Methane Absolute Adsorption in Kerogen Nanoporous Media with Realistic Continuous Pore Size Distributions

Pang, Wanying; Jin, Zhehui

doi:10.1021/acs.energyfuels.0c01886

Cited by 14 publications

(13 citation statements)

References 80 publications

(166 reference statements)

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“…99,279 In this regard, the OK lattice model can capture varying methane adsorption behaviors in micropores and mesopores, and by coupling with PSD, it can have a good agreement with molecular simulations in terms of methane absolute adsorption in model kerogen nanoporous media. 100 However, rock heterogeneity has not been fully taken into account in the present study nor has it been treated without correlating with actual rock composition analysis. Furthermore, the effect of pore geometry has not been investigated well, while most conversion methods are based on slit geometry.…”

Section: Summary About Various Conversion Methods Andmentioning

confidence: 99%

“…It is shown that the gas adsorption capacity in organic matter is generally larger than that in inorganic matter. 29,70,307 In addition, molecular simulations show that the methane maximum excess adsorptions in carbon micropores and mesopores at 333.15 K are within the range of 0.0036−0.006 mmol/m 2 , 100 while those in illite are within the range of 0.0017−0.0024 mmol/m 2 . 99 Similar observations have been reported as listed in Table 5.…”

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 95%

“…On the other hand, it has been reported that while m ex per unit surface area is dependent on the pore width in micropores, it becomes insensitive to pore width in mesopores. 100,304 Rock compositions, 114 moisture, 28,306 and surface roughness and chemistry 50,305 can also play important roles in methane adsorption in nanoporous media. Xiong et al 114 performed GCMC simulations to study methane adsorption in carbon, clay (e.g., illite and montmorillonite), and quartz slit pores.…”

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 99%

“…They found that the regressed OK model shows monolayer adsorption behaviors, while the regressed maximum adsorbed density should be less than the theoretical limit of methane density (560 kg/m 3 ) by assuming a hexagonal closed packing (HCP). Moreover, Pang et al 100 built a model kerogen nanoporous media with a realistic continuous PSD. The methane adsorption is characterized into six distinct types based on the density profiles obtained from the GCMC simulation.…”

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 99%

“…28,119−121 In addition, these statistical thermodynamic approaches have also been applied to study the conversion of excess adsorption into absolute adsorption. 99,100,122 Although these approaches provided important insights into the conversion of excess adsorption to absolute adsorption, daunting challenges still remain in current studies due to the complex shale media characteristics including rock heterogeneity and pore size heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review about Methane Adsorption in Shale Nanoporous Media

2021

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Shale/tight gas plays an increasingly important role to meet the growing global energy demand and reduce carbon emissions. Unlike conventional reservoirs, shale formations are subject to rock heterogeneity and have pore size distributions ranging from sub-1 nm to a few micrometers. Thanks to the large number of nanosized pores, adsorbed methane capacity plays a dominant role in total shale gas-in-place. Methane adsorption behaviors can vary drastically in micropores and mesopores, and rock surface type may also greatly affect its adsorption. In this review, we provide a systematic discussion on measurements of shale rock properties including rock compositions and pore structures such as specific surface area (SSA) and pore size distribution (PSD), which are important parameters for methane adsorption in shale nanoporous media. We also provide in-depth discussions on experimental measurements on methane (excess) adsorption in shale nanoporous media, methane adsorption behavior characterization based on molecular simulations, and various excess-adsorption-to-absolute-adsorption conversion methods. We pay particular attention to the assumptions and working mechanisms proposed in various interpretation methods which are embedded in pore structures (SSA and PSD) and absolute adsorption characterizations. In the end, we summarize the key challenges in the methane adsorption characterization in shale media.

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Section: Summary About Various Conversion Methods Andmentioning

confidence: 99%

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 95%

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 99%

Section: Molecular Simulations On Methane Adsorptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review about Methane Adsorption in Shale Nanoporous Media

2021

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Predicting the Gas Storage Capacity in Shale Formations Using the Extreme Gradient Boosting Decision Trees Method

Wang,

Li,

Huo

et al. 2024

Energy Tech

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Accurate shale gas reserves estimation is essential for development. Existing machine learning (ML) models for predicting gas isothermal adsorption are limited by small datasets and lack verified generalization. We constructed an “original dataset” containing 2112 data points from 11 measurements on samples from 8 formations in 3 countries to develop ML‐based prediction models. Similar to previous ML models, total organic matter, pressure, and temperature are characterized as the three most significant features using the mean impurity method. In contrast to previous ML models, the study reveals that these three features are inadequate to be used to make reasonable predictions for the datasets from the measurements different from those used to train the models. Instead, the extreme gradient boosting decision trees (XGBoost) model with two more features (specific surface area and moisture) exhibits good robustness, generalization, and precision in the prediction of gas isothermal adsorption. Overall, An XGBoost model with optimal input features is developed in this work, which exhibits both good performance in gas adsorption prediction and good potential for the estimation of gas storage in shale gas development.

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