Abstract

Seismic data processing lies at the heart of oil and gas exploration, transforming raw field recordings into high-fidelity images of the subsurface. This review synthesizes the state of the art in seismic processing techniques, covering preprocessing, imaging, inversion, and attribute analysis. We examine the challenges posed by complex geology, anisotropy, noise, and the ever-growing volume of data, highlighting strategies for noise attenuation, efficient computation, and uncertainty quantification. Applications in structural and stratigraphic interpretation, reservoir property estimation, and time-lapse monitoring are discussed to illustrate how advanced processing workflows enhance exploration success and production forecasting. Finally, we explore emerging trends—including machine learning and deep-learning algorithms, cloud-based real-time processing, and multi-physics integration—and consider their potential to revolutionize seismic workflows. By providing a comprehensive overview of methods, challenges, applications, and future directions, this review aims to guide researchers and industry professionals in adopting innovative processing strategies for more accurate resource assessment and sustainable development.