Abstract

Seismic Reservoir characterisation is a critical step in understanding the depositional environment and reservoir property distribution. The conventional way approach integrates multiple, yet relevant seismic attributes to effectively predict the distribution of reservoir facies and hydrocarbon presence beyond well control. However, these conventional approaches are computation intensive, association of human subjectivity and oversights, and time consuming, which sometimes yet may not produce desired outcomes. To address these challenges, a novel approach is proposed for fully automated Seismic facies classification using AI/ML driven unsupervised algorithms. These automated seismic facies classification will be beneficial within the reservoir characterization process to map the depositional environment and reservoir properties in an area. Well data has been integrated as posteriori information with the outcomes for understanding the seismic facies classification and calibrating the interpretation