Particle size logging inversion method of deep complex clastic rock and its application in fine lithology identification
REN Yufei1,2, YAN Jianping1,2,3, WANG Min4, SONG Dongjiang5, GENG Bin4
1 School of Geoscience and Technology,Southwest Petroleum University,Chengdu 610500,China; 2 Natural Gas Geology Key Laboratory of Sichuan Province-Southwest Petroleum University,Chengdu 610500,China; 3 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation-Southwest Petroleum University,Chengdu 610500,China; 4 Institute of Exploration and Development,Shengli Oil Field,SINOPEC,Shandong Dongying 257015,China; 5 Shandong Ruilin Energy Technology Co.,Ltd,Shandong Dongying 257000,China
Abstract The Miocene strata in the L area of the Y Basin in the western part of the South China Sea are characterised by high temperature and ultra-high pressure,which makes drilling difficult and core data rare. In addition, the accuracy of rock chip logging in reflecting lithology is relatively low,making it difficult to meet the requirements of fine identification of lithology. The deep clastic rocks in the second section of Huangliu Formation in the L area of Y basin are used in this study,firstly,using the limited data of core size analysis,rock chip logging and logging,we selected the particle size parameter characterizing lithology: median Md and five logging curves of natural gamma,density,neutron,acoustic time difference and resistivity which are sensitive to changes in the particle size,and constructed the data set of five variables of the median Md and logging,and then we used K-MEANS secondly,using K-MEANS clustering method,the dataset was divided into four classes according to the optimal relationship between “sum of error squares and the number of clusters”(referred to as “granularity classification”),which optimised the correlation between the median Md-granularity and the logging response,and obtained the logging response characteristics of the different classes and the corresponding lithological types. Then,in the actual well data processing process,Fisher's discriminant equation is applied to determine the type of particle size classification to which the unknown depth point belongs,and finally,the intelligent calculation model of median particle size logging based on XGBoost algorithm is established under the particle size classification,and based on the numerical range of median particle size corresponding to different lithologies,it realises the purpose of fine identification of lithology by inverting the median Md curve according to the logging on the wellbore profile. The purpose of fine identification of lithology is achieved by inverting the Md curve on the wellbore profile according to the logging.The results show that the sandstone lithology in the second section of Huangliu Formation in L area is divided into: siltstone,fine sandstone,medium sandstone and coarse sandstone considering the difference of grain size,among which the fine sandstone and medium sandstone are the most dominantly developed lithologies,and the median Md of grain size has the closest relationship with the lithology of different grain sizes,and it is the most reflective of the different grain sizes of lithologies;the intelligent calculation of the median Md of grain size logging model based on XGBoost algorithm is better than that of multiple regression algorithm after the classification of the grain sizes. The prediction effect of the model is better than that of the multiple regression prediction model,and the correlation coefficient between the calculated median particle size and the measured value reaching 0.9397,the average absolute error(MAE) is 0.0195,and the average relative error MRE is 0.1752. The model is an effective method for the fine identification of the lithology of the deep complex clastic rocks,and it also lays a foundation for sedimentary grain sequence analysis and fine interpretation of the reservoir configuration,and the evaluation of the validity on the vertical profile. It also lays the foundation for sedimentary grain sequence analysis,fine interpretation of reservoir configuration and validity evaluation in longitudinal section.
Fund:National Science and Technology Major Project(No. 2017ZX05072-002)and National Natural Science Foundation of China(No.42372177)
Corresponding Authors:
YAN Jianping,born in 1980,Ph.D., is a professor and doctoral supervisor. He is mainly engaged in teaching and research in logging geology and unconventional oil and gas logging evaluation. E-mail: yanjp_tj@163.com.
About author: REN Yufei,born in 2001, is a masteral candidate at Southwest Petroleum University,with research interests in logging geology,rock physics,and digital signal processing and analysis. E-mail: renyufei03@163.com.
Cite this article:
REN Yufei,YAN Jianping,WANG Min et al. Particle size logging inversion method of deep complex clastic rock and its application in fine lithology identification[J]. JOPC, 2025, 27(1): 240-255.
REN Yufei,YAN Jianping,WANG Min et al. Particle size logging inversion method of deep complex clastic rock and its application in fine lithology identification[J]. JOPC, 2025, 27(1): 240-255.
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