Reducing Risks through Seismic Inversion and Comprehensive Prospectivity Analysis

The field illustrated here produces from a clastic reservoir. This reservoir’s varying thickness and lateral heterogeneity pose significant challenges, which are being addressed through pre-stack seismic inversion and machine learning-driven reservoir characterization. A prospectivity analysis, incorporating VP/VS ratio, sand thickness from supervised classification, and high-quality reservoir zones from unsupervised clustering, is proposed to target optimal drilling locations. Additionally, a multi-seed stochastic approach is used to handle uncertainties, mitigating risk.