ANALYSIS OF HYDROCARBON AND ECONOMIC PAY RESERVOIRS DISTRIBUTION FOR PRODUCTION IN LATE MIOCENE SEQUENCE IN SADEWA FIELD, KUTAI BASIN USING INTEGRATION OF SEISMIC STRATIGRAPHY, SEISMIC INVERSION, AND SEISMIC ATTRIBUTE METHODS

• Main Author: Jocelynn Untung, Ilona
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/57012
• Institution: Institut Teknologi Bandung

In identifying hydrocarbon reservoir distribution, it can be done by analysing the physical properties of the reservoir. The research field to be identified focusing on Sadewa Field in Kutai Basin, with depositional environment in upper slope. The target of this field is sandstone formation and the reservoir contains of thin layer of sandstone with hydrocarbon content which is very hard to identify. This research not only looking for the hydrocarbon reservoir distribution, but also define the seismic facies depositional environment in the target and looking for the economic pay distribution map. Economic pay is a parameter value for identify economic hydrocarbon reservoir to produce. With integration of seismic stratigraphy, seismic inversion, and seismic attribute methods are expected to get the distribution map of economic pay for developing Sadewa Field in the future. The research is using 3D PSTM seismic data and seven well data. The scope of this research is analysing data between DL4Bsb and DL4Asb, that were correlated with the biostratigraphy report. Model Based AI inversion is proceeds because AI can separate gas sand with wet sand and shale from the crossplot, also AI inversion reflect the physical property of the rock. By using attribute seismic such as cosine phase, variance, RMS Amplitude, and sweetness, it can support for interpretation of the seismic data, depositional enviroment, and mapping the hydrocarbon reservoir distribution. Spectral decomposition is used to see the facies and the direction of the hydrocarbon reservoir distribution more clearly. There are two sandstone targets in DL4Bsb and DL4Asb, namely 960 and 990 and the targets are located between thick shale. Crossploting all of the attributes with the thickness of the economic pay will helps to identifying the target. By multiplying RMS Amplitude attribute and 1/Variance attribute, it can define the economic pay hydrocarbon reservoir distribution, because the correlation between the attribute and the thickness is very high