Abstract

Traditional sedimentary depositional modelling, often reliant on drilled well data, structural configurations and conventional seismic amplitude attributes, proves inadequate for less explored or frontier sedimentary basins. Such areas, characterized by geological complexity and sparse in-situ information, necessitate a more constrained and data-driven approach to model generation, moving beyond conceptual basin evolution understandings. This study employs Enhanced Seismic Facies Analysis (ESFA), a methodology that integrates seismic signatures with sedimentary facies characteristics, to develop a more constrained geological model. ESFA systematically analyses nine key geoscientific parameters - position, external form, internal configuration, amplitude, frequency, continuity, smoothness, appearance, and waveform to precisely identify sedimentary facies [1]. Applying this methodology, the present work focuses on understanding and capturing seismic sedimentary facies signatures within shallow to deep marine environments, subsequently building a robust 2D geological model in Mannar Offshore area. This approach not only yields superior information regarding sedimentary facies but also significantly aids in predicting paleo-environments at various locales, thereby establishing a more reliable model for petroleum system understanding. Furthermore, the insights gained from this methodology present a promising avenue for training Artificial Intelligence/Machine Learning models to generate comprehensive 3D sedimentary deposition models in the in-situ data scarce regions.