Geophysical quantitative prediction of TOC content in source rocks of Madingo Formation in Block A,Lower Congo Basin
Ji Shaocong1,2, Yang Xianghua1,2, Zhu Hongtao1,2, Deng Yunhua3, Kang Hongquan3, Wang Bo1,2
1. Faculty of Earth Resources, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 2. Key Laboratory of Tectonics and Petroleum Resources of the Ministry of Education, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 3. CNOOC Research Institute Co. Ltd., Beijing 100027, China
Abstract：TOC content is an important content of evaluation of organic matter abundance and hydrocarbon generation potential.There are few samples available for testing TOC content in source rocks of Madingo Formation in Block A, Lower Congo Basin due to oil-based mud pollution.And the distribution of these samples is nonuniform, so it is difficult to carry out quantitative evaluation.Geophysical data contain a variety of geochemical information in source rocks and can be used to quantitatively and effectively predict TOC content.Based on measured TOC content and logging data in study area, we find out some logging parameters with good correlations with TOC by cross-plot analysis.The predicted TOC content was calculated by multiple regression analysis, BP neural network and modified ΔlgR.And the best algorithm for single well TOC content prediction based on logging data is selected.A neural network model near the well is established based on the relation between the predicted TOC content of single well and seismic attributes on 3D seismic data, and this model is used to calculate a TOC data volume.The results show that well logging parameters with good correlations with measured TOC include density, natural gamma and interval transit time.BP neural network method has the best prediction and the correlation coefficient between predicted TOC content and measured TOC content of single well is up to 0.9542.
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