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research-article

Improved Permeability Correlations from Well Log Data using Artificial Intelligence Approaches

[+] Author and Article Information
Tamer Moussa

PhD Student, Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
g201105270@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Cairo University, Cairo, Egypt
elkatatny@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
mmahmoud@kfupm.edu.sa

Abdulazeez Abdulraheem

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
toazeez@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4039270 History: Received August 28, 2017; Revised January 03, 2018

Abstract

Permeability is a key parameter for reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network. The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A Self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) technique were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error was 0.0638 and the correlation coefficient was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data.

Copyright (c) 2018 by ASME
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