A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field

被引：0
|
作者：
Sagar Singh
Ali Ismet Kanli
Selcuk Sevgen
机构：
[1] Indian Institute of Technology Roorkee,Department of Earth Sciences
[2] Istanbul University,Department of Geophysical Engineering, Faculty of Engineering
[3] Avcilar Campus,Department of Computer Engineering, Faculty of Engineering
[4] Istanbul University,undefined
[5] Avcilar Campus,undefined
来源：
Studia Geophysica et Geodaetica | 2016年 / 60卷
关键词：
porosity estimation; artificial neural network; well log data; Kansas gas field;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study aims to design a back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field in the USA. In order to estimate the porosity, a neural network approach is applied, which uses as input sonic, density and resistivity log data, which are known to affect the porosity. This network easily sets up a relationship between the input data and the output parameters without having prior knowledge of petrophysical properties, such as porefluid type or matrix material type. The results obtained from the empirical relationship are compared with those from the neural network and a good correlation is observed. Thus, the ANN technique could be used to predict the porosity from other well log data.
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收藏
页码：130 / 140
页数：10
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