Hydrocarbon detection for cavern carbonate reservoir using low-and-high-frequency anomalies in spectral decomposition

Li, Fangyu; Lu, Wenkai; Li, Yandong; Yan, Zhang; Xiaodong, Zheng

doi:10.1190/segam2012-0473.1

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…Edited by Jie Hao Li et al 2011) and indicate hydrocarbon (such as oil, gas or gas hydrate) in sandstone or carbonate reservoirs (e.g., Castagna et al 2003;Li et al 2012). Therefore, SD has been routinely used in thin-bed prediction (Marfurt and Kirlin 2001), reservoir characterization (Li et al 2011), and hydrocarbon detection (Huang et al 2016;Naseer and Asim 2018).…”

Section: Introductionmentioning

confidence: 99%

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

et al. 2020

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An important application of spectral decomposition (SD) is to identify subsurface geological anomalies such as channels and karst caves, which may be buried in full-band seismic data. However, the classical SD methods including the wavelet transform (WT) are often limited by relatively low time–frequency resolution, which is responsible for false high horizon-associated space resolution probably indicating more geological structures, especially when close geological anomalies exist. To address this issue, we impose a constraint of minimizing an lp (0 < p < 1) norm of time–frequency spectral coefficients on the misfit derived by using the inverse WT and apply the generalized iterated shrinkage algorithm to invert for the optimal coefficients. Compared with the WT and inverse SD (ISD) using a typical l1-norm constraint, the modified ISD (MISD) using an lp-norm constraint can yield a more compact spectrum contributing to detect the distributions of close geological features. We design a 3D synthetic dataset involving frequency-close thin geological anomalies and the other 3D non-stationary dataset involving time-close anomalies to demonstrate the effectiveness of MISD. The application of 4D spectrum on a 3D real dataset with an area of approximately 230 km2 illustrates its potential for detecting deep channels and the karst slope fracture zone.

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Section: Introductionmentioning

confidence: 99%

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

et al. 2020

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Extraction of strong beadlike reflections for a carbonate-karst reservoir using a tensor-based adaptive mathematical morphology

Wang

et al. 2017

J. Geophys. Eng.

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Robust time-frequency analysis of seismic data using general linear chirplet transform

et al. 2018

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Time-frequency analysis (TFA) has been widely used in seismic processing and interpretation. A good time-frequency representation can preferably characterize geologic spatial distribution and detect hydrocarbon reservoir anomalies. This paper applies a robust seismic TFA method based on the general linear chirplet transform (GLCT). The GLCT method is an extended form of LCT, which is a unifying framework encompassing the short time Fourier transform (STFT) and the continuous wavelet transform (CWT) using the chirplet atom as the kernel function instead of the sinusoidal wave or wavelets. By rotating the chirplet atom at each time-frequency point, GLCT method could adaptively choose the best atom to fit the local time-frequency feature of seismic signals. The algorithm follows such a simple logic and produces a broadband time-frequency spectrum free of cross-term interference, resulting in good performance characterizing the instantaneous spectral variations. Synthetic data analysis demonstrates that the GLCT method is able to reach a higher energy concentration in the time-frequency plane than conventional methods. Robustness analysis indicates that GLCT produces more stable results that outperform not only STFT, CWT, but also high-resolution methods such as the synchrosqueezing transform and complete ensemble empirical mode decomposition in the case of noisy data. The application to field data illustrates that the isofrequency attributes extracted by GLCT through spectral decomposition could effectively image subtle stratigraphic structures of the subsurface paleotopography and highlight the frequency anomalies associated with hydrocarbons. Sometimes, these anomalies might be otherwise inundated in the background noise. Our method can be a validation tool for seismic facies interpretation improvement and direct hydrocarbon indication in practice.

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Hydrocarbon detection for cavern carbonate reservoir using low-and-high-frequency anomalies in spectral decomposition

Cited by 7 publications

References 3 publications

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

Extraction of strong beadlike reflections for a carbonate-karst reservoir using a tensor-based adaptive mathematical morphology

Robust time-frequency analysis of seismic data using general linear chirplet transform

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