改进人工神经网络原理对储层渗透率的预测——以北部湾盆地涠西南凹陷为例

doi:10.11743/ogg20070115

Abstract

Abstract:

Computational method of artificial neural network is a non-linear processing system,which predicts reservoir physical property with logging data.In previous calculation of permeability with genetic algorithm,only single data points were used and no data of the neighboring horizons were involved in the training,thus the accuracy and reliability of the prediction model were limited.In order to solve this problem,the data points of several neighboring horizons are used for training and a prediction model of reservoir permeability is built.Based on the normalization of core test data and relevant logging data,reservoir permeability is calculated point-by-point with the improved windowing technique and feedback neural network method.Its application to petroleum exploration in Southwest Weizhou depression of Beibuwan basin shows that the predicted and measured permeability coincides well.

Key words: windowing technique, permeability prediction, artificial neural network, Southwest Weizhou Depression, Beibuwan Basin

CLC Number:

TE112.2

Shan Jingfu, Ji Youliang, Liu Chengzhi. Prediction of reservoir permeability with improved artificial neural network principle:taking the Southwest Weizhou Depression in Beibuwan Basin as an example[J]. Oil & Gas Geology, 2007, 28(1): 106-109,115.

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Prediction of reservoir permeability with improved artificial neural network principle:taking the Southwest Weizhou Depression in Beibuwan Basin as an example

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