Application of pore throat size distribution data to petrophysical characterization of Montney tight-gas siltstones
Bibliographic record
Abstract
Abstract Pore throat size distribution (PSD) is a fundamental characteristic that influences the large-scale petrophysical properties and reservoir quality of tight rocks in unconventional plays. Here, we use PSD data obtained from mercury intrusion capillary pressure (MICP) and nuclear magnetic resonance (NMR) measurements of Montney tight-gas siltstones to investigate the relationship of pore throat size to several other petrophysical attributes including porosity, permeability and total organic carbon (TOC) content. We find that pore size correlates positively with porosity but negatively with TOC. Additionally, we evaluate methods to estimate absolute permeability and gas relative permeability from MICP data and compare the modeled results with measured data. Estimates of absolute permeability using peak pore throat diameter in the bundle of tortuous tubes model are found to closely match measured permeability values when a tortuosity factor of 3 is applied. Estimates of relative permeability using MICP data in a modified Purcell approach are found to be comparable to measured values only if gas is considered as the wetting phase rather than the non-wetting phase. Hydrocarbon-wet reservoir conditions and the negative correlation of porosity with TOC are both consistent with the presence of solid bitumen/pyrobitumen as a pervasive pore-filling phase (Wood et al., 2018, this issue).
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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 | 0.001 |
| 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".