APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS

There are a lot of methods to interpret seismic post-stack data, such as acoustic impedance inversion and multi-attribute method. Both methods are to integrate between seismic and well data. Acoustic impedance inversion method is used to predict physical properties of rocks, specifically the acousti...

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Main Authors: , SITI NUR INDAH ARUMI, , Dr. Ing. Ari Setiawan, M.Si.
Format: Theses and Dissertations NonPeerReviewed
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
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ETD
Online Access:https://repository.ugm.ac.id/130044/
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spelling id-ugm-repo.1300442016-03-04T08:12:58Z https://repository.ugm.ac.id/130044/ APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS , SITI NUR INDAH ARUMI , Dr. Ing. Ari Setiawan, M.Si. ETD There are a lot of methods to interpret seismic post-stack data, such as acoustic impedance inversion and multi-attribute method. Both methods are to integrate between seismic and well data. Acoustic impedance inversion method is used to predict physical properties of rocks, specifically the acoustic impedance values. Model based inversion is one of the most popular inversion techniques. By using the logs values from the wells, the initial model is made and then be analyzed with the real seismic data in order to obtain the minimum error prediction and the best correlation between the initial model and the real seismic data. On the other hand, multi-attribute method is employed to predict the well-log properties from seismic data. The log properties that will be predicted are porosity logs. The objective of this method is to derive a multi-attribute transform, which is a linear or nonlinear transformation, between the attributes from seismic data and the target log values from wells. Linear transformation is derived by least-squares minimization, whereas the nonlinear transformation is derived by train the neural networks, especially the Probabilistic Neural Networks (PNN). Both methods are applied to seismic data set in block F3 Netherlands, and the result of acoustic impedance values are about 4700-5600 (m/s)(gr/cc) and the porosity values range from 0.27 � 0.32 fraction for shaly sand 1, whereas for shaly sand 2, the acoustic impedance values are about 3600 � 4500 (m/s)(gr/cc) and the porosity values are 0.34 � 0.37 fraction. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , SITI NUR INDAH ARUMI and , Dr. Ing. Ari Setiawan, M.Si. (2014) APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=70455
institution Universitas Gadjah Mada
building UGM Library
country Indonesia
collection Repository Civitas UGM
topic ETD
spellingShingle ETD
, SITI NUR INDAH ARUMI
, Dr. Ing. Ari Setiawan, M.Si.
APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
description There are a lot of methods to interpret seismic post-stack data, such as acoustic impedance inversion and multi-attribute method. Both methods are to integrate between seismic and well data. Acoustic impedance inversion method is used to predict physical properties of rocks, specifically the acoustic impedance values. Model based inversion is one of the most popular inversion techniques. By using the logs values from the wells, the initial model is made and then be analyzed with the real seismic data in order to obtain the minimum error prediction and the best correlation between the initial model and the real seismic data. On the other hand, multi-attribute method is employed to predict the well-log properties from seismic data. The log properties that will be predicted are porosity logs. The objective of this method is to derive a multi-attribute transform, which is a linear or nonlinear transformation, between the attributes from seismic data and the target log values from wells. Linear transformation is derived by least-squares minimization, whereas the nonlinear transformation is derived by train the neural networks, especially the Probabilistic Neural Networks (PNN). Both methods are applied to seismic data set in block F3 Netherlands, and the result of acoustic impedance values are about 4700-5600 (m/s)(gr/cc) and the porosity values range from 0.27 � 0.32 fraction for shaly sand 1, whereas for shaly sand 2, the acoustic impedance values are about 3600 � 4500 (m/s)(gr/cc) and the porosity values are 0.34 � 0.37 fraction.
format Theses and Dissertations
NonPeerReviewed
author , SITI NUR INDAH ARUMI
, Dr. Ing. Ari Setiawan, M.Si.
author_facet , SITI NUR INDAH ARUMI
, Dr. Ing. Ari Setiawan, M.Si.
author_sort , SITI NUR INDAH ARUMI
title APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
title_short APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
title_full APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
title_fullStr APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
title_full_unstemmed APLIKASI METODE INVERSI IMPEDANSI AKUSTIK DAN PERBANDINGAN METODE MULTIATRIBUT MENGGUNAKAN REGRESI LINIER DENGAN NEURAL NETWORKS PADA LAPANGAN F3, NORTHSEA, NETHERLANDS
title_sort aplikasi metode inversi impedansi akustik dan perbandingan metode multiatribut menggunakan regresi linier dengan neural networks pada lapangan f3, northsea, netherlands
publisher [Yogyakarta] : Universitas Gadjah Mada
publishDate 2014
url https://repository.ugm.ac.id/130044/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=70455
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