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Abstract

The integration of Artificial Intelligence (AI) into seismic data processing has ushered in transformative innovations in the oil and gas sector, enhancing exploration accuracy, reducing interpretation time, and optimizing reservoir characterization. This review explores the current landscape of AI applications in seismic workflows, including automated fault detection, lithofacies classification, and real-time seismic imaging. Advanced machine learning algorithms, such as deep neural networks, convolutional neural networks (CNNs), and reinforcement learning, are being leveraged to interpret large and complex datasets with improved precision. Despite these advancements, several challenges persist, including data quality issues, interpretability of AI models, and integration with legacy geophysical systems. The paper critically examines these limitations and discusses emerging solutions, including explainable AI, hybrid learning models, and transfer learning approaches. Furthermore, the review outlines future directions such as the convergence of AI with cloud computing, edge analytics, and quantum machine learning for seismic interpretation. By offering a comprehensive overview, this study aims to provide researchers, geophysicists, and industry professionals with insights into the evolving role of AI in seismic data processing and its potential to reshape subsurface exploration in the energy sector.
Keywords: Artificial Intelligence, Seismic Data Processing, Oil and Gas Exploration, Machine Learning, Reservoir Characterization