Adaptive Groundroll Filtering

Meur, D. Le; Benjamin, N.; Cole, Randy; Harthy, M. Al

doi:10.3997/2214-4609.20147745

Cited by 18 publications

(7 citation statements)

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“…First, conventional preprocessing is applied to separate surface waves from the recorded data (Le Meur et al, 2008) using the raw input data ( Figure 5). This preprocessing is aimed at partially attenuating any spatial aliasing and enhancing the linearity and signal-to-noise ratio of the Rayleigh waves.…”

Section: Surface-wave Workflowmentioning

confidence: 99%

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

et al. 2016

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We propose an innovative workflow based on the complementary use of Rayleigh waves alongside standard P-wave refraction tomography, which better depicts the shallow part of the near-surface P-wave velocity model. Our surface-wave processing sequence led to an S-wave near-surface velocity field that can be used as a constraint for P-wave tomography and can improve P-wave statics determination. Rayleigh waves are processed in three steps. The first step consists of an accurate frequency-dependent traveltime measurement for each selected source-receiver pair in which the phase difference between two adjacent traces is used to derive the phase velocity. Then, a frequency-dependent surface-wave velocity tomography is performed from the picked traveltimes. Finally, after surface-wave tomography, the frequency-dependent phase velocity volume output by the tomography is inverted to deliver an S-wave near-surface velocity model. This model is used to constrain the first-arrival P-wave tomography. To illustrate our method, we use a 3D narrow-azimuth land data set, acquired along a river valley. Strong lateral velocity variations exist in the shallow part, with slow velocities around the unconsolidated sediments of the riverbed and faster velocities in the consolidated sediments of the surrounding hills. A combined first-arrival tomography using the S-wave velocity model, the initial unconstrained refracted P-wave velocity model, and the original first arrivals is used to obtain a more accurate near-surface P-wave velocity model. This new approach led to a constrained P-wave velocity model from which primary statics are derived and then applied, leading to an improved image with better focusing and continuity of thin layers in the shallowest part.

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Section: Surface-wave Workflowmentioning

confidence: 99%

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

et al. 2016

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“…Because surface and shear wave propagations are not modeled by the acoustic wave equation, they are attenuated using adaptive ground roll attenuation (Le Meur et al, 2008) during field data preparation to compensate for the acoustic-FWI non-elastic limitation. In addition, because sedimentary rocks are not purely elastic media, wavefield energy is partially converted to heat during propagation.…”

Section: Acoustic-fwi Compensationmentioning

confidence: 99%

A practical acoustic full waveform inversion workflow applied to a 3D land dynamite survey

Mei

Tong

2015

SEG Technical Program Expanded Abstracts 2015

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Full waveform inversion (FWI) has been used to successfully build high-resolution earth velocity models for marine data. For land data sets, FWI using acoustic wave equation (acoustic-FWI) remains challenging due to the lack of modeling of strong surface waves, converted waves, phase dispersion, and near-surface weathering layer distortion. In addition, for land dynamite surveys, the source wavelet information is usually missing, and the recorded individual waveform varies greatly because of the spatially variant shot characteristics such as source charge size, shot depth, and hole-pattern as well as the distortion caused by the near-surface weathering layer. We designed and implemented a practical and comprehensive workflow to mitigate missing source wavelet information and vast spatial variation in the waveforms and to address the common challenges of acoustic-FWI application on land surveys during field data preparation.

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“…The processing sequence included adaptive groundroll attenuation to remove surface waves (Le Meur et al, 2008), frequencydependent noise attenuation for removal of spurious industrial/environment noises (Gulunay, 2008), first arrival tomography (Taillandier et al, 2011), surface-consistent compensations that benefit from the latter simultaneous decomposition for deconvolution filters and amplitude scalars (Garceran and Le Meur, 2012), as well as long-wavelength amplitude corrections under well constraints. Moreover, a stochastic approach for surface-consistent residual statics coupled with a customized IT…”

Section: Array-free Processing Testmentioning

confidence: 99%

Processing and Preliminary Interpretation of the Ultra High-Density Full-Azimuth 3D Seismic Survey, Dukhan Field, Qatar

et al. 2014

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The Dukhan Field 3D seismic survey is an ultra high-density wide azimuth survey (i.e., point source and receiver, short source and receiver line interval, and full azimuth recording) intended to provide a significant uplift in quality and resolution to enable the imaging of reservoir sub-layers and fine-scale structural features. This paper presents the challenges and lessons learned from full-field processing and inversion. Seismic interpretation demonstrates added value to the field, which leads to improved reservoir models and support to well operations through improved well placement. Subtle structural, stratigraphic, and diagenetic features are now imaged.Knowledge gained from processing this survey includes: 1) cost, time, and effort invested on the Dukhan 3D survey is worthwhile as high quality results are obtained at 15m bin processing; 2) 3.75m bin processing is now possible, and could be performed to improve image quality over the full seismic bandwidth for all reservoir intervals; 3) Pre-Stack Depth Migration (PSDM) workflow is recommended for optimal offset and azimuth reconciliation and noise attenuation; 4) the adverse effect of near-surface geologic variability on seismic data quality can be improved by utilizing microgravity and resistivity (Vertical Electrical Sounding) techniques; and 5) proper preconditioning of the input data is critical for reliable prediction of reservoir properties away from well control.While industry's knowledge and experience in acquisition and field operation of ultra high-density full-azimuth seismic has increased over the last 3 years, processing of such data to ensure optimal imaging and reliable reservoir property predictions (e.g., porosity) has lagged. QP believes that lessons learned through this round of processing can provide insight for prediction of rock properties in the major Dukhan reservoirs.

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Adaptive Groundroll Filtering

Cited by 18 publications

References 0 publications

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

A practical acoustic full waveform inversion workflow applied to a 3D land dynamite survey

Processing and Preliminary Interpretation of the Ultra High-Density Full-Azimuth 3D Seismic Survey, Dukhan Field, Qatar

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