Unlocking Unconventional Targets Using Advanced Fault Connectivity Algorithm

Abstract Reservoir compartmentalization poses challenges in unconventional targets between closely spaced wells with different fluid content. Advanced methods are necessary in these settings where traditional seismic interpretations fall behind, relying only on structural analysis. This study presents a refined advanced edge-detecting algorithm for noise reduction, while preserving structural and stratigraphic discontinuities. Using seismic attributes as indicators for hydraulic conductivity, we simulate steady-state flow between horizons utilizing enhanced edge-detection techniques by adjusting iteration weights to improve image clarity, revealing fault connections that were previously undetectable. This workflow applied to a reprocessed depth migrated seismic data converted to time, revealed lateral separation between zones supporting the hypothesis of hydraulic connectivity across faulted zones. The results offer improved tools for complex subsurface geological structures and provide critical insights for reservoir development and management in unconventional reservoirs.