Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction
Abstract
Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.
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BibTeX
@misc{wang2026seismic,
title = {{Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction}},
author = {Shengyi Wang and Xuehua Chen and Xingyu Luo and Kaixing Huang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05922},
eprint = {2609.05922},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05922},
}
RIS
TY - UNPB
TI - Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction
AU - Shengyi Wang
AU - Xuehua Chen
AU - Xingyu Luo
AU - Kaixing Huang
PY - 2026
DA - 2026/09/05
PB - arXiv
DO - 10.48550/arxiv.2609.05922
UR - https://arxiv.org/abs/2609.05922
AB - Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.
ER -