Completion Design Production Case Study in the Duvernay Shale Formation
Bibliographic record
Abstract
Abstract In the last five years the Duvernay formation has become one of the most sought after plays in Canada. This case study takes a closer look at the completion methods and corresponding production of the Duvernay wells near Fox Creek, Alberta, Canada. With the Duvernay being a highly over-pressured reservoir, the completion system is a major component in the success of conducting hydraulic fracturing. In the first two years, different completion tools were applied and after 2012 the vast majority of the completion methods became either the plug and perf cemented liner or the open-hole packer ball-drop systems. Through the production case study that has been completed, it was found that the open-hole completion method had better cumulative production and rate compared to the plug and perf method by ~23% at 3.5 years, although early time production has similar results. A big component that affects the production and completion method is the lateral length, the total stages and as a combined result, the stage spacing. The lateral reach ranged from 900 m to 3000 m with stage spacing ranging from 25 m to 800 m and production results varying by 253%. The most common number of frac stages per well was 16 stages but the 20 stage wells had 78% more production after 1 year. Interestingly the higher number of stages, (up to 59 per well) had results that deviated substantially below the average, as well as above as expected. Based on the five-year learning curve, some of the initial wells that were deemed underperformers were re-fractured with new perforation clusters throughout the well placed between the old perforation clusters. A particularly interesting example well re-stimulated with proppant and near wellbore diverter added 254% incremental recoverable gas reserves and 295% more condensate reserves. The results of this re-fracture will be examined as part of this study of current and past completions design.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".