Shale gas reservoir logging porosity rapid prediction method and system

Abstract

The invention provides a shale gas reservoir logging porosity rapid prediction method comprising the following steps: obtaining shale gas reservoir logging data, predicting according to a predetermined proportion of logging curve data in the shale gas reservoir logging data to obtain a pore value corresponding to shale, and generating a pore prediction threshold according to the pore value; establishing a random forest porosity prediction model through a random forest regression algorithm according to the data of the remaining proportion of the shale gas reservoir logging data; substituting the logging curve data into the random forest porosity prediction model to obtain a pore prediction value; comparing the pore prediction value with the pore prediction threshold, and generating a porosity prediction model according to a comparison result and the random forest porosity prediction model; and obtaining a porosity prediction result according to the porosity prediction model and the logging data of the shale gas reservoir to be measured.

Description

technical field [0001] The invention relates to the field of geological exploration, in particular to a method and system for rapidly predicting the logging porosity of shale gas reservoirs. Background technique [0002] Shale porosity is an important basic parameter for shale gas exploration and development. Affected by rich organic matter, high content of clay minerals, and compact lithology, the pore structure of shale is more complicated than that of sandstone, and the porosity is generally about 3% to 6%. Conventional logging porosity prediction methods cannot directly Applied to shale porosity prediction. [0003] In the prior art, quantitative evaluation methods for porosity of oil and gas reservoirs are established based on the Wyllie formula or multivariate fitting of logging curves. In conventional sandstone reservoirs, acoustic logging, density logging, and neutron porosity logging mainly reflect changes in formation porosity. At this time, reservoir porosity ca...

AI Technical Summary

Problems solved by technology

The random forest algorithm has the advantages of simple data processing, suitable for large sample data sets, and no need to adjust too many parameters, but it is rarely used in the field of oil and gas reservoir evaluation

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