用部分叠位地震反演估计无各向异性声阻抗:以马来盆地Inas油田为例

M. Gouda, A. Salim, G. Hamada
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引用次数: 1

摘要

地震速度是一个关键的地球物理特性,它包含了关于地球各层的重要信息。地层的分层、粘土含量和层内裂缝是导致地震速度依赖于入射角的主要原因,即“地震各向异性”。地震各向异性会影响与速度相关的属性,如声阻抗,从而导致地震数据的错误解释。因此,对声阻抗进行各向异性校正对减小不确定性具有重要意义。各向异性参数可以用各种方法从岩心资料和测井资料中获得。然而,以往工作的共同局限性是,在井位上获得各向异性,而忽略了层的横向非均质性。本研究的目的是通过近、中、远角叠的部分叠反演获得无各向异性声阻抗(Zp0)。利用反演的输出通过Thomsen各向异性方程的重构、统计建模和多层前馈神经网络理论(MLFN)三种不同的方法获得Zp0。与未校正的p -阻抗相比,MLFN和重构Thomsen模型的结果与阻抗测井曲线匹配更好,层间横向连续性更明显,振幅谱增强。这种改进降低了最终油藏模型的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Anisotropy-Free Acoustic Impedance from Partial-Stack Seismic Inversion: A Case Study From Inas Field, Malay Basin
Summary 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 Multi-Layer 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.
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