Petrophysical Analysis and Prediction of Porosity Function in One of the Southwest Reservoirs of Iran

Abbaszadeh Shahri, A; Hosseini, R; Rezaei, F; Mehdizadeh, K; Panaei, N

doi:10.22071/gsj.2015.42731

Petrophysical Analysis and Prediction of Porosity Function in One of the Southwest Reservoirs of Iran

Document Type : Original Research Paper

Authors

A Abbaszadeh shahri ¹

R Hosseini ²

F Rezaei ³

K Mehdizadeh ²

N Panaei ²

¹ Assistant Professor, Department of Civil Engineering, Islamic Azad University, Rudehen Branch, Tehran, Iran

² M. Sc., Department of Geology, Islamic Azad University, Damavand Branch, Damavand, Iran

³ Assistant Professor, Research Institiute for Earth Sciences, Geological Survey of Iran, Tehran, Iran

10.22071/gsj.2015.42731

Abstract

Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human brain has the ability to learn by recognized and determined examples. The target of the present paper is to establish and prove the Petrophysical Analysis as powerful approach in prediction and diagnosis of rock reservoir porosity by use of petrophysical logs, in which by a high accuracy suggested Petrophysical Analysis based solution the porosity can be estimated using conventional logging data. On the basis of the available petrophysical data, the proposed method was examined in one of the southwest oil field of Iran. The obtained results of network analysis conditioning to reliability to data with different tests such as regression, root mean square and SPLine showed that the amount of network error in terms of available data in engineering range with a high acceptable safety factor could be used to predict and estimate porosity. This method with ability of cost reduction and viability can help and provide a large variety in this field for further extended research.

Keywords

Petrophysical Analysis

porosity

Logging data

Neural Network

Sonic log

Gamma log