Literature DB >> 31478655

Supercritical CO2 and CH4 Uptake by Illite-Smectite Clay Minerals.

Junyoung Hwang1, Ronny Pini1.   

Abstract

Clay minerals abound in sedimentary formations and the interaction of reservoir gases with their submicron features have direct relevance to many geoenergy applications. The quantification of gas uptake over a broad range of pressures is key toward assessing the significance of these physical interactions on enhancing storage capacity and gas recovery. We report a systematic investigation of the sorption properties of three source clay minerals-Na-rich montmorillonite (SWy-2), illite-smectite mixed layer (ISCz-1), and illite (IMt-2)-using CO2 and CH4 up to 30 MPa at 25-115 °C. The textural characterization of the clays by gas physisorption indicates that micropores are only partly accessible to N2 (77 K) and Ar (87 K), while larger uptakes are measured with CO2 (273 K) in the presence of illite. The supercritical excess sorption experiments confirm these findings while revealing differences in uptake capacities that originate from the clay-specific pore size distribution. The lattice density functional theory model describes accurately the measured sorption isotherms by using a distribution of properly weighted slit pores and clay-specific solid-fluid interaction energies, which agree with isosteric heats of adsorption obtained experimentally. The model indicates that the maximum degree of pore occupancy is universal to the three clays and the two gases, and it depends solely on temperature, reaching values near unity at the critical temperature. These observations greatly support the model's predictive capability for estimating gas adsorption on clay-bearing rocks and sediments.

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Year:  2019        PMID: 31478655     DOI: 10.1021/acs.est.9b03638

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

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Authors:  Pedram Mahzari; Thomas M Mitchell; Adrian P Jones; Donald Westacott; Alberto Striolo
Journal:  Sci Rep       Date:  2021-05-24       Impact factor: 4.379

2.  Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials.

Authors:  Giulia Lo Dico; Álvaro Peña Nuñez; Verónica Carcelén; Maciej Haranczyk
Journal:  Chem Sci       Date:  2021-06-02       Impact factor: 9.825

  2 in total

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