Machine learning assisted history matching for a deepwater lobe system

High exploration costs resulting in sparse datasets and complicated geological structures in deepwater depositional systems make the reservoir characterization extremely difficult. To meet this challenge, rule-based geostatistical subsurface modeling has been developed to fill this data gap with enhanced integration of geological concepts for deepwater reservoirs based on geological rules constrained by this sparse well data. The rule-based model simulates sediment dynamics through depositional rules to calculate reservoir architecture and the associated rock properties distributions. As a result, rule-based models incorporate conceptual and qualitative information, such as temporal deposition sequence and consequent compensational stacking patterns. Integrating quantitative data, such as fluid production history, is a remaining obstacle to the broad application of rule-based subsurface models.