Constrained least-squares spectral analysis: Application to seismic data

Puryear, Charles; Portniaguine, Oleg; Cobos, C.; Castagna, John P.

doi:10.1190/geo2011-0210.1

Cited by 85 publications

(23 citation statements)

References 20 publications

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“…Changes of the thickness of the thin reservoir and variability of the stratigraphic interface characteristics and filling of pore fluid particularly the gas reservoirs all may generate corresponding response in different frequency volumes. Some frequency volumes within particular frequency bands have priority to highlight and maximize geophysical responses (Puryear et al, 2012). Choosing the right time-frequency analysis method is critical to achieve instantaneous spectral analysis of the seismic signals.…”

Section: Instantaneous Spectral Analysis Based On Cwtmentioning

confidence: 99%

Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis

Xue

Cao

Tian

et al. 2014

Journal of Petroleum Science and Engineering

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Section: Instantaneous Spectral Analysis Based On Cwtmentioning

confidence: 99%

Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis

Xue

Cao

Tian

et al. 2014

Journal of Petroleum Science and Engineering

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“…Information from high-resolution spectral decomposition, (Puryear et al, 2012) of the seismic data is also used during the inversion process. Thin beds below tuning thickness can be imaged by inverting the frequency spectra for layer thickness using complex spectral analysis.…”

Section: Sparse-layer Inversion Using Basis Pursuit Decompositionmentioning

confidence: 99%

Increasing the effective bandwidth of seismic data through sparse-layer inversion: A case study from Mangala field, Rajasthan, India

Somasundaram¹,

Chacko²,

Ravichandran³

et al. 2015

The Leading Edge

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Mangala field is in the northern part of the onshore Barmer Basin in India. The primary reservoir in the field is the Fatehgarh Formation, deposited in the rifting phase that created the Barmer Basin during the Late Cretaceous to early Paleocene period. The majority of reservoir oil is contained within the upper FM1 member of the Fatehgarh Formation, composed of single-story and multistory stacked, meandering-channel sands. These sands vary in thickness from 3 to 7 m, with net to gross ranging from 18% to 78%. For such a heterogeneous fluvial system, correlation of floodplain shales and fluvial sands poses a major challenge for reservoir characterization when based on well data alone. Conventional 3D seismic data do not resolve the thin FM1 sands. For more accurate reservoir modeling, various techniques, which included sparse-spike inversion and spectral decomposition, were attempted with limited success. Sparse-layer reflectivity inversion performed on 3D stack PSTM seismic data, however, resulted in improved detectability and resolution and has provided a greater understanding of the lateral continuity of these thin fluvial reservoir units. The 7-to 50-Hz bandwidth of the input seismic data increased to 7 to 100 Hz by the inversion process. The improved imaging of channel geometries has enabled geobody mapping and their input into a revised geologic model.

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“…To provide layer information finer than conventional resolution, a constrained least-squares spectral analysis (CLSSA) was used to spectrally decompose the conditioned seismic (Puryear et al, 2012) and applied as a constraint to sparse layer inversion.…”

Section: Figure 1 the Above Images Show A Vertical Section Comparing mentioning

confidence: 99%

Highly Detailed Reservoir Imaging by Using Sparse Layer Inversion in a Complex N.Sea Turbidite

Mann¹,

Eykenhof²,

Castagna³

et al. 2014

Proceedings

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SUMMARYSparse layer inversion (SLI), like sparse spike inversion (SSI), invokes a sparse reflectivity solution for the reconstruction of noisy seismic traces in the presence of a known, band-limited wavelet. However, solving for layers, i.e. dipoles, rather than individual interfaces, holds the potential for achieving increased detail and lateral stability over that usually achieved with SSI. In this paper, we present the method and show the application of SLI to a complex turbidite reservoir in the UK Central North Sea. IntroductionWhen inverting the seismic convolution model to estimate broadband reflectivity, a sparse representation of the number of interfaces is a common choice. Sparseness leads to increased temporal detail and sharp interface definition. However, given the performance of such sparse spike inversion (SSI) methods with regard to noise and layer continuity, an alternative means for achieving bandwidth extension is worth considering. In this paper we discuss sparse layer inversion (SLI) (Zhang and Castagna, 2011;Zhang et al., 2013), which aims to resolve subtle seismic events below tuning thickness. This method was applied to seismic data from a Paleocene discovery in the UK Central North Sea, focusing on the complex thin bed interference pattern affecting the Mey sandstone reservoir. The objective was to 'deblur' the seismic image sufficiently to enable improved stratigraphic interpretation. An additional challenge was the variable frequency-dependent amplitude loss over the centre of the structure due to overburden gas. This was addressed prior to SLI. Results were compared to a conventional SSI reflectivity inversion. In addition, the SLI results were tested for their suitability as input to acoustic impedance inversion.

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Constrained least-squares spectral analysis: Application to seismic data

Cited by 85 publications

References 20 publications

Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis

Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis

Increasing the effective bandwidth of seismic data through sparse-layer inversion: A case study from Mangala field, Rajasthan, India

Highly Detailed Reservoir Imaging by Using Sparse Layer Inversion in a Complex N.Sea Turbidite

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