STUDY OF GEOSTATISTICAL INVERSION IN THE LITHOLOGIC DISTRIBUTION AND VELOCITY MODELING OF THICK IGNEOUS ROCK IN THE FY AREA, NORTHERN TARIM BASIN, CHINA
Y. Xu, H. Yang, G. Peng, X. Deng, Q. Miao, Y. Ma, J. Liu
1School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China
2Institute of China Petroleum Tarim Oilfield Company, Korla, China
3School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China
Keywords: thick igneous rocks, geostatistical inversion, lithology distribution, velocity modeling
Abstract
In the northern Tarim Basin, a large number of thick igneous rocks are encountered in the drilling process in the Permian. Their lithology and velocity are very strongly, which has a great influence on migration imaging of the “beaded” areas. It is very important to conduct the fine lithology identification and high-precision velocity modeling of the igneous rocks for the exploration and development of the reservoirs. A geostatistical inversion method to obtain the igneous-rock lithologic distribution pattern and velocity modeling in the FY area of the northern Tarim Basin is introduced in this paper. The results show that the application of the geostatistical inversion method greatly improves the resolution of lithology identification. This helps us further understand the Permian igneous rocks distribution in the FY area. Comparison between the seismic facies classification maps of the FY study area shows that the obtained velocity model can reflect the lateral distribution of igneous rocks well. At the same time, the velocity model can reflect the variation of igneous rocks velocity in detail and has a high precision. The average velocity error of the wells participating in the inversion is less than 2%, and the minimum average velocity error is 0.23%. Finally, the velocity model is applied to seismic data processing, and the processing results indicate that it can help to improve seismic migration imaging. The study demonstrates that the geostatistical inversion method can provide a high-precision velocity model for formation pressure prediction and seismic data processing and interpretation, ultimately guiding the exploration and development of oil.
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