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Research Papers: Petroleum Engineering

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

[+] Author and Article Information
Tamer Moussa

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia
e-mail: g201105270@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia;
Petroleum Department,
Cairo University,
Cairo 12613, Egypt
e-mail: elkatatny@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia
e-mail: mmahmoud@kfupm.edu.sa

Abdulazeez Abdulraheem

Department of Petroleum Engineering,
King Fahd University of Petroleum and
Minerals,
Dhahran 5049, Saudi Arabia
e-mail: toazeez@gmail.com

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 28, 2017; final manuscript received January 3, 2018; published online March 15, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 140(7), 072903 (Mar 15, 2018) (8 pages) Paper No: JERT-17-1464; doi: 10.1115/1.4039270 History: Received August 28, 2017; Revised January 03, 2018

Permeability is a key parameter related to any hydrocarbon 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 (ANN). 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) techniques 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 (MSE) was 0.0638 and the correlation coefficient (CC) 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 (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

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Figures

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Fig. 1

Relative importance of different well log data with measured permeability

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Fig. 2

Mud invasion relationship with short/deep resistivity from Schlumberger crossplot Rint-13b

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Fig. 3

Regression plot of estimated permeability from SaDE-ANN model and core permeability data

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Fig. 4

Training and validation errors with iterations

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Fig. 5

Regression plot of estimated permeability from eureqa software and core permeability data

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Fig. 6

Permeability profile of well # 01 based on testing data obtained from SaDE-ANN and eureqa models

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Fig. 7

Regression plot of estimated core permeability data of well # 02 obtained from SaDE-ANN model and eureqa

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Fig. 8

Permeability profile of well # 02 based on validation data obtained from SaDE-ANN and eureqa models

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