Characterization of gas shale pore systems by porosimetry, pycnometry, surface area, and field emission scanning electron microscopy/transmission electron microscopy image analyses: Examples from the Barnett, Woodford, Haynesville, Marcellus, and Doig units
Bibliographic record
Abstract
Abstract The nanometer-scaled pore systems of gas shale reservoirs were investigated from the Barnett, Marcellus, Woodford, and Haynesville gas shales in the United States and the Doig Formation of northeastern British Columbia, Canada. The purpose of this article is to provide awareness of the nature and variability in pore structures within gas shales and not to provide a representative evaluation on the previously mentioned North American reservoirs. To understand the pore system of these rocks, the total porosity, pore-size distribution, surface area, organic geochemistry, mineralogy, and image analyses by electron microscopy were performed. Total porosity from helium pycnometry ranges between 2.5 and 6.6%. Total organic carbon content ranges between 0.7 and 6.8 wt. %, and vitrinite reflectance measured between 1.45 and 2.37%. The gas shales in the United States are clay and quartz rich, with the Doig Formation samples being quartz and carbonate rich and clay poor. Higher porosity samples have higher values because of a greater abundance of mesopores compared with lower porosity samples. With decreasing total porosity, micropore volumes relatively increase whereas the sum of mesopores and macropore volumes decrease. Focused ion beam milling, field emission scanning electron microscopy, and transmission electron microscopy provide high-resolution (∼5 nm) images of pore distribution and geometries. Image analysis provides a visual appreciation of pore systems in gas shale reservoirs but is not a statistically valid method to evaluate gas shale reservoirs. Macropores and mesopores are observed as either intergranular porosity or are confined to kerogen-rich aggregates and show no preferred orientation or align parallel with the laminae of the shale. Networks of mesopores are observed to connect with the larger macropores within the kerogen-rich aggregates.
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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 | 0.000 | 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) | 0.001 | 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".