A Comparative Study of Pore Size Distribution in Gas Shales
Bibliographic record
Abstract
Abstract Petrophysical characterization of unconventional rocks is an important challenge faced by the industry for reservoir evaluation. In particular, characterizing the pore size distribution (PSD) of tight rocks is challenging due to their small pore size and presence of clay minerals. In this paper, we compare the PSD of shale samples using both of the adsorption and desorption isotherms of water (H2O), nitrogen (N2), and carbon dioxide (CO2). The shale samples are collected from three wells completed in the Horn River Basin. A setup is designed to obtain the water sorption (adsorption and desorption) isotherms for shale samples. The model developed by Zolfaghari and Dehghanpour (2015) is used to calculate the PSD of shale samples from water sorption isotherms. BET (Brunauer-Emmett-Teller) analysis is used to obtain the N2 and CO2 sorption isotherms, and their corresponding PSDs. Also, SEM (Scanning Electron Microscope) images of the shale samples are utilized to visualize the pores of the shale samples. The comparative analysis of PSDs indicates that different methods give different PSDs. All of the calculated PSDs indicate that majority of the pores are smaller than ~10 nm. The portion of pores less than than ~1.5 nm is larger when the PSDs are calculated using the water sorption isotherms compared to that of the BET analysis. The PSDs calculated from the water sorption isotherms also show pores of larger than ~40 nm, which is in agreement with the SEM images of the shale samples. However, BET does not detect these large pores.
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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".