Lithologic Reservoirs ›› 2020, Vol. 32 ›› Issue (1): 86-93.doi: 10.12108/yxyqc.20200109

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TOC prediction technology based on optimal estimation and Bayesian statistics

ZHAO Wanjin1, GAO Haiyan2, YAN Guoliang1, GUO Tongcui3   

  1. 1. PetroChina Research Institute of Petroleum Exploration&Development-Northwest, Lanzhou 730020, China;
    2. Lanzhou University of Finance and Economics, Lanzhou 730020, China;
    3. PetroChina Research Institute of Petroleum Exploration&Development, Beijing 100083, China
  • Received:2019-05-08 Revised:2019-08-03 Online:2020-01-21 Published:2019-11-22

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Abstract: The total organic carbon(TOC)content in tight rock formations often directly indicates the location of oil and gas reservoirs,but dense rock formations often have complex lithology configurations,so it is difficult to identify effective source rocks by using conventional geophysical techniques. This paper proposed a TOC wellseismic joint prediction technology based on optimal estimation and Bayesian statistical classification. The TOC estimated by the conventional method was taken as the initial value,and the initial value of TOC was corrected by using the constructed rock density calculation model and optimization theory. The TOC log curve with the best matching with the laboratory sample was obtained. Based on the analysis of TOC sensitive parameters,the inversion TOC sensitive parameters were converted into TOC probability volume spatial distribution by Bayesian statistical classification method. This technology was applied to the prediction of lacustrine dense marl reservoirs,providing a reliable basis for the deployment of high-yield oil wells ST3. The practical application results verified the effectiveness of the technology,and the technology can be applied to the prediction of effective source rocks with similar geological features.

Key words: tight lithology, total organic carbon, rock physics model, optimization, Bayesian, well-seismic joint

CLC Number: 

  • P631.4
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