Modeling of Supercritical Fluid Adsorption on Organic-Rich Shales and Coal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An important component of hydrocarbon storage in coalbed methane and organic-rich shales is sorption within organic matter. Natural gas sorption isotherms measured in these systems may include a combination of adsorption onto the internal surface area and absorption within the organic matter. The focus of the current work is the modeling of adsorbed fluids. There have been multiple models proposed for modeling single- and multi-component adsorption on coal and shale, of which the most popular remain the simple Langmuir model and its extension to multi-components. In this work, we first review various approaches used for modeling adsorption on coal and organic-rich shale, including newer approaches such as the 2D Equation-of-State (2D-EOS) method. We discuss extensions of simple approaches, such as the Langmuir and Dubinin- Radushkevich (D-R) equations, to modeling supercritical, single-component fluids. The applications of these models to coal and shale datasets will be demonstrated. An important finding of the current work is that the simple Langmuir/D-R models are mostly adequate for modeling supercritical, single-component adsorption on coals and shales, provided that certain adjustments are made to account for supercritical fluid properties, such as adsorbed-phase density. The 2D-EOS model was found to be superior for modeling high-pressure CO2 excess adsorption on shale, however. Several multi-component adsorption models are used to predict binary component adsorption (CH4-CO2) on shale and to investigate CO2 selectivity over CH4 for two shale systems as a function of pressure and gas composition. This information is useful for designing enhanced recovery operations in dry shale. A dataset containing heavy hydrocarbon adsorption on shale was also examined to determine the importance of adsorption in "liquid-rich" systems. The change in selectivity of heavier hydrocarbons with addition of CO2 was examined using binary gas adsorption modeling; it was found that CO2 could reduce heavy hydrocarbon component selectivity which provides a possible mechanism for enhanced recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it