Thin‐bed reflectivity inversion

Chopra, Satinder; Castagna, John P.; Portniaguine, Oleg

doi:10.1190/1.2369941

Cited by 57 publications

(30 citation statements)

References 5 publications

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“…Partyka et al (1999), Partyka (2005), Marfurt and Kirlin (2001) also use spectral decomposition to predict the thickness of imaged sedimentary layers. While Chopra et al (2006) and Puryear and Castagna (2008) use spectral inversion for layer thickness determination. Adriansyah and McMechan (2002) detect thin reservoirs from attribute analysis, acoustic impedance inversion, and full-wavefield modeling.…”

Section: Introductionmentioning

confidence: 99%

Rock physics-based seismic trace analysis of unconsolidated sediments containing gas hydrate and free gas in Green Canyon 955, Northern Gulf of Mexico

Zhang

McConnell²,

Han

2012

Marine and Petroleum Geology

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Section: Introductionmentioning

confidence: 99%

Rock physics-based seismic trace analysis of unconsolidated sediments containing gas hydrate and free gas in Green Canyon 955, Northern Gulf of Mexico

Zhang

McConnell²,

Han

2012

Marine and Petroleum Geology

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“…Portniaguine and Castagna (2004) used spectral decomposition of the data together with three different constraints (namely, the L1 norm, L2 norm, and sparse spike constraints) to prove the applicability of spectral decomposition as well as the higher resolution of results using the sparse spike constraint. Puryear (2006), Chopra et al (2006aChopra et al ( , 2006b, and Puryear and Castagna (2006) showed that the inversion of spectral decompositions for layer properties can be improved when the reflection coefficients are determined simultaneously, which is to say that the inversion should be launched on the reflectivity series instead of on the trace itself.…”

Section: Introductionmentioning

confidence: 96%

A Discussion on Factors Affecting the Results of Thin Bed Thickness Determination Using Spectral Inversion

Taheri

Javaherian

Salahshoor

2012

Petroleum Science and Technology

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Layer thickness is regarded as the basis for other steps of reservoir evaluation and simulation. This makes it highly important in oil exploration and production, motivating researchers to establish several methods to determine it. Having a migrated zero-offset seismic section, it is usually easy to evaluate the thickness of a layer from the distance between the top and the base of the layer in a peak-to-peak or trough manner. However, in locations wherein the layer thickness is not sufficient to yield two distinct peaks/troughs from its top and base-that is, when the layer thickness is below the tuning thickness-this simple method may not be useful. Hence, other methods need to be developed for this case. Spectral inversion is a method that uses spectral decomposition to improve images of layers whose thicknesses are below the tuning thickness. In this method, a procedure is formulated to invert the frequency spectra of the layer thickness, which is then applied to synthetic data using a complex spectral analysis. Layer thicknesses significantly below the seismic tuning thickness can be accurately determined in this manner without amplitude calibration. In this article, the spectral inversion method is applied to symmetric and asymmetric wedge models and the result is evaluated by changing different parameters to investigate their effects. It is concluded that the approach not only guarantees the effectiveness of spectral inversion but makes it fast enough to be practical in use for a short frequency band of the lowest available frequencies together with short enough investigation intervals for layer thickness (T) and even component of reflectivity series squared minus odd component of reflectivity series squared, k D r 2 e r 2 o .

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“…Variations in wave amplitude and phase, caused by layers of different thicknesses, jointly with interference patterns, allow the extraction of stratigraphic details such as thicknesses and lateral continuity (Partyka, 2005). Now, as it is unlikely for a lithological unit to have the same coefficients at its top and its base, the relevant pair of reflection coefficients r 1 and r 2 may be decomposed into an even part (r 1 + r 2 ) 2 ⁄ and an odd part (r 1 − r 2 ) 2 ⁄ (Castagna, 2004;Chopra, Castagna & Portniaguine, 2006). This results in the Spectral Inversion (Puryear & Castagna, 2008) that transforms the amplitude spectrum of a seismic trace estimated in a narrow window in a reflectivity series which depends on the thicknesses of layers in the window of analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of spectral inversion and fuzzy logic interpolation in post-stack resolution enhancement on a seismic volume of the Colombian llanos basin

Ramı́rez

Almanza

Vides

2017

CT&F Cienc. Tecnol. Futuro

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) and the lateral resolution using a fuzzy logic interpolation (Souverville et al., 2015). Both the Spectral Inversion using a genetic algorithm and the interpolation are implemented in Matlab®, and tested in synthetic data and in a high resolution digital image, where both algorithms showed good performance. Finally, when applied to the C7 unit of the Carbonera formation in a seismic volume of the Llanos Basin (Colombia), a seismic cube of greater resolution in space and time was obtained. The seismic attributes variance and sweetness were estimated before and after applying the Spectral Inversion and interpolation, noting that applying both gave higher interpretability to the stratigraphic facies. The interpretation of three different time slices allowed inferring formation environments that were validated with well information. et al., 2015). A inversão espectral mediante algoritmo genético e a interpolação foram implementadas em Matlab®, sendo testadas em dados sintéticos, bem como na imagem digital de alta resolução, onde ambos algoritmos demostraram um bom desempenho. Finalmente, ao ser aplicado a um volume sísmico da Bacia Llanos no intervalo C7 da Formação Carbonífera (Colombia), conseguimos um cubo sísmico de maior resolução em espaço e tempo. Foram calculados os atributos sísmicos variância e "sweetness" antes e depois da aplicação da inversão espectral e a interpolação, observando que a aplicação de ambos resultava em maior interpretabilidade às fácies estratigráficas. A interpretação de três mapas sísmicos a diferentes tempos ("time slices") permitiu inferir ambientes de formação que foram validados com informação de poço. P ara mejorar la interpretación estratigráfica de datos sísmicos 3D se procuró aumentar la resolución vertical mediante una inversión espectral (Puryear & Castagna, 2008;Qiu, Junhua, Xiaohui, Hu, & Lei, 2016) y la resolución lateral usando una interpolación de lógica difusa (Souverville et al., 2015). La inversión espectral mediante algoritmo genético y la interpolación se implementaron en Matlab®, y se probaron en datos sintéticos así como en una imagen digital de alta resolución, donde ambos algoritmos demostraron un buen desempeño. Finalmente, al aplicarse a un volumen sísmico de la Cuenca Llanos en el intervalo C7 de la Formación Carbonera (Colombia), se consiguió un cubo sísmico de mayor resolución en espacio y tiempo. Se estimaron los atributos sísmicos varianza y "sweetness" antes y después de aplicar la inversión espectral y la interpolación, observando que la aplicación de ambos daba mayor interpretabilidad a las facies estratigráficas. La interpretación de tres mapas sísmicos a diferentes tiempos ("time slices") permitió inferir ambientes de formación que fueron validados con información de pozo. ABSTRACT

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Thin‐bed reflectivity inversion

Cited by 57 publications

References 5 publications

Rock physics-based seismic trace analysis of unconsolidated sediments containing gas hydrate and free gas in Green Canyon 955, Northern Gulf of Mexico

Rock physics-based seismic trace analysis of unconsolidated sediments containing gas hydrate and free gas in Green Canyon 955, Northern Gulf of Mexico

A Discussion on Factors Affecting the Results of Thin Bed Thickness Determination Using Spectral Inversion

Application of spectral inversion and fuzzy logic interpolation in post-stack resolution enhancement on a seismic volume of the Colombian llanos basin

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