Perpustakaan Digital ITB

Mature reservoir management requires efficient methodologies to evaluate reservoir performance amid increasing uncertainty caused by subsurface heterogeneity, declining production patterns, and limited reservoir information. This study develops a data-driven integrated workflow by combining static reservoir characterization, production performance analysis, and fuzzy pattern recognition to evaluate the relative quality of reservoirs in Norne Field. Static reservoir evaluation was performed using spatial partitioning to estimate local reservoir volume distributions, while production decline analysis and type curve matching were applied to characterize dynamic reservoir behavior. The estimated parameters were then further calibrated by history matching and further evaluated using fuzzy clustering to generate a continuous reservoir quality distribution. The results of the study show that the developed workflow successfully identified the dominant reservoir quality trends across the field. Validation against conventional numerical simulation-based potential assessments showed comparable spatial trends, although differences still exist in areas influenced by complex compartmentalization structures. Overall, this integrated study provides an efficient screening approach for reservoir quality evaluation to support future development optimization and the identification of potential opportunities within the reservoir.