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Geochemical Characteristics and Process of Hydrocarbon Generation Evolution of the Lucaogou Formation Shale, Jimsar Depression, Junggar Basin

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  • Wenjun He

    (College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
    Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China)

  • Yin Liu

    (Key Laboratory of Deep Oil and Gas, School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China)

  • Dongxue Wang

    (Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China)

  • Dewen Lei

    (College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
    Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China)

  • Guangdi Liu

    (College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China)

  • Gang Gao

    (College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China)

  • Liliang Huang

    (College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
    Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China)

  • Yanping Qi

    (Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China)

Abstract

Lacustrine shale, represented by the Middle Permian Lucaogou Formation in the Jimsar Depression in the eastern Junggar Basin, has become one of the main areas of shale oil exploration in China. In this study, we used 137 samples of shale from the Lucaogou Formation, drawn from 14 wells in the Jimsar Depression, to investigate their characteristics of pyrolysis, organic carbon and soluble organic matter content, biomarkers, organic microscopic composition, and vitrinite reflectance. Basin simulation and hydrocarbon generation thermal simulation experiments were also conducted in a closed system. The results of this study indicate that the input of an algae source was dominant in the source rocks of the Lucaogou Formation, that the water in which the rocks were deposited had high salinity and strong reducibility, and that the source rocks were oil-prone. The Lucaogou source rocks generally had good hydrocarbon generation capability, but showed significant heterogeneity. At the end of the Cretaceous period, the shales in the Lucaogou Formation entered the oil-generation window as a whole. Currently, the shales of the Lucaogou Formation are generally in the high-maturity stage in the deep part of the depression, producing a large amount of high-maturity oil and condensate gas, while those in the shallow part have relatively low maturity and can only produce a large amount of conventional crude oil. The maximum crude oil generation rate of the Lucaogou Formation shale obtained from the thermal simulation results was 220.2 mg/g of the total organic carbon (TOC), and the maximum hydrocarbon expulsion efficiency was estimated to be 59.3–76.4%.

Suggested Citation

  • Wenjun He & Yin Liu & Dongxue Wang & Dewen Lei & Guangdi Liu & Gang Gao & Liliang Huang & Yanping Qi, 2022. "Geochemical Characteristics and Process of Hydrocarbon Generation Evolution of the Lucaogou Formation Shale, Jimsar Depression, Junggar Basin," Energies, MDPI, vol. 15(7), pages 1-19, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2331-:d:777559
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    References listed on IDEAS

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    1. Yuhan Jiang & Dujie Hou & Hang Li & Ziming Zhang & Ruibo Guo, 2020. "Impact of the Paleoclimate, Paleoenvironment, and Algae Bloom: Organic Matter Accumulation in the Lacustrine Lucaogou Formation of Jimsar Sag, Junggar Basin, NW China," Energies, MDPI, vol. 13(6), pages 1-23, March.
    2. Yang Su & Ming Zha & Keyu Liu & Xiujian Ding & Jiangxiu Qu & Jiehua Jin, 2021. "Characterization of Pore Structures and Implications for Flow Transport Property of Tight Reservoirs: A Case Study of the Lucaogou Formation, Jimsar Sag, Junggar Basin, Northwestern China," Energies, MDPI, vol. 14(5), pages 1-20, February.
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    Cited by:

    1. Liu, Yazhou & Zeng, Jianhui & Qiao, Juncheng & Yang, Guangqing & Liu, Shu'ning & Cao, Weifu, 2023. "An advanced prediction model of shale oil production profile based on source-reservoir assemblages and artificial neural networks," Applied Energy, Elsevier, vol. 333(C).

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