Shale Pore Investigation beyond the Practical Pore Resolution Limits of Scanning Electron Microscopy
Bibliographic record
Abstract
Characterizing diverse pore types is essential for optimizing resource exploration in shale formations. Scanning electron microscopy (SEM) is the primary tool for this analysis; however, its practical pore resolution (PPR) limit of 30 nm hinders the quantitative analysis of smaller pores. To address this challenge, we introduced a method for characterizing mesopores below the SEM’s PPR threshold, focusing on the interclay mineral mesopores in Chang-7 shale as a case study. We employed low-pressure nitrogen adsorption and SEM to analyze the overall mesopore structures, confirming their self-similarity and dominance of interclay mineral mesopores. SEM digital analysis was used for the quantitative examination of interclay mesopores exceeding the PPR threshold. Additionally, a fractal model was developed using data from these larger mesopores and their fractal properties to predict the size distribution of super-PPR mesopores (2–30 nm). To validate this approach, we applied a same modeling method to predict the pore size distribution in the 30–60 nm range, and compared these predictions with measured values. With relative root-mean-square error (RRMSE) values ranging from 9.98 to 19.80%, our approach demonstrates high accuracy. This research advances mesopore characterization methods and offers deeper insights into hydrocarbon mobility and pore genesis in shale formations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".