Reservoir classification evaluation method based on secondary parameter selection

Abstract

The invention discloses a reservoir classification evaluation method based on secondary parameter selection. The reservoir classification evaluation method comprises the steps of introduction and classification of reservoir evaluation parameters, correlation analysis of the reservoir evaluation parameters, primary selection of the reservoir evaluation parameters, secondary selection of the reservoir evaluation parameters and determination of reservoir classification evaluation standards through K-means clustering. Beneficial effects of the present invention are: reservoir evaluation parametersare determined through secondary optimization; reservoir evaluation is completed through K-means clustering; compared with the prior art, the reservoir classification evaluation method has the advantages that parameter selection is more accurate and reliable, the reservoir classification result is more credible, reservoir evaluation parameters can be quantitatively optimized through secondary selection of the reservoir parameters, the standard is accurate and unified, and the reservoir evaluation method combines macroscopic and microscopic parameters, so that the finally obtained evaluation standard is more comprehensive and accurate.

Description

technical field [0001] The invention relates to a method for classifying and evaluating reservoirs, in particular to a method for classifying and evaluating reservoirs based on secondary selection of parameters, and belongs to the technical field of classifying and evaluating reservoirs. Background technique [0002] Reservoir classification evaluation is an important basic work in oil and gas exploration and development. With the advancement of various analytical and testing technologies, many parameters that characterize reservoirs have been brought. How to effectively and accurately select parameters as the final evaluation parameters and complete the reservoir Layer evaluation has always been a concern of scholars. At this stage, there are many methods for classification and evaluation based on the selected parameters, but when selecting parameters, most of them are based on experience, cannot be accurately quantified, and the standards are not uniform. [0003] At pres...

AI Technical Summary

Problems solved by technology

In the traditional reservoir evaluation, most of the parameters are selected based on expert experience, and the evaluation parameters are directly determined, which will make the selection of evaluation parameters inconsistent

[0005] (2) When determining the specific standard ranges of various reservoir evaluation parameters, there is also a lack of quantitative methods, so different scholars use the same data and parameters to obtain different evaluation standards

[0006] (3) Due to relatively few analysis and testing methods in the past, the designated reservoir evaluation criteria usually only have macroscopic reservoir parameters, and the evaluation parameters are not comprehensive

Nowadays, with the improvement of analysis and testing methods, various types of microscopic reservoir parameters can be obtained, but at present, there is no combination with macroscopic reservoir parameters to obtain a comprehensive reservoir evaluation standard, and no corresponding method has been established

Images