From Micropores to Macropores: Investigating Pore Characteristics of Longmaxi Shale in the Sichuan Basin
Bibliographic record
Abstract
Pore characteristics are crucial in the occurrence, aggregation, migration, and potential for CO 2 sequestration in shale gas reservoirs. We employed N 2 adsorption/desorption, CO 2 adsorption, mercury intrusion porosimetry (MIP), focused ion beam-scanning electron microscopy (FIB-SEM), and three-dimensional reconstruction of FIB-SEM images to characterize the pore characteristics in the Longmaxi Formation of the Sichuan Basin, southwestern China. These methods allowed for the detailed study of microstructure, porosity, and permeability down to the micropore scale (<2 nm). Meanwhile, N 2 and CO 2 isotherm analyses revealed a range of pore sizes, including micropores, mesopores (2–50 nm), and macropores (>50 nm). Micropores significantly contribute to the specific surface area, while mesopores and macropores predominantly contribute to pore volume. MIP results indicated extremely high pore tortuosity and connected porosity less than 1.43%. FIB-SEM and its three-dimensional reconstructions showed significant pore distribution within organic matter. At the FIB-SEM resolution (5 and 10 nm), pore connectivity is notably poor, with many large pores several hundred nanometers in diameter, undetected by the N 2 isotherm method. Permeabilities estimated by FIB-SEM are 1–2 orders of magnitude lower than those measured by MIP, exhibiting anisotropy. Assessments of gas in place and CO 2 storage capacity suggest that porosity evaluations via MIP may underestimate the quantifiable gas content in shale formations. The combined use of N 2 adsorption/desorption, CO 2 adsorption, MIP, and FIB-SEM techniques for integrating pore size characteristics offers a holistic perspective of the pore size spectrum in shale gas reservoirs, effectively addressing the limitations inherent in each individual method.
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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 | 0.001 | 0.001 |
| 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 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".