Integrated workflows for shale gas and case study results for the Horn River Basin, British Columbia, Canada
Bibliographic record
Abstract
The variation in well performance observed between various shale gas plays, and indeed within individual basins and on individual pads, has gone some way to dispelling myths regarding the perceived homogeneity of “shale gas” targets. With increased quantities of data and more determined analysis, we show that understanding the micro- and mesoscale heterogeneity can be advanced through interdisciplinary studies that incorporate traditional and advanced geophysical data and methods with geological understanding and engineering measurements. This understanding is critical in optimizing well placement, the spacing and length of horizontal wells, and hydraulic fracturing effort to maximize recovery. Specifically, we illustrate that in the Muskwa Formation and the Otter Park, Klua, and Evie members of the Horn River Formation, reservoir quality can be predicted using lambda-rho and mu-rho data extracted from AVO inversion studies. From log data, we show that the most prospective reservoir intervals are characterized by decreasing lambda, increasing mu and/or a lambda:mu ratio less than one.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".