Impact of Shale Properties on Pore Structure and Storage Characteristics
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Bibliographic record
Abstract
Abstract Characterising the pore structure of gas shales is of critical importance to establish the original gas in place and flow characteristics of the rock matrix. Methods of measuring pore volume, pore size distribution, and sorptive capacity of shales, inherited from the coalbed methane and conventional reservoir rock analyses, although widely applied, are of limited value in characterising many shales Helium which is routinely used to measure shale skeletal and grain density, permeability and diffusivity, has greater access to the fine pore structure of shale than larger molecules such as methane. Utilizing gases other than He to measure porosity or flux requires corrections for sorption to be incorporated in the analyses. Since the permeability of shales vary by several orders of magnitude with effective stress, methods that do not consider effective stress such as crushed permeability, permeability from Hg porosimetry, and from desorption are of limited utility and may be at best instructional. For shales investigated to date, clay-rich rocks have higher porosity and permeability than biogenic silica-rich shales or carbonate-rich shales. Shales rich in detrital quartz have higher porosity and permeability than shales rich in biogenic quartz and hence simply knowing the mineralogy of a shale may not be diagnostic. The porosity of most shales is mainly dependent on the degree of pore volume development in pores less than 10 um. Quantifying total gas in place in shales by much of the industry using coal desorption methods and porosity and water saturation determinations, developed for conventional reservoir rocks, may lead to substantial errors. Canister ‘desorption' methods applied to gas shales routinely captures free and solution gas as well as sorbed gas which, if considered as only sorbed gas, results in a significant overestimation of gas in place. A proprietary method of analyses, referred to as MARIO, results in rigorous total gas in place determinations that avoids errors including those associated with molecular sieving and provides a maximum value of the sorbed gas contribution to total gas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it