The feasibility and value of low-frequency data collected using colocated 2-Hz and 10-Hz geophones

Chiu, Stephen K.; Eick, Peter M.; Howell, Jack; Malloy, Jeff

doi:10.1190/tle32111366.1

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…More recently, additional geophysical technol ogies are extending their application to reservoir exploration and description. This includes: elec tromagnetic surveys (MacGregor and Tomlinson, 2014), microseismicity (Goodway, 2012;Grechka et al, 2015), multicomponent seismic recording (Caldwell, 1999;Gaiser et al, 2001), broader fre quency band seismic acquisition (Moldoveanu et al, 2007;Chiu et al, 2013;Vassallo et al, 2013), increased spatial density of seismic receiv ers (Ramsdem et al, 2005;Winter et al, 2014), Downloaded 07/08/17 to 186.88.151.214. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ permanent instrumentation of oil fields for short er timelapse monitoring (Ebrom et al, 2000;Lumley, 2001), and seismic tomography based on receiver interferometry Schuster, 2009;Curtis et al, 2006;Grechka and Zhao, 2012).…”

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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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mentioning

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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Full-waveform inversion of conventional Vibroseis data recorded along a regional profile from southeast Poland

2015

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Deep-Learning-Based Low-Frequency Reconstruction in Full-Waveform Inversion

Chai

Yang

2023

Remote Sensing

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Low frequencies are vital for full-waveform inversion (FWI) to retrieve long-scale features and reliable subsurface properties from seismic data. Unfortunately, low frequencies are missing because of limitations in seismic acquisition steps. Furthermore, there is no explicit expression for transforming high frequencies into low frequencies. Therefore, low-frequency reconstruction (LFR) is imperative. Recently developed deep-learning (DL)-based LFR methods are based on either 1D or 2D convolutional neural networks (CNNs), which cannot take full advantage of the information contained in 3D prestack seismic data. Therefore, we present a DL-based LFR approach in which high frequencies are transformed into low frequencies by training an approximately symmetric encoding-decoding-type bridge-shaped 3D CNN. Our motivation is that the 3D CNN can naturally exploit more information that can be effectively used to improve the LFR result. We designed a Hanning-based window for suppressing the Gibbs effect associated with the hard splitting of the low- and high-frequency data. We report the significance of the convolutional kernel size on the training stage convergence rate and the performance of CNN’s generalization ability. CNN with reasonably large kernel sizes has a large receptive field and is beneficial to long-wavelength LFR. Experiments indicate that our approach can accurately reconstruct low frequencies from bandlimited high frequencies. The results of 3D CNN are distinctly superior to those of 2D CNN in terms of precision and highly relevant low-frequency energy. FWI on synthetic data indicates that the DL-predicted low frequencies nearly resemble those of actual low frequencies, and the DL-predicted low frequencies are accurate enough to mitigate the FWI’s cycle-skipping problems. Codes and data of this work are shared via a public repository.

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The feasibility and value of low-frequency data collected using colocated 2-Hz and 10-Hz geophones

Cited by 3 publications

References 6 publications

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

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

Full-waveform inversion of conventional Vibroseis data recorded along a regional profile from southeast Poland

Deep-Learning-Based Low-Frequency Reconstruction in Full-Waveform Inversion

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