AI-driven and advanced sensing technologies for subsurface oil exploration
Abstract
Subsurface oil exploration is becoming increasingly challenging due to the complexity of geological formations, growing exploration depths, and the limitations of conventional interpretation methods. A thorough analysis of current developments in artificial intelligence and sophisticated sensing technologies for subsurface oil prospecting is presented. With special emphasis on their uses, advantages, and implementation difficulties, this paper assesses advancements in seismic imaging, electromagnetic surveys, remote sensing, machine learning, and integrated data interpretation frameworks. The results show that while advanced sensing technologies offer higher-resolution subsurface information and lower exploration uncertainty, artificial intelligence greatly increases exploration efficiency by enhancing data processing, pattern recognition, reservoir characterization, and decision-making. Nonetheless, there are lots of issues with data quality, model dependability, scalability, and field deployment. The review offers a thorough reference for researchers and industry practitioners looking to increase exploration accuracy, efficiency, and sustainability. It also emphasizes future research possibilities for intelligent, integrated exploration systems.
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