Investigating Pore Characteristics and Their Dependence on Shale Composition: Case Study from a Permian Basin in India
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Shale reservoirs, often acting as caprocks for conventional hydrocarbon reservoirs, exhibit moderate to high porosity and remarkably low permeability. Organic-rich shales serve as reservoirs for unconventional hydrocarbons. This study focused on evaluating the characteristics of the source rocks and the factors influencing pore parameters in organic-rich shale from a Permian Basin in India, exploring its feasibility as both a CO 2 sink and a natural gas source. Experimental techniques were employed to explore the mineral and the organic matter characteristics along with attributes of the pores hosted within them. The investigated shales displayed diverse thermal maturity levels, spanning from that in oil-prone to gas-prone zones, with the total organic carbon content varying from 0.72 to 24.98 wt %, indicating substantial organic richness. Rock-Eval pyrolysis results revealed a range of thermal maturity ( T max ) values between 426 and 474 °C, while X-ray diffraction analysis indicated significant quantities of illite and kaolinite, along with trace amounts of pyrite in certain samples. Field-emission scanning electron microscopy imaging and its detailed interpretation provided valuable insights into the pore structure and arrangement. In our study, we found that both the clay content and the organic matter significantly contribute to gas adsorption. While clay content strongly influences mesopore attributes, the organic matter predominantly affects micropore attributes. Furthermore, a direct relationship among fractal dimension, surface area, and pore volume, indicating increased complexities with these variables. Our examination of mesopore fractal attributes revealed that smaller mesopores exhibit a more convoluted and irregular configuration in comparison to the larger ones. These findings provide significant insights into the pore morphology of the analyzed shale sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".