Velocity anisotropy estimation for brine-saturated sandstone and shale

Tsuneyama, Futoshi; Mavko, Gary

doi:10.1190/1.2056371

Cited by 16 publications

(6 citation statements)

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“…with a linear correlation coefficient of 0.7463 based on 259 samples of sandstones, shales, coals and carbonates. Tsuneyama and Mavko (2005) reported a similar regression…”

Section: O R R E L a T I O N A N D P R I N C I P A L C O M P O N E mentioning

confidence: 63%

“…Wang (2002) reported a regression of with a linear correlation coefficient of 0.7463 based on 259 samples of sandstones, shales, coals and carbonates. Tsuneyama and Mavko (2005) reported a similar regression based on brine‐saturated sandstone and shale samples.…”

Section: Correlation and Principal Component Analysismentioning

confidence: 70%

“…Thomsen's ɛ and γ parameters have often been reported to be well correlated (e.g., Sondergeld et al . 2000; Wang 2002; Tsuneyama and Mavko 2005). Wang (2002) reported a regression of with a linear correlation coefficient of 0.7463 based on 259 samples of sandstones, shales, coals and carbonates.…”

Section: Correlation and Principal Component Analysismentioning

confidence: 99%

“…Thomsen's ε and γ parameters have often been reported to be well correlated (e.g., Sondergeld et al 2000;Wang 2002;Tsuneyama and Mavko 2005). Wang (2002) reported a regression of…”

Section: O R R E L a T I O N A N D P R I N C I P A L C O M P O N E mentioning

confidence: 99%

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A statistical review of mudrock elastic anisotropy

Horne¹

2013

Geophysical Prospecting

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Mudrocks, defined to be fine‐grained siliclastic sedimentary rocks such as siltstones, claystones, mudstones and shales, are often anisotropic due to lamination and microscopic alignments of clay platelets. The resulting elastic anisotropy is often non‐negligible for many applications in the earth sciences such as wellbore stability, well stimulation and seismic imaging. Anisotropic elastic properties reported in the open literature have been compiled and statistically analysed. Correlations between elastic parameters are observed, which will be useful in the typical case that limited information on a rock's elastic properties is known. For example, it is observed that the highest degree of correlation is between the horizontal elastic stiffnesses C11 and C66. The results of statistical analysis are generally consistent with prior observations. In particular, it is observed that Thomsen's ɛ and γ parameters are almost always positive, Thomsen's ɛ and γ parameters are well correlated, Thomsen's δ is most frequently small and Thomsen's ɛ is generally larger than Thomsen's δ. These observations suggest that the typical range for the elastic properties of mudrocks span a sub‐space less than the five elastic constants required to fully define a Vertical Transversel Isotropic medium. Principal component analysis confirms this and that four principal components can be used to span the space of observed elastic parameters.

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“…with a linear correlation coefficient of 0.7463 based on 259 samples of sandstones, shales, coals and carbonates. Tsuneyama and Mavko (2005) reported a similar regression…”

Section: O R R E L a T I O N A N D P R I N C I P A L C O M P O N E mentioning

confidence: 63%

Section: Correlation and Principal Component Analysismentioning

confidence: 70%

Section: Correlation and Principal Component Analysismentioning

confidence: 99%

“…Thomsen's ε and γ parameters have often been reported to be well correlated (e.g., Sondergeld et al 2000;Wang 2002;Tsuneyama and Mavko 2005). Wang (2002) reported a regression of…”

Section: O R R E L a T I O N A N D P R I N C I P A L C O M P O N E mentioning

confidence: 99%

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A statistical review of mudrock elastic anisotropy

Horne¹

2013

Geophysical Prospecting

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“…The well‐log database includes all the standard logs from the one vertical well and the three deviated wells. The velocity logs for the deviated wells are corrected in terms of velocity anisotropy by the method discussed by Tsuneyama and Mavko (2005). The V S log is not available for all the wells, but we generate these logs when necessary using , which gives a good fit to the actual data of available V P and V S in this area, under brine‐saturated conditions.…”

Section: Validation Of the Methods With A Case Studymentioning

confidence: 99%

Elastic‐impedance analysis constrained by rock‐physics bounds^†

Tsuneyama

Mavko

2007

Geophysical Prospecting

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A B S T R A C TBy applying seismic inversion, we can derive rock impedance from seismic data. Since it is an interval property, impedance is valuable for reservoir characterization. Furthermore, the decomposition of the impedance into two fundamental properties, i.e. velocity and density, provides a link to the currently available rock-physics applications to derive quantitative reservoir properties. However, the decomposition is a challenging task due to the strong influence of noise, especially for seismic data with a maximum offset angle of less than 30 • . We present a method of impedance decomposition using three elastic impedance data derived from the seismic inversion of angle stacks, where the far-stack angle is 23.5 • . We discuss the effect of noise on the analysis as being the most significant cause of making the decomposition difficult. As the result, the offset-consistent component of noise mostly affects the determination of density but not the velocities (P-and S-wave), whereas the effect of the random component of noise occurs equally in the determination of the velocities and density. The effect is controlled by the noise enhancement factor 1/A, which is determined by a combination of stack angles. Based on the results of the analysis, we show an innovative method of decomposition incorporating rock-physics bounds as constraints for the analysis. The method is applied to an actual data set from an offshore oilfield; we demonstrate the result of analysis for sandbody detection. I N T R O D U C T I O NElastic impedance (EI) is the apparent seismic impedance at specific angles of incidence, which was derived by Connolly (1998, 1999) and Mukerji et al. (1998). The elastic impedance is a function of P-wave velocity (V P ), S-wave velocity (V S ), density (ρ) and the angle of incidence (θ), based on the approximations of Zoeppritz's equations (1919) by Aki and Richards (1980) and Shuey (1985). Therefore, it is possible, in principle, to back-calculate V P , V S and ρ from the three elastic impedances for different stack angles of the same seismic data. Very often, the maximum stack angle of seismic data is limited to less than 30 • , which is not wide enough to obtain stable calculation results. Hence, except for long-offset seismic data with a maximum offset angle greater than approximately 60 • , it is impractical to calculate V P , V S and ρ separately from the seismic data. * Instead of calculating V P , V S and ρ directly from elastic impedances, the required properties are most often estimated by establishing empirical relationships between the impedance and the rock properties at well locations using well-log data. Whitcombe et al. (2002) introduced extended elastic impedance in a modified form of elastic impedance using tanχ as an alternative for sin 2 θ, where χ is an arbitrary angle. They then extended the linear trend of the impedance change as a function of tan χ , from negative infinity at χ = -π/2 to positive infinity at χ = π/2, in place of a limited extension of the horizontal axis f...

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