A Novel Hydrocarbon Detection Approach via High-Resolution Frequency-Dependent AVO Inversion Based on Variational Mode Decomposition

Abstract

Amplitude-versus-offset (AVO) inversion always plays an important role in reservoir fluid identification, which allows the estimation of various rock and fluid properties from prestack seismic data. In this paper, we propose a new method for discrimination of hydrocarbon accumulation that combines frequency-dependent AVO inversion scheme and variational mode decomposition (VMD). VMD is a recently developed algorithm for adaptive signal decomposition that is able to nonrecursively decompose a multicomponent signal into a number of quasi-orthogonal intrinsic mode functions and avoid mode mixing effectively. VMD is superior to other state-of-the-art approaches in obtaining high-resolution and high-fidelity local time-frequency depiction performance. Two synthetic signals are employed to illustrate that VMD achieves higher temporal and frequency resolution than the conventional continuous wavelet transform (CWT) decomposition. Other synthetic examples, elastic and dispersive, are utilized to demonstrate that the proposed method is more reliable for the detection of hydrocarbon saturation and a comparison is made with the CWT-based inverted results. Application on field data has further shown that the proposed approach has the potential in identifying the reservoir related to hydrocarbon.