Determining electrofacies of Gadwan Formation using MRGC clustering method by integrating petrophysical logs and FMI image log

jahan mohammadi, Hossein; mosadegh, Hossein; Azizzadeh, Mehran; sarkheil, Hamid; Mohammadnia, Mohammad

Volume 8, Issue 1 (Spring & Summer 2022) KJES 2022, 8(1): 97-117 | Back to browse issues page

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jahan mohammadi H, mosadegh H, Azizzadeh M, sarkheil H, Mohammadnia M. Determining electrofacies of Gadwan Formation using MRGC clustering method by integrating petrophysical logs and FMI image log. KJES 2022; 8 (1) :97-117
URL: http://gnf.khu.ac.ir/article-1-2817-en.html

Determining electrofacies of Gadwan Formation using MRGC clustering method by integrating petrophysical logs and FMI image log

Hossein Jahan mohammadi

, Hossein Mosadegh ^*

¹, Mehran Azizzadeh

, Hamid Sarkheil

, Mohammad Mohammadnia

1- , hosadegh@gmail.com

Abstract: (1262 Views)

The continuous development of the oil industry has led to a significant increase in the number of wells that are simultaneously analyzed. For this purpose, it is important to develop methods that improve the integration of all well information at different scales, while reducing the time required for studies. This article examines the integration of image log (FMI) with other conventional logs (gamma, density, neutron and sonic) to determine electrofacies in the Gadwan Formation in one of the oilfields in Abadan plain, SW Iran. Due to the high resolution, the image logs can provide important information regarding sedimentology, texture, and porosity distribution. This information is very valuable in situations where it is not possible to core from the Formation. To accurately estimate, the necessary environmental corrections were applied to conventional logs. In the next step, electro-facies were created by integrating conventional logs and image log using the graphical clustering method (MRGC) in Geolog software and with the FACIMAGE™ module and finally, the model with 8 facies was selected as the optimal model. Among the determined facies, the electrofacies number 8 was recognized as the best reservoir facies (due to high effective porosity and low shale volume). Comparing the results of lithology, shale volume, porosity and water saturation with the facies determined by the cluster analysis method showed that the use of image logs has greatly improved the separation of electrofacies.

Keywords: Electrofacies, Graphical clustering (MRGC), Abadan plain, Gadwan formation, Image log

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Type of Study: Original Research | Subject: Petroleum Geology
Received: 2022/05/13 | Accepted: 2022/08/14 | Published: 2022/09/13