Phase stability via nonlinear optimization: A case study

Misra, Somanath; Chopra, Satinder

doi:10.1190/1.3517304

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…From the perspective of wavelet extraction, the traditional methods include spectrum correlation (e.g. Bednar, 1983; Misra & Chopra, 2011; Otis & Smith, 1997; Rosa & Ulrych, 1991; Sacchi & Ulrych, 2000; Walden & White, 1998; White & O'Brien, 1974) and well‐log calibration (e.g. Danielsen & Karlsson, 1984; Lines & Treitel, 1985), but their estimations are time‐invariant and thus insufficient to accommodate the nonstationary seismic data.…”

Section: Introductionmentioning

confidence: 99%

Automating seismic‐well tie via self‐supervised learning

Abubakar

2023

Geophysical Prospecting

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As an essential process in subsurface interpretation, seismic-well tie aims at calibrating the well measurements in depth domain with the seismic records in time domain for reliable reservoir mapping and modelling. Such a process usually requires massive manual efforts, primarily extracting source wavelets to synthesize seismograms from well measurements and stretching/squeezing synthetic seismograms to match actual seismic signals, and thus becomes time-consuming and labour intensive with the amount of wells expanding in a field. This paper formulates the seismic-well tie problem from the perspective of computer vision and, considering the lack of manually prepared training labels in most of real projects, proposes automating it by a self-supervised workflow of two key components. The first component is to extract time-variant wavelets in the target interval using a dual-task autoencoder, which is optimized by maximizing the spectrum similarity between the extracted wavelet and the actual seismic. The second component is to estimate the corresponding time-shift between a pair of the synthetic seismogram and the actual seismic using a flow net, which is trained by a pseudo dataset derived from the actual well measurements. More specifically, the data generation consists with defining one-dimensional time-shift curves to warp the original less accurate time-depth relationships and then synthesizing the corresponding seismograms based on the convolutional model at all available wells. When turning to the stage of inference at a target well, the proposed workflow automatically extracts a representative wavelet, generates the corresponding synthetic seismogram from the original timedepth relationship and estimates the time-shift curve necessary for revising the original time-depth relationship that would lead to an optimal tying with the actual seismic at the well. The proposed workflow is tested on two field datasets, and both results demonstrate improved tying over traditional methods such as statistical wavelet extraction and dynamic time warping.

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Section: Introductionmentioning

confidence: 99%

Automating seismic‐well tie via self‐supervised learning

Abubakar

2023

Geophysical Prospecting

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“…In the last seven decades, several papers on deconvolution and wavelet estimation have been published in Geophysics literature. Most often the authors focus on and try to solve problems related to the wavelet phase character (Clarke, 1968;Eisner and Hampson, 1990;Ulrych and Treitel, 1991;Lazear, 1993;Leinbach, 1995;Ursin et al, 1996;Ursin, 1998, 2000;Ursin and Porsani, 2000;Sacchi and Ulrych, 2000;Misra and Sacchi, 2007;Lü and Wang, 2007;van der Baan, 2008;Misra and Chopra, 2010;Ledesma and Porsani, 2013) The Wiener spiking deconvolution filter has been developed and applied for seismic data processing (Robinson, 1957;Robinson and Treitel, 1980;Leinbach, 1995;Yilmaz, 2001), with the assumptions that the reflectivity series have the statistical properties of random white noise and the wavelet is minimum-phase. The ability of this filter to compress the seismic wavelet in time, despite these questionable assumptions, is responsible for the popularity of the Wiener spiking deconvolution technique in the petroleum industry.…”

Section: Introductionmentioning

confidence: 99%

“…Ledesma and Porsani (2013) uses the roots of these polynomials to obtain an optimum inverse filter via genetic algorithm. Misra and Sacchi (2007) and Misra and Chopra (2010) deconvolve the data with a standard spiking deconvolution filter. From the filtered, whitened data, they estimate an all-pass phase filter, which is then applied to the whitened data.…”

Section: Introductionmentioning

confidence: 99%

Improving seismograms’ resolution by using a phase-shrinkage filtering method

Porsani¹

2022

Braz J Geophys

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The improvement in the temporal resolution of seismograms is usually achieved by compressing, or deconvolving, the seismic pulse. In this paper, I present a new method of filtering time series that shrinks the positive or negative polarity band of a signal while expanding its opposite polarity. The filtering method uses the complex seismic trace to compute the instantaneous phase, which is shrunk around the positions of the local maxima (or minima) values corresponding to the reflections of the original signal. It calculates the real and imaginary components afterward, thus representing the filtered signal. The method is applied trace-by-trace and promotes a significant improvement in the temporal resolution of the seismograms, revealing in greater detail the reflections and structures of the subsurface. Numerical examples with stacked seismic - 2D from the Pelotas Basin, from the mouth of the Amazon river - and, 3D - from F3Demo, illustrate the performance of the new method proposed here. The average amplitude spectra of the filtered data reveal the presence of the high and low frequency contents in the filtered results.

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“…The ltered seismic trace is then an estimate of the reectivity series. Misra and Sacchi (2007) and Misra and Chopra (2010) deconvolve the data with a standard spiking deconvolution lter. From the ltered, whitened data they estimate an allpass phase lter which is then applied to the whitened data.…”

Section: Introductionmentioning

confidence: 99%

Reflectivity estimation using minimum-delay seismic trace decoposition

Porsani¹,

Silva²

2012

Proceedings of the 5 Simpósio Brasileiro De Geofísica

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Copyright 2012, SBGf -Sociedade Brasileira de Geofísica Este texto foi preparado para a apresentação no V Simpósio Brasileiro de Geofísica, Salvador, 27 a 29 de novembro de 2012. Seu conteúdo foi revisado pelo Comitê Técnico do V SimBGf, mas não necessariamente representa a opinião da SBGf ou de seus associados. É proibida a reprodução total ou parcial deste material para propósitos comerciais sem prévia autorização da SBGf.

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Phase stability via nonlinear optimization: A case study

Cited by 7 publications

References 5 publications

Automating seismic‐well tie via self‐supervised learning

Automating seismic‐well tie via self‐supervised learning

Improving seismograms’ resolution by using a phase-shrinkage filtering method

Reflectivity estimation using minimum-delay seismic trace decoposition

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