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Updated: Aug 5, 2026
Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
Published on: October 21, 2018
AI-driven and advanced sensing technologies for subsurface oil exploration
Alhamrah Mohannad Firas Ali1, Alzharawi Shamil Kamil Khanjar1, Aleksandr Aleksandrovich Azeev1
1Department of Subsoil Use and Oil and Gas Engineering, Siberian Federal University, Krasnoyarsk, Krasnoyarsk Krai, Russia.
Advanced sensing technologies and artificial intelligence (AI) improve subsurface oil exploration by providing higher resolution data and increasing efficiency. Challenges remain in data quality, model dependability, and field deployment for sustainable exploration.
Area of Science:
- Geophysics
- Artificial Intelligence
- Petroleum Geoscience
Background:
- Subsurface oil exploration faces challenges from complex geology, deeper targets, and traditional interpretation limits.
- Conventional methods struggle with increasing exploration complexity and depth.
Purpose of the Study:
- To analyze current artificial intelligence and sensing technologies for subsurface oil prospecting.
- To assess the uses, advantages, and implementation difficulties of these advanced methods.
Main Methods:
- Review of advancements in seismic imaging, electromagnetic surveys, and remote sensing.
- Analysis of machine learning applications and integrated data interpretation frameworks.
Main Results:
- Advanced sensing provides higher resolution subsurface data, reducing exploration uncertainty.
- Artificial intelligence significantly boosts exploration efficiency in data processing, pattern recognition, and decision-making.
Conclusions:
- Integrated AI and sensing technologies enhance exploration accuracy, efficiency, and sustainability.
- Future research should focus on intelligent, integrated exploration systems addressing current deployment challenges.
