Reservoir classification method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a reservoir classification method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a target imaging logging image and target conventional logging curve data corresponding to a target depth region in a target drilling well; inputting the target imaging logging image into a first convolutional neural network classification model, and outputting a first reservoir classification result of a target reservoir corresponding to the target depth region; inputting the target conventional logging curve data into a second convolutional neural network classification model, and outputting a second reservoir classification result of the target reservoir; inputting a splicing result obtained by splicing the first reservoir classification result and the second reservoir classification result into a target full-connection layer to obtain a target reservoir classification result of the target reservoir; and determining the category of the target reservoir according to the target reservoir classification result. According to the scheme, the reservoir is automatically classified through the convolutional neural network classification model, so that the working efficiency of reservoir classification and the classification accuracy are improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a method, apparatus, electronic device, and computer-readable storage medium for classification of reservoirs. Background technique [0002] Reservoirs are rock formations that store oil and gas, and contain interconnected pores. Classification of reservoirs can objectively and generally express the oil and gas storage capacity of the reservoirs, which is of great significance for accurate and quantitative evaluation of oil and gas reserves, and plays an important guiding role in oil and gas exploration and development. [0003] In the prior art, when classifying a reservoir, the reservoir is usually classified by manually analyzing parameters such as porosity, permeability, and lithology of the reservoir. However, artificial-based reservoir classification methods mainly rely on expert experience and are subject to subjective factors, resulting in low classifica...

AI Technical Summary

Problems solved by technology

The manual-based reservoir classification method mainly relies on expert experience and will be affected by subjective factors. The accuracy of classification is low, and the work efficiency is also low in the face of a large amount of logging data.

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