Effect of Lamination on Shale Reservoir Properties: Case Study of the Montney Formation, Canada
Bibliographic record
Abstract
The presence of lamination on sedimentary rocks is a distinct characteristic, particularly in shales. They are distinct due to the contrast between successive layers with regard to grain size, composition, color, and sedimentary structures, such as graded beds. Typically, the degree of lamination is controlled by the sedimentation rate and flow regime. Herein, we developed a mudstone classification scheme in terms of lamination because lamination-based shale facies are related to differing features in mineral composition, porosity, and Young’s modulus. This study also attempts to verify whether wireline log patterns are relevant to shale lithofacies. The relationship between the porosity and lamination of the Montney Formation can be used to estimate reservoir properties. Our results show that an increased silt lamina in mudstone leads to an increase in the quartz and calcite contents and a decrease in the clay content, which increases the porosity, permeability, and Young’s modulus. However, reservoir quality is not solely dependent on lamination because of the complex interaction between components. The degree of lamination affected the neutron, density, and sonic log responses. Furthermore, the presence of lamination tends to decrease the neutron percentage, with similar trends in density and sonic log box plots in the study area. When the percentage of clay or cement material decreases, the neutron and density log responses diminish. Meanwhile, when the rock texture variation increases with an increase in the degree of lamination, the sonic log response decreases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".