High‐resolution impedance layering through 3-D stratigraphic inversion of poststack seismic data

Gluck, S.; Juve, E.; Lafet, Y.

doi:10.1190/1.1437790

Cited by 28 publications

(3 citation statements)

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“…Convergence to the final solution is achieved using a full 3D algorithm based on simulated annealing. The resulting layered structure provides an effective resolution of typically an eighth of the wavelength (Gluck et al, 1997;Widess, 1973). The data reduction implicit in the layer definition makes this algorithm extremely robust to noise.…”

Section: Application To Seismic Datamentioning

confidence: 99%

Frequency bandwidth enhancement using 3D layered reflectivity inversion

Walia¹,

Sandanayake²,

Koop³

et al. 2004

SEG Technical Program Expanded Abstracts 2004

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A new methodology for frequency bandwidth enhancement using 3D layered reflectivity inversion is demonstrated in this paper. The inversion is used to obtain layered reflectivity and then band-limited to the outer bounds of the input seismic. The 3D reflectivity inversion starts with a thin layer model based on the geological picks following the structural and stratigraphic trends. The initial thickness of the thin layers is determined by the frequency bandwidth of the seismic data, and the inversion process orients them conformably to stratigraphic layers in the subsurface. The thickness and impedance of each layer (and hence interface reflection coefficient) is perturbed and the corresponding modelled synthetic is compared to the seismic data in a volumetric fashion to minimise the misfit (Walia, 2001). This contributes to the removal of tuning effects and provides a reflectivity sequence with a much higher frequency bandwidth than the seismic data. Subsequently, the reflection series is band-limited to obtain higher resolution seismic data. The limits of bandwidth enhancement is selected by correlating the enhanced seismic to well logs and VSPs.

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Section: Application To Seismic Datamentioning

confidence: 99%

Frequency bandwidth enhancement using 3D layered reflectivity inversion

Walia¹,

Sandanayake²,

Koop³

et al. 2004

SEG Technical Program Expanded Abstracts 2004

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“…While the positions of major reflectors can generally be picked out in migrated, post-stack seismic sections, it is hard to identify the layer lithologies without knowing the rock properties in those regions. Detailed stratigraphic interpretation of a seismic section usually involves its inversion to an acoustic impedance section (Gluck et al, 1997). Acoustic impedance, being a reservoir parameter, can be related more easily to rock property measurements obtained through well logging.…”

Section: Introductionmentioning

confidence: 99%

Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness

Mustafa¹,

Alfarraj²,

AlRegib³

2021

Preprint

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“…Another major step in exploiting seismic in formation is related to the interpretation of seis mic amplitudes, their variation with offset (AVO) (Ostrander, 1984;Rutherford and Williams, 1989;Hilterman, 1990;Castagna and Smith, 1994;Ursin and Ekren, 1995) and the transformation of seismic data into elastic medium properties via seismic inversion methods (Cooke and Schneider, 1983;Lailly, 1983;Tarantola and Valette, 1982; van Riel and Berkhout, 1985;Hampson, 1991). Initially with AVO (Snyder and Wrolstad, 1992;Hall et al, 1995) and later with the development of seismic inversion (HellandHansen et al,1997;Gluck et al, 1997;Ma, 2002), reflection ampli tudes provide insight into strata lithology, poros ity, and fluid content by means of rock physics models. In addition, empirical statistical meth ods are used to successfully relate the seismic data, and a large collection of possible derived at tributes (e.g., Brown, 2001;Chopra and Marfurt, 2005), with the reservoir properties.…”

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confidence: 99%

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

Bosch

Mukerji²,

González

2014

Encyclopedia of Exploration Geophysics

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seismic inversion and rock physics models. Furthermore well logs allow for local validation of the estimations and constraint for the struc ture and properties. Combining the spatially dis tributed information provided by seismic surveys with the localized information obtained from well logging tools requires handling disparate property support scales and use of spatial statisti cal models. Most common models are based on the spatial homogeneity of the mean (one point statistics) and the spatial covariance (two point statistics). Cokriging estimation and multivariate Gaussian simulation are examples of these meth ods, which are very useful in the spatial combina tion of information. In addition, methods based on multipoint statistics are used to model com plex morphology. The integration of the overall data, information, geophysical, and geological knowledge into a reliable reservoir model is elab orated via diverse workflows. A basic sequence includes seismic interpretation, seismic inver sion, reservoir characterization, geostatistical es timation, geostatistical simulation, and dynamic fluid flow modeling. It is common to cycle within the sequence to update the model as additional information about the reservoir arrives. Some workflows proceed in a stepwise manner, while others, with increasing computational require ments, employ joint inversion formulations where multiproperty models are estimated to jointly satisfy the complete set of available data.

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High‐resolution impedance layering through 3-D stratigraphic inversion of poststack seismic data

Cited by 28 publications

References 0 publications

Frequency bandwidth enhancement using 3D layered reflectivity inversion

Frequency bandwidth enhancement using 3D layered reflectivity inversion

Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

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