Innovative methods for flow-unit and pore-structure analyses in a tight siltstone and shale gas reservoir
Bibliographic record
Abstract
Abstract Tight gas reservoirs are notoriously difficult to characterize; routine methods developed for conventional reservoirs are not appropriate for tight gas reservoirs. In this article, we investigate the use of nonroutine methods to characterize permeability heterogeneity and pore structure of a tight gas reservoir for use in flow-unit identification. Profile permeability is used to characterize fine-scale (<1 in. [<2.5 cm]) vertical heterogeneity in a tight gas core; more than 500 measurements were made. Profile permeability, although useful for characterizing heterogeneity, will not provide in-situ estimates of permeability; furthermore, the scale of measurement is much smaller than log scale. Pulse-decay permeability measurements collected on core plugs under confining pressure were used to correct the profile permeability measurements to in-situ stress conditions, and 13-point averages of profile permeability were used to relate to log-derived porosity measurements. Finally, N2 adsorption, a new method for tight gas was used to estimate the pore-size distribution of several tight gas samples. A unimodal or bimodal distribution was observed for the samples, with the larger peak corresponding to the dominant pore-throat size, as confirmed by independent methods. Furthermore, the adsorption-desorption hysteresis loop shape was used to interpret the dominant pore shape as slot-shaped pores, which is typical of many tight gas reservoirs. The N2 adsorption method provides rapid analysis and does not suffer from some of the same limitations of Hg injection. In the future, we hope that the N2 adsorption method may prove useful for flow-unit characterization (based on dominant pore size) of fine-grained (siltstone-shale) tight gas reservoirs.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".