Artificial intelligence and 3D subsurface interpretation for bright spot and channel detections

Yasir Bashir; Muhammad Afiq Aiman Bin Zahari; Abdullah Karaman; Doğa Doğan; Zeynep Döner; Ali Mohammadi; Syed Haroon Ali; Yasir Bashir; Muhammad Afiq Aiman Bin Zahari; Abdullah Karaman; Doğa Doğan; Zeynep Döner; Ali Mohammadi; Syed Haroon Ali

doi:10.3934/geosci.2024034

AIMS Geosciences

2024, Volume 10, Issue 4: 662-683. doi: 10.3934/geosci.2024034

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Artificial intelligence and 3D subsurface interpretation for bright spot and channel detections

1.
Department of Geophysical Engineering, Faculty of Mines, İstanbul Technical University, İstanbul, Türkiye
2.
Faculty of Science, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
3.
Department of Geological Engineering, Faculty of Mines, İstanbul Technical University, İstanbul, Türkiye
4.
Eurasia Institute of Earth Sciences, İstanbul Technical University, Istanbul, Türkiye
5.
Department of Earth Sciences, University of Sargodha, Sargodha, Punjab, Pakistan

Received: 31 May 2024 Revised: 29 July 2024 Accepted: 13 August 2024 Published: 04 September 2024

Seismic interpretation is primarily concerned with accurately characterizing underground geological structures & lithology and identifying hydrocarbon-containing rocks. The carbonates in the Netherlands have attracted considerable interest lately because of their potential as a petroleum or geothermal system. This is mainly because of the discovery of outstanding reservoir characteristics in the region. We employed global 3D seismic data and a novel Relative Geological Time (RGT) model using artificial intelligence (AI) to delve deeper into the analysis of the basin and petroleum resource reservoir. Several surface horizons were interpreted, each with a minimum spatial and temporal patch size, to obtain a comprehensive understanding of the subsurface. The horizons were combined with seismic attributes such as Root mean square (RMS) amplitude, spectral decomposition, and RGB Blending, enhancing the identification of the geological features in the field. The hydrocarbon potential of these sediments was mainly affected by the presence of a karst-related reservoir and migration pathways originating from a source rock of satisfactory quality. Our results demonstrated the importance of investigations on hydrocarbon potential and the development of 3D models. These findings enhance our understanding of the subsurface and oil systems in the area.
- artificial intelligence (AI),
- seismic interpretation,
- attributes,
- prospect,
- hydrocarbon
Citation: Yasir Bashir, Muhammad Afiq Aiman Bin Zahari, Abdullah Karaman, Doğa Doğan, Zeynep Döner, Ali Mohammadi, Syed Haroon Ali. Artificial intelligence and 3D subsurface interpretation for bright spot and channel detections[J]. AIMS Geosciences, 2024, 10(4): 662-683. doi: 10.3934/geosci.2024034

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Abstract

Seismic interpretation is primarily concerned with accurately characterizing underground geological structures & lithology and identifying hydrocarbon-containing rocks. The carbonates in the Netherlands have attracted considerable interest lately because of their potential as a petroleum or geothermal system. This is mainly because of the discovery of outstanding reservoir characteristics in the region. We employed global 3D seismic data and a novel Relative Geological Time (RGT) model using artificial intelligence (AI) to delve deeper into the analysis of the basin and petroleum resource reservoir. Several surface horizons were interpreted, each with a minimum spatial and temporal patch size, to obtain a comprehensive understanding of the subsurface. The horizons were combined with seismic attributes such as Root mean square (RMS) amplitude, spectral decomposition, and RGB Blending, enhancing the identification of the geological features in the field. The hydrocarbon potential of these sediments was mainly affected by the presence of a karst-related reservoir and migration pathways originating from a source rock of satisfactory quality. Our results demonstrated the importance of investigations on hydrocarbon potential and the development of 3D models. These findings enhance our understanding of the subsurface and oil systems in the area.

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