Broadband seismic amplitude variation with offset inversion

Abstract

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

Seismic AVO statistical inversion incorporating poroelasticity

“…̃ ( ) is the mapping relationship between the AVO reflection coefficients (Li et al 2017b). Since seismic inversion in complex frequency domain has the advantage of low-frequency recovery, the AVO forward model in the complex frequency domain is introduced as (Zong et al 2017;Li et al 2017a, b), where + i is complex frequency. + i , i is the complex spectrum of seismic AVO data.…”

“…For seismic AVO inversion algorithms in different domains (Buland 2003;Yuan et al 2015;Yin et al 2013Yin et al , 2016Li et al 2017a, b;Zong et al 2017), the timecomplex frequency-domain AVO inversion is helpful to consider the attenuation effects during the propagation of seismic waves and improve inversion resolution. Li et al (2017b) deduced the seismic AVO forward operator in the complex frequency domain and estimated the low-frequency information of the model parameters with AVO stepwise inversion in complex frequency domain.…”

“…Li et al (2017b) deduced the seismic AVO forward operator in the complex frequency domain and estimated the low-frequency information of the model parameters with AVO stepwise inversion in complex frequency domain. Zong et al (2017) studied the influence of attenuation coefficients and frequency components in complex frequency-domain AVO inversion on mining the low-frequency information of model parameters. Since the high-frequency information contained in well-logging data is utilized in statistical inversion incorporating statistical learning theory, the resolution and identification of thin layers can be improved effectively (Eidsvik et al 2004;Braak 2006;Bosch et al 2007;Grana and Della Rossa 2010;Figueiredo et al 2018).…”

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

“…Elastic parameters are commonly obtained with seismic inversion, which uses elastic information contained in seismic waves. Common inversion methods include model-based (Cooke and Schneider, 1983;Russell and Hampson, 1991;Kumar et al, 2016) [39][40][41], prestack elastic (Aki and Richards, 1980;Zong et al, 2012Zong et al, , 2017Lu et al, 2015Lu et al, , 2018 [42][43][44][45][46], nonlinear (neural networks and support vector machines) (Liu and Liu, 1998;Torresa et al, 2013;Cheng and Fu, 2022) [47][48][49], and geological statistical (Wu et al, 2008;Giroud et al, 2017;Mosser et al, 2020) [50][51][52] inversion. Model-based inversion has low lateral resolution and is unsuitable for reservoirs with rapid lateral changes (Yao and Gan, 2000) [53].…”

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

“…Prestack AVO/AVA (amplitude variation with offset/angle) seismic inversion aims to quantitatively explore the information of subsurface elastic (e.g., longitudinal and transverse wave velocities, density, anisotropy, etc.) [1][2][3][4][5][6][7][8] or petrophysical parameters (e.g., porosity, fluid saturation, mineral content, etc.) [9][10][11][12][13] and then provides a database for geofluid identification, lithology classification, and reservoir prediction [14].…”