Petrophysics of Triple-Porosity Tight Gas Reservoirs With a Link to Gas Productivity
Bibliographic record
Abstract
Summary Petrographic work on thin sections from rock samples collected in tight gas sandstones of the western Canada sedimentary basin (WCSB) shows that the sandstones are composed of intergranular, microfracture and slot, and isolated noneffective porosities. The petrographic observations of these triple-porosity rocks have led to a petrophysical interpretation with the use of a triple-porosity model. Tight gas reservoirs are very complex heterogeneous systems that have been evaluated in the past mostly with single-porosity models. We propose that these types of reservoirs can be represented better by triple-porosity models for more rigorous quantitative petrophysical characterization. The triple-porosity model discussed in this paper fits the petrographic observations very well, leading to a more rigorous characterization of effective and noneffective porosity. The petrographic and core-calibrated triple-porosity model is then used for well-log interpretation of those wells when these data are not available. The result is a reasonable quantitative characterization of the tight gas reservoir that can be used for improving hydraulic-fracturing design, flow-units determination, reservoir engineering, and simulation studies. The data can be determined at room conditions and simulated conditions of net stress. It is concluded that honoring with a triple-porosity model the different types of porosities observed in thin sections and cores leads to more-rigorous and -useful petrophysical interpretations that can be linked to gas productivity.
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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 | 0.001 |
| 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".