High-frequency anomalies in carbonate reservoir characterization using spectral decomposition

Abstract

Low-frequency shadows have often been used as hydrocarbon indicators in the application of spectral decomposition. The reason behind the low-frequency anomaly has been explained as high-frequency energy attenuation caused by hydrocarbons. However, in our practice on carbonate reservoir characterization in two areas, Precaspian Basin and Central Tarim Basin, China, we encountered high-frequency anomalies, i.e., the isofrequency slices or sections at high frequencies exhibit anomalies associated with the good carbonate reservoir, particularly in the tight limestone background. We used the product of porosity and thickness as a parameter to measure the quality of the carbonate reservoir of each well and classified the 46 wells in our two studied areas into three types. Type I wells contain high-porosity thick reservoirs, type II wells contain reservoirs with moderate porosity and thickness, and type III wells contain only low-porosity thin reservoirs. The results were that 12 out of 13 type I wells exhibit high-frequency anomalies, and 30 out of 33 type II and type III wells do not exhibit high-frequency anomalies. We further validated the existence of this high-frequency anomaly by forward modeling analysis and fluid substitution experiments using the actual well-log curves measured in the carbonate reservoir. The results showed that in our two studied areas the high-frequency anomalies are more common than low-frequency shadows that can be observed only when the thickness of the reservoir is more than half of the wavelength or the reservoir rocks are extremely unconsolidated. Therefore, this high-frequency anomaly may be used as a more reliable indicator for a good carbonate reservoir than low-frequency shadows in real applications.

Spectral Decomposition and a Waveform Cluster to Characterize Strongly Heterogeneous Paleokarst Reservoirs in the Tarim Basin, China

“…(3) Update these four parameters for an optimal wavelet g γ n by searching within a range D using Equation (8). The searching range around a parameter ξ is [ξ − ∆ξ, ξ + ∆ξ]; for instance, ∆µ is the time-sampling interval.…”

Laboratory‐based seismic attenuation in Fontainebleau sandstone: Evidence of squirt flow

“…Nevertheless, the presence of an intricate pore structure or an inhomogeneous fluid saturation distribution in a reservoir can dramatically influence the magnitude and frequency dependence of attenuation. Once its trends are well quantified, attenuation could be employed as an attribute to aid existing inversion techniques in seismic exploration and, particularly, in reservoir characterization [ Chapman et al ., ; Li et al ., ; Lambert et al ., ; Wu et al ., ]. In the laboratory, the forced oscillation technique provides for the measurement of seismic attenuation and the corresponding modulus dispersion from a sinusoidal stress/strain procedure [ Subramaniyan et al ., ].…”

Spectral decomposition and de‐noising via time‐frequency and space‐wavenumber reassignment

“…Spectral decomposition has been widely used in a variety of seismic processing and interpretation problems such as reservoir characterization (Partyka, Gridley and Lopez ), detection of hydrocarbon‐related frequency anomalies (Castagna, Sun and Siegfried ; Sinha et al . ; Li, Zheng and Zhang ), frequency dependent AVO analysis (Yoo and Gibson ) and deconvolution (Margrave, Lamoureux and Henley ). Space‐wavenumber decomposition methods have also been used to reconstruct seismic signals (Sacchi, Kaplan and Naghizadeh ) and seismic imaging (Ma and Margrave ).…”