XU Yinbo, YAO Shuqing, LUO Xiaoling, BI Caiqin. 2023: Logging response characteristics and identification model of oil shale of Lucaogou Formation in Shitoumei area of Santanghu Basin, Xinjiang. Geological Bulletin of China, 42(11): 1808-1817. DOI: 10.12097/j.issn.1671-2552.2023.11.002
    Citation: XU Yinbo, YAO Shuqing, LUO Xiaoling, BI Caiqin. 2023: Logging response characteristics and identification model of oil shale of Lucaogou Formation in Shitoumei area of Santanghu Basin, Xinjiang. Geological Bulletin of China, 42(11): 1808-1817. DOI: 10.12097/j.issn.1671-2552.2023.11.002

    Logging response characteristics and identification model of oil shale of Lucaogou Formation in Shitoumei area of Santanghu Basin, Xinjiang

    • This contribution studied the organic geochemistry and logging response characteristics of oil shale in Shitoumei area of Santanghu Basin and built a ΔlogR model based on the analysis of sample testing data and logging data, in order to establish a logging identification model of oil shale.The results show that the oil shale in the study area has high organic matter abundance and low maturity, and the organic matter type is Ⅰ—Ⅱ1 with a medium oil yield.Compared with surrounding rock, oil shale is characterized by high resistivity, high sonic interval transit time, low density and high natural gamma.The TOC of mudstone in the study area shows the best correlation with the resistivity and the sonic interval transit time logging and a calculation formula for ΔlogR model is thus established based on the superposition of the two logging curves.Then, through the coupling of the TOC data and ΔlogR, a logging identification model of TOC is established.The oil yield of oil shale in the study area has a good correlation with TOC, so the logging identification model of oil yield of oil shale is established based on the relevant formulas of oil yield and TOC, as well as the relevant formulas of TOC and ΔlogR.The correlation coefficient between the measured oil yield and the predicted oil yield based on this model can reach 0.86, indicating a good correlation.These models can be a basis for the subsequent identification of oil shale and the utilization of old well data in the study area.
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