Measurements of gas permeability and diffusivity of tight reservoir rocks: different approaches and their applications
Bibliographic record
Abstract
Abstract Permeability and diffusivity are critical parameters of tight reservoir rocks that determine their viability for commercial development. Current methods for measuring permeability and/or diffusivity may lead to erroneous results when applied to very tight rocks including gas shales, coal, and tight gas sands, as well as rocks considered as seals for nuclear waste repositories and strata for geological sequestration of CO2. The use of He as routinely applied to measure porosity, permeability, and diffusivity may result in non‐systematic errors because of the molecular sieving effect of the fine pore structure to larger molecules such as reservoir gases. Utilizing gases with larger adsorption potentials than He, such as N2, and including all reservoir gases to measure porosity or permeability of rocks with high surface area is a viable alternative, but requires correcting for adsorption in the analyses. This study expands several approaches to measure permeability and diffusivity with considerations for gas adsorption, which has not been explicitly considered in previous studies. We present new models that explicitly correct for adsorption during pulse‐decay measurements of core under reservoir conditions, as well as on crushed samples used to approximate permeability or diffusivity. We also present a method to determine permeability or diffusivity from on‐site drill‐core desorption test data as carried out to determine gas in place in coals or gas shales. Our new approach utilizes late‐time data from experimental pressure‐decay tests, which we show to be more reliable and theoretically (and practically) accurate than the early‐time approach commonly used to estimate gas‐transport properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".