Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Improved Seismic Fault Interpretation in a Structurally Complex Geologic Setting Using a Pretrained CNN Model and Seismic Attributes: An Example from the Browse Basin, Australia

Md Mahmodul Islam
* ORCID logo ,
Mohamed Elsaadany
,
Ismailalwali Babikir
,
Sami Elkurdy
,
Numair Ahmed Siddiqui
Version 1 : Received: 13 July 2023 / Approved: 13 July 2023 / Online: 14 July 2023 (12:41:00 CEST)

A peer-reviewed article of this Preprint also exists.

Islam, M.M.; Babikir, I.; Elsaadany, M.; Elkurdy, S.; Siddiqui, N.A.; Akinyemi, O.D. Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia. Appl. Sci. 2023, 13, 11300. Islam, M.M.; Babikir, I.; Elsaadany, M.; Elkurdy, S.; Siddiqui, N.A.; Akinyemi, O.D. Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia. Appl. Sci. 2023, 13, 11300.

Abstract

Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.

Keywords

Fault detection; reservoir characterization; seismic images; deep learning; convolutional neural network; variance attribute

Subject

Environmental and Earth Sciences, Geophysics and Geology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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