Petrophysical approach for estimating porosity, clay volume, and water saturation in gas-bearing shale: A case study from the Horn River Basin, Canada
Bibliographic record
Abstract
Shale gas exists partly as a gas adsorbed to clay mineral and partly as a free gas within the pores.To evaluate a shale gas reservoir and calculate total gas content, it is essential to accurately analyze porosity, clay volume, and water saturation.In this study, we estimate these factors for the Horn River Basin using various types of well log data such as density log, sonic log, resistivity log, and neutron porosity log.Because a simple density porosity equation results in unreasonable fluid densities, we estimate porosity using total organic carbon.Based on brittleness, an empirical equation for clay volume is defined.Because the correlation coefficient between core-tested clay volume and water saturation is greater than 0.9, the empirical equation for water saturation is also defined in terms of brittleness.For the shale gas reservoir in the Horn River Basin, porosity can be calculated by using a linear equation with the density log, and clay volume and water saturation can be calculated by using a linear relationship with Young's modulus and Poisson's ratio.This study suggests that porosity, clay volume, and water saturation models can be established using the elastic model built on seismic inversion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".