Geological Outcrop Digital Twin for Hydrocarbon Exploration: Enhancing Reservoir Characterization with Multi-Block Modeling

Abstract

Prior site-scale digital outcrop twin studies have demonstrated that centimeter-scale models can quantify reservoir heterogeneity and support subsurface interpretation (Zhang et al. 2026). Building on that foundation, this paper focuses on the next workflow problem: how multiple outcrop blocks can be organized, analyzed, and reused as a regional exploration asset rather than as isolated digital models. We present a multi-block geological outcrop digital twin platform that integrates UAV photogrammetry, machine learning-assisted lithofacies interpretation, and subsurface data including well logs, seismic interpretations, and thin-section analyses. The proposed framework organizes regional, block, and feature-level information in a unified data model, establishing a reusable database composed of 3D outcrop models, lithology samples, interpretation tracks, and block-level geological datasets. By enabling cross-block comparison of lithofacies, structural measurements, stratigraphic tracks, and subsurface-linked interpretation results, the platform improves the consistency and regional applicability of reservoir characterization. Field applications using anonymized shale and sandstone blocks show that the platform supports regional reservoir analog comparison and reduces repeated manual processing in platform-level workflows. The contribution is therefore not a repeat of site-scale heterogeneity characterization, but an extension from single-outcrop digital twins toward multi-block modeling, collaborative interpretation, and regional reservoir characterization for hydrocarbon exploration.