Application of Hilbert-Huang Transform in Effective Reservoir Characterization

One of the paramount goals in petroleum exploration is the identification of the reservoir. The foremost objective of reservoir characterization is for prediction of reservoir precisely. Nowadays, geophysical characterization is used to characterize a reservoir; mainly, seismic data is being used for understanding the reservoir's properties and internal structure. The reservoir can cause anomalies in the frequency of seismic signals. Over the past few decades, various transforms such as Fourier transform (FT) and wavelet transform (WT) have been used to extract the concealed attributes in the data. In the case of the Fourier transform (FT), it is the global transform that cannot reflect local specialty. This transform is the best applicable for stationary and linear data, but most of the geophysical data are non-stationary, nonlinear, and aperiodic in nature. Therefore, FT is good for spectral analysis but not fit for elucidating the temporal characteristics of the data. Wavelet transform (WT) overcomes the problem faced by Fourier transform as it is one of the efficient techniques for both spectral and temporal analysis of the data. The main problem with WT is that it does not work well with nonlinear data and suitable mother wavelet is needed to be assumed. This study focuses on highlighting the anomalies using the Hilbert-Huang Transform, which comprises two techniques, i.e., empirical mode decomposition (EMD) technique and Hilbert spectral analysis (HSA). EMD will decompose a signal into mono-components of frequency termed as intrinsic mode functions (IMFs). In this study, the EMD technique followed by HSA is applied to the data set which highlights the anomalies in the data set and makes facile to characterize a reservoir. It is difficult to characterize a reservoir because of varying properties such as porosity and permeability within small sections of the reservoir. This method shows its effectiveness in characterizing a reservoir.