Petroleum reservoir characterization with the aid of artificial neural networks

被引：105

作者：

Mohaghegh, S ^{[1
]}

Arefi, R ^{[1
]}

Ameri, S ^{[1
]}

Aminiand, K ^{[1
]}

Nutter, R ^{[1
]}

机构：

[1] LCC INC,ARLINGTON,VA 22201

来源：

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING | 1996年 / 16卷 / 04期

关键词：

reservoir characterisation; neural networks; permeability prediction; saturation prediction; well log interpretation; computer application;

D O I：

10.1016/S0920-4105(96)00028-9

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous formations can be predicted with good accuracy using information deduced from readily available geophysical well logs. The methodology by which this is carried out is based on the intelligent and adaptive pattern recognition capabilities of an artificial neural network (three-layer feed forward, back propagation). The need for expensive processes to acquire porosity, permeability and fluid saturation data (such as well testing and extensive coring of the formation) may therefore be greatly reduced. Examples of several neural networks developed during this study are presented.

引用

页码：263 / 274

页数：12