Gas Adsorption Modeling in Multi-Scale Pore Structures of Shale
Bibliographic record
Abstract
Abstract Shale pore space has a wide distribution of sizes (nm-μm) and complex configurations. Better knowledge of gas adsorption characteristics in real pore space is crucial for estimating shale gas-in-place. We develop a novel methodology to accurately and effectively calculate gas adsorption isotherms in multi-scale pore networks that simulate real pore structures inside shale. The influence of water saturations (in kerogen and clay) and pore distributions on gas adsorption is examined with our new model. 3D pore networks which connect both mesopores (2-50nm) and macropores (>50nm) are developed based on 2D SEM images and mercury intrusion analysis. Interparticle pores and pores inside kerogen have different morphologies from the pores in clay agglomerates in our pore networks. The gas adsorption on each dry pore/throat's surface is realized by capillary condensation with the Kelvin equation, which relates capillary condensation to pore/throat structure, different solid (clay and kerogen) surface characteristics and fluid properties. Moreover, we use the gas-liquid Gibbs adsorption model for gas adsorption on wet solid surfaces with water present, which is not considered in the literature. 3D pore networks and nitrogen adsorption isotherms are generated for the Silurian Longmaxi Formation shale samples. The simulated nitrogen adsorption isotherms are comparable to adsorption test results. The comparison confirms that both accurate adsorption modeling on pore surfaces and reliable pore space reconstruction are important for designing and analyzing adsorption measurements. Sets of methane adsorption isotherms are further calculated on different pore networks. Each pore network is assigned a unique combination of clay content, total organic carbon content and pore size distribution (PSD). When the pore volume is constant, shale has higher adsorption amount of methane with decreasing pore sizes. When the water saturation increases, water will first occupy the void space in clay from small pores to large pores and then extend to pores inside kerogen. It is concluded that the adsorption amount of methane could be significantly reduced by 50% when water saturation in pore space increases from zero to 30%. Different from previous adsorption modeling studies on single dry pore/throat or a bundle of dry tubes, this study considers the adsorption modeling on a pore network that connects pores and throats with different sizes, wet or dry surfaces and various morphologies. This methodology and simulation results are reliable and effective for fundamental study and field performance estimation of gas adsorption in shale reservoirs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".