New Life for Old Wells – A Case Study of the Effects of Re-Stimulating Gas Wells Using Fracturing Through Coiled Tubing and Snubbing Techniques
Bibliographic record
Abstract
Abstract The use of a coiled tubing conduit for the hydraulic fracturing of shallow gas wells in southern Alberta, Canada has increased each year since its beginnings in 1997. The coiled tubing fracturing (CTF) technique has been utilized for both new and old wells as a means of fracture-stimulating multiple reservoir intervals. This paper will detail a re-stimulation project completed during the summer and fall of 2002, in which the CTF and snubbing-conveyed fracturing (SF) processes were utilized to re-fracture a group of shallow gas wells that were originally completed in the 1970s. The objective is to examine the possible ways of enhancing production of older shallow gas wells by fracture stimulation utilizing tubing-conveyed processes. Specifically, the paper will outline ways to consider and select wells that are candidates for re-entries, in addition to the essential work and evaluation that has to be done both prior to and following the re-entry, re-perforation, and stimulation techniques. This study will also provide a relative comparison between conventional fracturing techniques used previously and the CTF and SF processes used most recently. Also included are pre- and post-stimulation production data that give a clear indication of how effective the fracturing through tubing process is when used to re-stimulate these older wells.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".