Integrated of Static and Dynamic Modeling Workflow for Belimbing Oil Field Development of Talangakar Sandstone Reservoir, South Sumatra Basin

Abstract

The Belimbing Field S layer is a productive layer of the Upper Talangakar Formation (TRM) with a transition environment, deposited in the syn-rift phase at upper Oligocene to lower Miocene. Belimbing S layer contributes as main production reservoir with 645 bopd (96% watercut). Water injection at Bel-10 wells and Bel-11 wells, at central block, was first performed in October 1997 with 762 bwipd injection rate. The water injection was performed peripherally from flank, with the initial purpose to pressure maintenance, even though the water was injected into the oil zone. There was a significant increase in pressure and oil gain in the monitor wells. With the last RF of 30% indicates that this layer still has a lot of potential to be developed by waterflood method. The BEL-19 injection in the Eastern Block from 2005 to 2015 was success too, indicated by increased of pressure and production at BEL-12, BEL-14 and BEL-27 wells. As an effort to increase production, field development studies were conducted by G&G study and dynamic modeling.
Limitations on number of core data (SCAL and RCA) become obstacles in G&G and Reservoir modeling, so in rock typing, we used core data from the nearest field (Limau Niru). In this method, also performed synthetic data processing curve relative permeability and capillary pressure by evaluating production data. The distribution of acoustic impedance (AI) and waveform is required to know the distribution of Facies and reservoir properties to get a more detailed description and heterogeneity of the reservoir.
Limitations on number of core data (SCAL and RCA) become obstacles in G&G and Reservoir modeling, so in rock typing, we used core data from the nearest field (Limau Niru). In this method, also performed synthetic data processing curve relative permeability and capillary pressure by evaluating production data. The distribution of acoustic impedance (AI) and waveform is required to know the distribution of Facies and reservoir properties to get a more detailed description and heterogeneity of the reservoir.
From the data above, we obtain rock typing to distribute reservoir property in 3D static and dynamic model. Through the initialization process, history matching and forecast is then processed the best scenario, the waterflood pattern in the form of inverted five spots and primary infill to optimize the oil recovery.