Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (4): 91-100.doi: 10.12108/yxyqc.20260408

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

Quantitative characterization method for clastic rock components based on improved electrical imaging logging mineral probability spectrum

REN Yufei1,2(), YAN Jianping2,3,4(), YAN Ke1, HUANG Lisha5, WANG Min6, GENG Bin6   

  1. 1 Exploration Division, PetroChina Southwest Oil and Gas Field Company, Chengdu 610041, China
    2 School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    3 Natural Gas Geology Key Laboratory of Sichuan Province, Southwest Petroleum University, Chengdu 610500, China
    4 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
    5 Research Institute of Exploration and Development, PetroChina Tarim Oilfield Company, Korla 841000, Xinjiang, China
    6 Research Institute of Exploration and Development, Sinopec Shengli Oilfield Company, Dongying 257015, Shandong, China
  • Received:2025-12-27 Revised:2026-02-04 Online:2026-07-01 Published:2026-07-06
  • Contact: YAN Jianping E-mail:renyufei03@163.com;yanjp_tj@163.com

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Abstract:

To address the strong heterogeneity of mineral components in deep complex clastic reservoirs and the limited accuracy of conventional logging methods, a quantitative calculation method based on improved electrical imaging logging mineral probability spectra was proposed, achieving continuous and precise determination of key mineral contents such as feldspar, calcite, and clay. The research findings indicate: （1） The resistivity data of electrical imaging was converted into standardized pixel values, and pixel waveform spectra were constructed through histogram equalization and normal distribution processing. Subsequently, based on Archie’s formula and the parallel conductivity model, porosity contribution corrections were applied to the pixel values to extract imaging mineral spectra reflecting pure mineral components. （2） The imaging mineral spectra were calibrated using whole-rock X-ray diffraction (XRD) analysis data from core samples. The optimal segmentation algorithm was employed to determine the optimal pixel value thresholds for distinguishing mud, feldspar, and calcite (calcite/felsic mineral boundary at 15%; felsic mineral/clay boundary at 70%), thereby establishing a quantitative calculation model from imaging mineral spectra to mineral content. （3） In practical single-well applications, the electrical imaging mineral calculation model significantly improved mineral identification accuracy, with correlation coefficient R2 between calculated mineral content and XRD measured data greater than 0.800 0, which can identify low physical property intervals caused by calcareous cementation, and is conducive to the detailed evaluation of complex clastic reservoirs.

Key words: clastic rock, electrical imaging logging, mineral component, lithology identification, conductivity, reservoir detailed evaluation, Huangliu Formation, Yinggehai Basin

CLC Number: 

  • TE122

Fig. 1

Correlation between carbonate cements and reservoir physical properties of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 2

Probability density extraction principles for electrical imaging pixels of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 3

Pixel waveform spectrum of electrical imaging logging of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 4

Pixel waveform diagram after equalization processing of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 5

Schematic diagram of electrical imaging logging-derived porosity spectrum calculation of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 6

Flowchart for solving mineral probability maps in electrical imaging logging images of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 7

Flowchart for mineral cutoff value determination based on optimal segmentation algorithm"

Table 1

Accuracy comparison of different cutoff value combinations for imaging mineral spectra of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

灰质
截止值/%		 长英质
截止值/%		 灰质MAE/% 长英质MAE/% 泥质MAE/% 综合MAE/%
14 69 4.19 4.37 3.84 4.13
14 70 4.19 4.55 3.59 4.11
14 71 4.19 4.81 3.56 4.19
15 69 2.42 2.77 3.84 3.01
15 70 2.42 2.68 3.59 2.90
15 71 2.42 2.78 3.56 2.92
16 69 3.86 4.82 3.84 4.17
16 70 3.86 4.32 3.59 3.92
16 71 3.86 4.07 3.56 3.83

Fig. 8

Determination of imaging mineral spectrum cutoff value using the optimal segmentation algorithm of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 9

Schematic diagram of workflow for mineral composition calculation based on electrical imaging logging"

Fig. 10

Correlation between calculated mineral composition from electrical imaging logging and measured mineral composition of Neogene Huangliu Formation in Ledong area, Yinggehai Basin"

Fig. 11

Mineral composition calculation results of electrical imaging logging images of well X-2 in Neogene Huangliu Formation, Ledong area, Yinggehai Basin"

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