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Yiwen Ju

Researcher at Chinese Academy of Sciences

Publications -  107
Citations -  2302

Yiwen Ju is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Coal & Oil shale. The author has an hindex of 20, co-authored 94 publications receiving 1470 citations. Previous affiliations of Yiwen Ju include China University of Petroleum.

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Coalbed methane sorption related to coal deformation structures at different temperatures and pressures

TL;DR: In this article, coal methane adsorption is investigated using three types of tectonically deformed coal with vitrinite reflectance of about 0.9% at different temperatures and pressures.
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Fractal characteristics of pores in non-marine shales from the Huainan coalfield, eastern China

TL;DR: In this paper, the fractal characteristics of non-marine organic shales from the Huainan coalfield in East China have been investigated, and the pore morphology is dominated by cylindrical and slit-shaped types.
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Structure and coalbed methane occurrence in tectonically deformed coals

TL;DR: Wang et al. as mentioned in this paper presented a summary on the research progress in tectonically deformed coals (TDC)s structural-genetic classification, tectonic strain influence on coal microstructure, coal porosity system, coal chemical structure and constituents, and their relationship with the excess coalbed methane.
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Impact of tectonism on pore type and pore structure evolution in organic-rich shale: Implications for gas storage and migration pathways in naturally deformed rocks

TL;DR: In this paper, the relationship between tectonism and pore structure using a combination of mineralogy, organic geochemistry, low pressure nitrogen adsorption, low-field nuclear magnetic resonance (NMR), optical microscopy, and high-resolution scanning electron microscopy (SEM) was investigated.
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Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin

TL;DR: The application of support vector machine (SVM) is superior to the traditional empirical risk minimization principle employed by artificial neural network (ANN) and is used to build a 3-D Marcellus Shale lithofacies model, which assists in identifying higher productive zones, especially with thermal maturity and natural fractures.