Reservoir characterization of the Montney Shale – integrating seismic inversion with microseismic
Bibliographic record
Abstract
Understanding the optimization of heterogeneity of unconventional shale reservoirs prior to hydraulic fracturing is important for optimizing hydrocarbon production and recovery. Early prediction of geomechanical heterogeneity impacts the efficiency of horizontal well placement and completion design. Heterogeneity within shale reservoirs is influenced by composition and textural variation of the rock, ie, the rock quality. Rock quality can be evaluated or predicted with seismic-derived rock properties. This study shows how seismic data are used to determine rock quality through a multi-attribute analysis of wells logs integrated with post-stack and pre-stack inversion to characterize the Montney Shale at Pouce Coupe, Alberta (Duenas, 2014). The heterogeneity analysis combined with microseismic data and production profiles of the two horizontal wells in the area shows that lithology has a major influence on the rock quality of the Montney interval. The combined interpretation of this work with an understanding of the natural fracture system and the stress state of the reservoir can provide a rock quality index (RQI). This RQI can aid in future exploration and operational development of the Montney and other shale reservoirs worldwide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".