Contribution ratio and distribution patterns of multiple oil sources in the Yanchang Formation of the Ordos Basin: A study utilizing machine learning and interpretability techniques

doi:10.13745/j.esf.sf.2023.9.56

Abstract

Abstract:

The Yanchang Formation within the Ordos Basin hosts multiple sets of potential source rocks, all exhibiting similar biomarker properties. The conventional method of oil-source correlation has proven ineffective, leading to longstanding debates within the field. In response to these challenges, this study introduces a novel deep learning-based scheme for oil-source comparison, leveraging artificial intelligence methods for research in this domain. The study presents the following key findings and insights: (1) Development of a deep neural network model for identifying the oil source type of unknown samples by utilizing 42 biomarker parameters from a diverse set of mudstone and shale samples representing different oil groups within the Yanchang Formation as training data. The model achieved identification accuracies of 83.0% for Chang 7 mudstone and 79.6% for Chang 8-Chang 10 mudstone, successfully distinguishing the primary source rocks of the Yanchang Formation from hydrocarbon generation products. (2) Application of the model to analyze the oil source classification of numerous sandstone and oil samples. The study calculated the contribution ratios of various source rocks to each oil group within the Yanchang Formation, summarizing their distribution patterns. (3) Conducting sensitivity analysis of the model using the permutation feature importance (PFI) algorithm, revealing differences in biomarkers between the two main source rocks of the Yanchang Formation. These findings contribute to advancing artificial intelligence techniques and technologies in the field of petroleum molecular geochemistry, offering valuable insights for future research and applications.

Key words: machine learning, deep neural network, sensitivity analysis, Yanchang Formation of Ordos Basin, oil-source correlation

CLC Number:

TE122.11

SU Kaiming, XU Yaohui, XU Wanglin, ZHANG Yueqiao, BAI Bin, LI Yang, YAN Gang. Contribution ratio and distribution patterns of multiple oil sources in the Yanchang Formation of the Ordos Basin: A study utilizing machine learning and interpretability techniques[J]. Earth Science Frontiers, 2024, 31(3): 530-540.

Figures/Tables 13

Fig.1 Well sampling locations, sedimentary facies, and distribution of sand bodies in the Chang 7 oil group of the Yanchang Formation

Fig.2 Samples exhibiting abnormal abundance of C30 rearranged-hopanes (C30*) and their correlation with maturity parameters

Fig.3 Stratigraphic column and petroleum system of the Yanchang Formation. Adapted from [30-31].

Fig.4 Computing unit (A, adapted from [37]) of the Multilayer Perceptron (MLP) and training process of the neural network (B, adapted from [36-37])

Table 1 Biomarker parameters selected as characteristic variables for feature data

化合物系列	生物标志物参数
萜烷系列	C₂₉βα/C₂₉αβ、C₃₀βα/C₃₀αβ、Ga/C₃₀αβ、Ts/C₃₀αβ、C₃₀/C₃₀αβ、C₃₀/C₂₉Ts、Ts/(Ts+Tm)、C₂₉αβ/C₃₀αβ、ΣC_19~26TT/C₃₀αβ、C₂₄TET/C₃₀αβ、C₂₃TT/C₃₀αβ、C₂₉Ts/(C₂₉Ts+C₂₉αβ)、C₃₁αβ22S/(22S+22R)、C₃₂αβ22S/(22S+22R)、C₃₃22S/(22S+22R)、C₃₄22S/(22S+22R)、Ga/C₃₁αβ、C₁₉TT/C₂₃TT、C₂₀TT/C₂₃TT、C₂₁TT/C₂₃TT、C₂₂TT/C₂₁TT、C₂₄TT/C₂₃TT、C₂₆TT/C₂₅TT、C₂₄TET/C₂₃TT、C₂₄TET/C₂₆TT、ETR、(C₁₉TT+C₂₀TT)/(C₂₃TT+C₂₄TT)、(C₃₁αβ+C₃₂αβ)/(C₃₃αβ+C₃₄αβ)和三环萜烷/(三环萜烷+藿烷)
甾烷系列	C₂₉ββ/(αα+ββ)、C₂₉20S/(20R+20S)、C₂₇/C_27~29、C₂₈/C_27~29、C₂₉/C_27~29、C₂₈/C₂₉、重排甾烷C₂₇20S/(20S+20R)、重排甾烷C₂₉20S/(20S+20R)、重排甾烷/甾烷、(孕甾烷+升孕甾烷)/甾烷、C₂₇αα20R/C₂₉αα20R和升孕甾烷/孕甾烷
其他	甾烷/藿烷

Fig.5 Deep MLP neural network used for oil source classification in this study

Fig.6 Box plot showing the test accuracy of the classification model

Table 2 Analysis results of sandstone and oil samples using the neural network model

层位 (油层组)	长7油源样品数/个	长8—长10油源样品数/个	长7烃源岩贡献率/%	长8—长10烃源岩贡献率/%
长7及以上层位	17	0	100	0
长8	34	12	74	26
长9	25	13	66	34
长10	7	4	64	36
总计	83	29	74	26

Fig.7 Planar distribution of two types of oil sources and source rocks of Chang 7 and Chang 9. Adapted from [2,52].

Fig.8 Random replacement and shuffling of data of a specific parameter (column)

Fig.9 Box plot of Permutation Feature Importance (PFI) for each biomarker parameter

Fig.10 Statistical comparison of biomarker parameters between Chang 7 mudstone/shale and Chang 8-Chang 10 mudstone/shale

Fig.11 3D scatter plots among the top three most important biomarker parameters from Fig.9

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