ANALYSIS OF SANDSTONE DISTRIBUTION AND RESERVOIR POTENTIAL IN THE 990-1090 INTERVAL OF LATE MIOCENE SEQUENCE USING SEISMIC ATTRIBUTES, SEISMIC STRATIGRAPHY, AND STOCHASTIC SEISMIC INVERSION IN THE - Fajri Amin, Thaha
The Kutai Basin is one of the basins with the largest hydrocarbon reserves in Indonesia. The "WILHELM" field is in a shelf slope depositional environment and is part of the Kutai Basin which is the focus of this study. In this field there is a reservoir in the form of thin sandstone layers which are in the Late Miocene age range. This study aims to identify the seismic facies and physical properties (porosity, pore fluid, saturation) of the depositional environment of the sandstone hydrocarbon reservoir in the target area of sandstone sequences 990 - 1090 in the "WILHELM" Field. The target reservoir in the Kampung Baru formation is thin sandstone, making it difficult to identify by seismic data. Therefore, a geostatistical approach is needed by carrying out stochastic inversion to analyze the reservoir of WKH .DPSXQJ%DUXIRUPDWLRQLQ WKH³:,/+(/0´ )LHOG LQ RUGHU WR EH DEOHWR resolution thin layers of sandstone and see the direction of distribution of deposition. This study will use 3D Pre-Stack Time Migration seismic data which have become angle stacks in the time domain with full stack, near angle stack, mid angle stack, and far angle stack. Then, this study uses seven well from nine wells that have identified sandstone reservoirs. The target zone will be further identified using log data, biostratigraphic reports and seismic data. Based on the tunning thickness analysis, the thickness of the individual sandstones in the Kampung Baru formation is below vertical resolution, so the sandstone zone below vertical seismic resolution can be performed using stochastic inversion. Stochastic inversion will produce several realizations and the results of these realizations are selected based on the smallest error map value. Then, an analysis is carried out to separate the lithology and fluid contained by using a crossplot from the log data. In this study, the results of stochastic inversion focused on elastic parameters were used based on the results of the sensitivity analysis.