Pore Size Distribution from Water Adsorption Isotherm
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 develope a model to characterize PSD of shales using water adsorption isotherms. We apply the model on several shale samples and compare the results with the PSDs obtained from BET (Brunauer-Emmett-Teller) analysis using the N2 and CO2 adsorption isotherms. The proposed model describes the relationship between the cumulative adsorbed water and the size of the invaded pores due to capillary condensation during the water adsorption process. A set-up is designed to obtain water adsorption isotherms. The shale samples are placed in a sealed environment with a controlled relative humidity (RH). Different saturated salt solutions are used to control RH. At the end of the adsorption process, the model is applied to calculate PSD of the shale samples using their water adsorption isotherm. In order to evaluate the model results, we used BET analysis to obtain PSD from the N2 and CO2 adsorption isotherms. Moreover, the specific surface area (SSA), pore volume (PV) and the average pore size of the shale samples are also obtained from the BET analysis to compare the proposed model and BET results. The results of both BET and the proposed model indicate that the majority of the pores are smaller than 10 nm. However, the model results from the water adsorption show a bimodal PSD, while the BET analysis shows a unimodal PSD. Also, the model calculates small pores of less than 1 nm, while BET does not detect these pores. Water adsorption by clay minerals at low RH values is a possible reason for this discrepancy. Furthermore, the sample with higher clay content shows larger hysteresis at the end of the sorption (adsorption-desorption) experiment; suggesting that the clay-bound water cannot be easily removed during the desorption process.
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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".