2020
DOI: 10.1016/j.coal.2020.103591
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Differential evolution and the influencing factors of low-maturity terrestrial shale with different types of kerogen: A case study of a Jurassic shale from the northern margin of Qaidam Basin,China

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Cited by 14 publications
(7 citation statements)
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“…Moreover, the nonporous OM in the Longmaxi shale in South China corresponds to relatively heavy conversion products (micrinite) of oil-prone kerogen macerals considering their thermal degradation process . In addition, the previous study observed that the surfaces of vitrinite are always nonporous …”
Section: Discussionmentioning
confidence: 94%
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“…Moreover, the nonporous OM in the Longmaxi shale in South China corresponds to relatively heavy conversion products (micrinite) of oil-prone kerogen macerals considering their thermal degradation process . In addition, the previous study observed that the surfaces of vitrinite are always nonporous …”
Section: Discussionmentioning
confidence: 94%
“…44 In addition, the previous study observed that the surfaces of vitrinite are always nonporous. 50 Considering the above, the pore-filling OM grains and the fracture-filling OM strips are interpreted to have formed from increments of viscous flow of oil (solid bitumen/ pyrobitumen from high to over mature stages) into the original pore network. 43 Furthermore, the particulate autochthonous OM grains are solid products with no migration 28,53 after the partial conversion of kerogen into oil and gas.…”
Section: Discussionmentioning
confidence: 99%
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“…Pores inside kerogen are highly heterogeneous. 16,88,125 Zhang has differentiated the type of pores based on initial structures of organic matter, 126 but those pores are preserved pores of the initial organic matter, which change with thermal maturity. No studies were done on isolated maceral pores, which could shed some light on pores of different macerals.…”
Section: Discussionmentioning
confidence: 99%
“…Six optimizers were used in this study to conduct feature selection. These are: differential evolution (DE) (Zhang et al, 2020); cuckoo search optimizer (CSO) (Wood, 2016); Jaya (Rao, 2016), particle swarm optimizer (PSO, Atashnezhad et al, 2014); sin-cosine algorithm (SCA) (Abualigah and Diabat, 2021); and Salp swarm algorithm (Faris et al, 2020). These optimizers were selected based on their rapid execution times and accuracies observed with a range of lithological datasets.…”
Section: Workflow Requirementsmentioning
confidence: 99%