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

Autor: Ricky Wicaksono, Karolina Bala Gadi, Abdurahman F. Muslim, Yoga Wismoyo, Grace Stephani
Rok vydání: 2019
Předmět:
Zdroj: Journal of Physics: Conference Series. 1363:012034
ISSN: 1742-6596
1742-6588
DOI: 10.1088/1742-6596/1363/1/012034
Popis: The Belimbing Field S layer is a layer of the Upper Talangakar Formation (TRM) with a transition precipitation environment deposited in the syn-rift phase with the upper Oligocene lifetime up to the lower Miocene productively producing oil. This is a sandstone layer that contributes the largest production current with 645 bopd production (96% watercut) of OOIP 107 MMBBL. Water injection at Bel-10 wells and Bel-11 wells was first performed in October 1997 with an injection rate of 762 bwipd. The water injection is performed peripherally from the north flank of the star fruit structure (Central Belimbing block) with the initial goal of pressure maintenance even though the water is injected into the oil zone in the S layer. There is a significant increase in pressure and oil gain in the monitor wells in the injection area. 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 detected by success with increased pressure and increased production at BEL-12, BEL-14 and BEL-27 wells. As an effort to increase production, field development studies were conducted for GnG study and dynamic modelling. Limitations on the number of core data (SCAL and RCA) become obstacles in G & G and Reservoir modeling, so in determining rock typing used core data from the nearest field (LimauNiru) is L5A-240 well. In this method also performed synthetic data processing curve relative permeability and capillary pressure by evaluating production data. The distribution of artificial intellegent (AI) and wavefom is required to know the distribution of Facies and reservoir properties so as 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 recovery of oil recovery.
Databáze: OpenAIRE
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