NANOSCALE PORE SIZE DISTRIBUTION EFFECTS ON GAS PRODUCTION FROM FRACTAL SHALE ROCKS
Bibliographic record
Abstract
Effect of nanoscale pore size distribution (PSD) on shale gas production is one of the challenges to be addressed by the industry. An improved approach to study multi-scale real gas transport in fractal shale rocks is proposed to bridge nanoscale PSD and gas filed production. This approach is well validated with field tests. Results indicate the gas production is underestimated without considering a nanoscale PSD. A PSD with a larger fractal dimension in pore size and variance yields a higher fraction of large pores; this leads to a better gas transport capacity; this is owing to a higher free gas transport ratio. A PSD with a smaller fractal dimension yields a lower cumulative gas production; this is because a smaller fractal dimension results in the reduction of gas transport efficiency. With an increase in the fractal dimension in pore size and variance, an apparent permeability-shifting effect is less obvious, and the sensitivity of this effect to a nanoscale PSD is also impaired. Higher fractal dimensions and variances result in higher cumulative gas production and a lower sensitivity of gas production to a nanoscale PSD, which is due to a better gas transport efficiency. The shale apparent permeability-shifting effect to nanoscale is more sensitive to a nanoscale PSD under a higher initial reservoir pressure, which makes gas production more sensitive to a nanoscale PSD. The findings of this study can help to better understand the influence of a nanoscale PSD on gas flow capacity and gas production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".