Segmental prediction method of total organic carbon content in mud shale

Abstract

The invention discloses a segmented prediction method for the total organic carbon content of mud shale, comprising: measuring the three-terminal mineral content and total organic carbon content of multiple rock samples at different depths in the target layer, and predicting the three-terminal content of the target layer; Meta-mineral content curve; put multiple rock samples into the three-terminal mineral content triangle diagram to determine the lithology of each rock sample, and divide the sample aggregation area according to the distribution of multiple input points to establish the three-terminal mineral content-total organic Carbon content grade-lithology identification template; put the three-terminal mineral content curve into the identification template to obtain the lithology distribution curve of the target layer, select the prediction model and corresponding parameters for different lithologies, and calculate the total organic carbon content of the target layer section for prediction. Effectively improve the accuracy of lacustrine TOC prediction.

Description

technical field [0001] The invention relates to the field of petroleum geology research, and more particularly, to a method for segmented prediction of total organic carbon content in shale. Background technique [0002] The evaluation of shale oil resources, exploration and formation selection and area selection evaluation are inseparable from the application of key geological parameters such as TOC (total organic carbon) content in organic-rich shale. TOC content is one of the main parameters for evaluating shale TOC and hydrocarbon generation capacity. Using logging data to evaluate the TOC content of source rocks can make up for the shortage of source rocks with less coring and discontinuous distribution of measured samples. By obtaining continuous and overall TOC content data, the purpose of quantitative prediction of organic carbon content in shale gas-bearing layers is achieved. [0003] Continental rifted lake basins are widely developed in eastern my country, and ...

AI Technical Summary

Problems solved by technology

The distribution of TOC in the entire section of the target layer can be obtained by the logging prediction method, but the traditional logging prediction model is established for a large set of stable sedimentary formations, and the prediction effect is poor for organic-rich shale layers with strong lacustrine heterogeneity , this is mainly due to the large differences in shale lithology and mineral composition caused by the frequently oscillating water environment. For different lithologies and different levels of TOC, only a unified prediction model and parameter prediction have large errors

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