KARAKTERISASI RESERVOIR MENGGUNAKAN INVERSI SEISMIK IMPEDANSI AKUSTIK DAN MULTI ATRIBUT SEISMIK PROBABILISTIC NEURAL NETWORK (PNN) PADA LAPANGAN ‘X’ SUB-CEKUNGAN TAMIANG CEKUNGAN SUMATRA UTARA

EKA PUTRI ARIANTI, ANGGI and Yusup Nur Khakim, Mokhamad and Poerwono, Pradanto (2019) KARAKTERISASI RESERVOIR MENGGUNAKAN INVERSI SEISMIK IMPEDANSI AKUSTIK DAN MULTI ATRIBUT SEISMIK PROBABILISTIC NEURAL NETWORK (PNN) PADA LAPANGAN ‘X’ SUB-CEKUNGAN TAMIANG CEKUNGAN SUMATRA UTARA. Undergraduate thesis, Sriwijaya University.

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Abstract

Tamiang Sub-basin of The North Sumatra Basin is the first area in Indonesia to produce hydrocarbons. The data used in this research are 5 well data and 3D Post Stack seismic data. The target was determined primarily on Miocene aged anticlines and reef bodies. Integration of well data and seismic data needs to be done, because it is useful to get a good interpretation of the hydrocarbon exploration process. Well data used petrophysical analysis to obtain hydrocarbon saturation values and then integrated using bandlimited inversion methods and multi attributes seismic methods. The bandlimited inversion method is used to calculate the impedance of the next rock layer from the previous layer in the form of physical information on the earth based on the information of the symmetrical record which is controlled by the well data. While multi-attribute seismic methods are use more than one attributes to predict some physical properties of the earth. The prediction carried out is the porosity distribution of seismic volume including porosity in C-26 and E-29 wells, field X which is assumed to have reservoir continuity which accumulates hydrocarbons. To increase the correlation between actual log and predict log, the Probabilistic Neural Network method is used. From the prediction results it is known that the distribution of X field porosity in the target reef zone is in the range of 15.5% - 17.5%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Tamiang Sub-Basin, North Sumatra Basin, Bandlimited Inversion, Reef, Multiattributtes, Porosity, Probablistic Neural Network
Subjects: Q Science > QC Physics
Q Science > QC Physics > QC801-809 Geophysics. Cosmic physics
Divisions: 08-Faculty of Mathematics and Natural Science > 45201-Physics (S1)
Depositing User: Users 1636 not found.
Date Deposited: 03 Sep 2019 02:36
Last Modified: 03 Sep 2019 02:36
URI: http://repository.unsri.ac.id/id/eprint/6081

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