Method for identifying lithology of whole well section

Abstract

The invention relates to a method for identifying the lithology of a whole well section. The method comprises the steps of taking out multiple rock core samples from multiple deep positions, and determining the granularity data and the lithology type of each rock core sample; determining the values of multiple types of well logging parameters of the multiple deep positions, and selecting N types of well logging parameters which are increased or decreased with increase in the granularity of the rock core samples, wherein N is more than or equal to 2; according to the lithology types of rock core sample points in the multiple deep positions and the values of N types of characteristic parameters, determining a corresponding relationship between each N-dimensional cell and the lithology type in the N-dimensional space of characteristic parameter coordinates; according to the values of the characteristic parameters and the corresponding relationship of each deep position within the whole well section, determining the representative lithology type of each deep position. According to the method, the advantages of a traditional geological analysis method and a method for identifying the lithology types through logging process are incorporated; the established interactive multi-dimension histogram lithology identification is correct and reliable, particularly applicable for use in the case of fewer rock core data.

Description

technical field [0001] The invention relates to a method for identifying lithology, in particular to a method for quantitatively identifying lithology types in the whole well section by using an interactive multi-dimensional histogram. Background technique [0002] At present, the methods of using logging information to identify lithology mainly include conventional logging data interpretation method, discriminant analysis method, neural network algorithm and so on. The purpose of the conventional logging data interpretation method is mainly to determine the shale content of the formation. The interpretation results are only two lithologies of sandstone and mudstone. The interpretation results are too coarse to reflect the geological heterogeneity; It is common, but the representativeness of the original sample data and the comprehensiveness of the indicators affecting the lithology category are relatively high, and the pertinence is poor. Due to the influence of fluid prope...

AI Technical Summary

Problems solved by technology

The purpose of the conventional logging data interpretation method is mainly to determine the shale content of the formation. The interpretation results are only two lithologies of sandstone and mudstone. The interpretation results are too coarse to reflect the geological heterogeneity; It is common, but the representativeness of the original sample data and the comprehensiveness of the indicators affecting the lithology category are relatively high, and the pertinence is poor. Due to the influence of fluid properties and other factors, the reliability is also poor, and it cannot meet the requirements of reservoir physical properties. The needs of fine evaluation and sedimentological research; neural network algorithm, the generalization ability of this method is not strong, the accuracy of identification results is poor, and artificial prior knowledge is required, and it is difficult to use conventional methods when there are few core data. Lithology identification

The above three methods are not accurate enough for lithology identification, and none of them can meet the needs of fine logging evaluation and geological research.

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