Estimation of anisotropy-free acoustic impedance from partial-stack seismic inversion: A case study from Inas Field, Malay Basin.

Gouda, M. and Salim, A.M.A. and Hamada, G. (2019) Estimation of anisotropy-free acoustic impedance from partial-stack seismic inversion: A case study from Inas Field, Malay Basin. In: UNSPECIFIED.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Seismic velocity is a pivotal geophysical property that contains important information about Earth layers. The layering of Earth, clay content and fractures within layers are the main causes of the dependence of seismic velocity on the angle of incidence which is called: �Seismic Anisotropy�. Seismic anisotropy affects the velocity-dependant attributes such as Acoustic Impedance resulting in misinterpretation of seismic data. Consequently, anisotropy correction of acoustic impedance is important to mitigate the uncertainty. Anisotropy parameters can be obtained from core data and well logs with various methods. However, the common limitation in the previous work is that anisotropy is obtained at well locations neglecting the lateral heterogeneity of layers. This study aims to obtain anisotropy-free acoustic impedance (Zp0) from the partial-stack inversion of near, mid and far-angle stacks. The outputs of the inversion are used to obtain Zp0 by three different methods: the refactorization of Thomsen's anisotropy equation, statistical modelling, and the MultiLayer Feedforward Neural Network Theory (MLFN). The results obtained from the MLFN and the refactorized Thomsen's model showed better matching with impedance logs, more obvious lateral continuity of layers and more enhanced amplitude spectrum compared to the uncorrected P-impedance. Such improvement reduces the uncertainty of the final reservoir model. © 2019 EAGE-GSM 2nd Asia Pacific Meeting on Near Surface Geoscience and Engineering.All right reserved.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0
Uncontrolled Keywords: Anisotropy; Feedforward neural networks; Geology; Multilayer neural networks; Seismic waves; Seismology; Well logging, Angle of Incidence; Anisotropy parameters; Geophysical properties; Lateral heterogeneity; Multilayer feedforward neural networks; Reservoir modeling; Seismic velocities; Statistical modelling, Acoustic impedance
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 06:38
Last Modified: 25 Mar 2022 06:38
URI: http://scholars.utp.edu.my/id/eprint/30218

Actions (login required)

View Item
View Item