant surface pressure. The paper uses case studies validated by spinner log comparisons to demonstrate that slickline fiber optic distributed temperature sensor measurements are a viable method for performing reservoir surveillance in multi-zone gas wells with velocity strings in Western Canada. Utilizing fiber optic measurements in these wells reduces operating costs and should ultimately lead to increased efficiency of reservoir stimulation practices.
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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 it