Open Access

ARTICLE

Automatic Channel Detection Using DNN on 2D Seismic Data

by Fahd A. Alhaidari¹, Saleh A. Al-Dossary², Ilyas A. Salih^1,*, Abdlrhman M. Salem¹, Ahmed S. Bokir¹, Mahmoud O. Fares¹, Mohammed I. Ahmed¹, Mohammed S. Ahmed¹

1 Imam Abdulrahman Bin Faisal University, College of Computer Science and Information Technology, Dammam, 34212, Saudi Arabia
2 Saudi Aramco Oil Company, EXPEC-II, Dhahran, Saudi Arabia

* Corresponding Author: Ilyas A. Salih. Email:

Computer Systems Science and Engineering 2021, 36(1), 57-67. https://doi.org/10.32604/csse.2021.013843

Received 30 August 2020; Accepted 29 October 2020; Issue published 23 December 2020

Abstract

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.

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Keywords

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