Advancing Fractured Reservoir Characterization: A Reservoir-Driven Seismic Reprocessing Approach

Naturally fractured reservoirs (NFRs) are among the most complex subsurface systems to explore, characterize, and produce efficiently. They represent a significant portion of global hydrocarbon reserves and are often associated with unconventional reservoirs, carbonates, tight sands, and basement plays. Their production potential is primarily governed by the distribution, orientation, and connectivity of fracture networks rather than solely by matrix porosity. Therefore, accurately identifying and characterizing fractures is critical for optimizing hydrocarbon recovery. Traditional seismic processing workflows, although effective in many conventional settings, are often inadequate when applied to naturally fractured reservoirs. The complexity arises from multiple factors including low signal-to-noise ratio (SNR), inconsistent amplitude behavior, strong heterogeneity in rock properties, and the presence of subtle anisotropic effects that conventional processing techniques fail to address. As a result, fracture networks may remain undetected, misinterpreted, or improperly modeled, leading to suboptimal field development strategies, poor well placement, and an increased risk of bypassed hydrocarbons. To overcome these constrains, We proposes a reservoir-driven seismic reprocessing technique. Unlike traditional approaches that focus primarily on generic seismic data enhancement, this method integrates reservoir-specific knowledge—such as borehole data, petrophysical logs, and seismic attributes—into the seismic reprocessing workflow. The central idea is to guide seismic imaging and attribute extraction using reservoir insights, thereby enhancing the interpretability and relevance of the seismic volumes for fracture characterization.