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    Geochemical Evidence for Oil and Gas Expulsion in Triassic Lacustrine Organic-Rich Mudstone, Ordos Basin, China.Tongwei Zhang, Xiangzeng Wang, Jianfeng Zhang, Xun Sun, Kitty L. Milliken, Stephen C. Ruppel & Daniel Enriquez - 2017 - Interpretation: SEG 5 (2):SF41-SF61.
    Forty-six core samples were collected from a deep well that penetrated organic-rich layers of the Chang 7, 8, and 9 members of the Yanchang Formation in the Ordos Basin. Tests for total organic content, Rock-Eval pyrolysis, X-ray diffraction mineralogy, and molecular composition of gases released from rock crushing were conducted. Analytical results indicate that TOC and clay contents are elevated. The organic matter -rich mudstone in the Triassic Yanchang Fm suggests good-to-excellent source potential for oil generation. Its thermal maturity is (...)
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  2.  39
    Chemostratigraphic Insights Into Fluvio-Lacustrine Deposition, Yanchang Formation, Upper Triassic, Ordos Basin, China.Harry Rowe, Xiangzeng Wang, Bojiang Fan, Tongwei Zhang, Stephen C. Ruppel, Kitty L. Milliken, Robert Loucks, Ying Shen, Jianfeng Zhang, Quansheng Liang & Evan Sivil - 2017 - Interpretation: SEG 5 (2):SF149-SF165.
    A chemostratigraphic study of a 300 m long core recovered from the southeastern central Ordos depocenter reveals thick intervals of fine-grained, organic-rich lacustrine strata, interpreted to represent deepwater deposition under meromictic conditions during lake highstand phases, interspersed with thick intervals of arkosic sandstones, reflective of fluvio-deltaic deposition during lake lowstand phases. Along with elevated concentrations of %Al, traditionally a proxy for clay content, maximum total-organic-carbon values in the deepwater lacustrine facies reach 8%, with average values of approximately 3%. The fine-grained, (...)
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    A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series.Hui Hu, Jianfeng Zhang & Tao Li - 2020 - Complexity 2020:1-21.
    Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average, the gradient boosting regression tree, the support vector regression, and the backpropagation neural network. The latest decomposition model, the (...)
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