An algorithm for calculation of bed thickness and reflection coefficients from amplitude spectrum

Puryear, Charles; Castagna, John P.

doi:10.1190/1.2369866

Cited by 16 publications

(5 citation statements)

References 3 publications

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“…From Chung and Lawton [11], the tuning thickness of the seismic is 15.6 ms located near trace 13 of the seismic section. It is showed that, in term of positioning the reflector, m-m attribute displays fairly similar layer thickness to which displayed by spectral inversion [7,8].…”

Section: M-m Attributementioning

confidence: 89%

“…The improvement of seismic vertical resolution has also been tried in frequency domain [5][6][7]. Portniaguine and Castagna [8] inverted the reflectivity of thin bed by spectral inversion, a form of sparse-spike inversion in that it outputs a sparse reflectivity series.…”

Section: Introductionmentioning

confidence: 99%

“…In 2006, Puryear and Castagna [7] derived an algorithm for calculating a bed thickness from amplitude spectrum of the seismic. The algorithm was implemented for both modeled and real data.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Minimum-Maximum (m-m) Attribute and Zero-INTENS-Difference (z-i-d) Attribute for Estimating Seismically Thin-Bed Thickness

Purnomo

Harith

2011

itbj.eng.sci.

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Abstract. This paper demonstrates a new alternative way in estimating seismically thin-bed (below-tuning) thickness. Initial thickness is built by bandpass filtering the amplitude display of a zero-phase seismic. The filter removes the non minimum and or non maximum and left the maximum and or the minimum of seismic amplitude. The unresolved below-tuning thickness is then corrected by zero-INTENS-difference (z-i-d) attribute. INTENS is integrated energy spectra, an attribute which can be derived from spectral analysis. z-i-d attribute is zero difference of INTENS between the seismic and its synthetic. The method generates INTENS difference profile by subtracting seismic INTENS and its synthetic INTENS iteratively. The iteration is controlled by dipole space shifting from distance to closer or vice versa. The true thickness is derived by locating z-i-d which laid in INTENS different profile. It has found that, for free noise true seismic and perfect-wavelet (a wavelet which only approximately similar with wavelet which constructing the true seismic) synthetic seismic, in INTENS different profile, the z-i-d location always corresponds to true dipole space or thickness. The method could resolve all thickness of a wedge-modeled seismic with three different dominant frequencies. When the synthetic seismic is constructed with imperfect wavelet, slightly different analysis is needed to locate z-i-d attribute and the result is not as perfect as when perfect wavelet constructing synthetic seismic. A quiet similar result is got when the method is implemented for noisy wedge-modeled seismic. Bad thickness estimation is resulted for 20% noise seismic. The method algorithm is extended for similar dipole polarity model and multilayer model to bring the method to real seismic data nearer. The extension is done by estimating thickness of every layer of a stacked-wedge-modeled seismic. The algorithm then generalized for estimating layers thickness with several thickness combinations. The method was able to delineate shallow channel of Stratton Field by providing good pseudo-acousticimpedance (pseudo AI) map.

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Section: M-m Attributementioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Combination of Minimum-Maximum (m-m) Attribute and Zero-INTENS-Difference (z-i-d) Attribute for Estimating Seismically Thin-Bed Thickness

Purnomo

Harith

2011

itbj.eng.sci.

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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: 97%

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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“…However, with the rapid development of oil exploration and development, people find that some of the thin layers might be important oil bearing reservoirs and have high oil production capacity. Aiming to solve the above issue, spectral inversion has been proposed [1] [2] [3]. As described by Puryear and Castagna (2008) [4], in order to get a wide-band sparse-reflectivity cube, spectral inversion utilizes spectral decomposition to unravel the complex interference patterns created by thin-bed Spectral inversion is a novel way of removing the wavelet form seismic data.…”

Section: Introductionmentioning

confidence: 99%

An Effective Thin Reservoir Identification Method for Fine Oilfield Development

Lv¹,

Wang²,

Shi³

et al. 2023

ENG

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Thin reservoirs prediction method such as spectral inversion has drawn considerable attention in recent years. In order to avoid extracting wavelets within the whole field area purposeless and to make the filtered data has preferable fidelity as well as signal-to-noise ratio, an effective structural constrained thin reservoir description method which combines spectral inversion and wide-band Ricker wavelet filtering technology has been proposed in this paper. The method given here is more credible and is suitable for the prediction of middle-deep thin reservoirs. We take LD-A structure within Bohai Bay Basin as an example to show the implement of our method. Several sets of thin sand layers which are hardly to recognize originally have been finally identified. Also, with the application of this method, a high-production thin reservoir of LD-B structure has been identified accurately, which provides credible information for subsequent fine oil exploration and development.

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An algorithm for calculation of bed thickness and reflection coefficients from amplitude spectrum

Cited by 16 publications

References 3 publications

Combination of Minimum-Maximum (m-m) Attribute and Zero-INTENS-Difference (z-i-d) Attribute for Estimating Seismically Thin-Bed Thickness

Combination of Minimum-Maximum (m-m) Attribute and Zero-INTENS-Difference (z-i-d) Attribute for Estimating Seismically Thin-Bed Thickness

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

An Effective Thin Reservoir Identification Method for Fine Oilfield Development

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