Geofluid Discrimination Incorporating Poroelasticity and Seismic Reflection Inversion

2016

Geofluids

Seismic inversion with prestack seismic data such as amplitude variation with offsets (AVO) inversion is an important tool in the estimation of elastic parameters for predicting lithology and discriminating fluid in conventional or unconventional hydrocarbon reservoirs. The product of Young's modulus and density (Young's impedance, YI) and the product of Poisson ratio and density (Poisson ratio impedance, PI) show great potential in lithology prediction and fluid discrimination of unconventional resources such as shale gas or oil. The high quality requirements for prestack data in density inversion render the estimation of YI and PI arduous and inaccurate with a conventional prestack inversion approach. In this study, a direct AVO inversion approach is proposed to estimate YI, PI, and density directly from P‐wave seismic data. The linearized P‐wave reflectivity approximate equation in terms of YI, PI, and density is initially derived. Five models, including four typical AVO classes, are utilized to verify the accuracy of the derived linearized P‐wave reflectivity equation in comparison with the exact P‐wave reflectivity equation and the frequently used linearized reflectivity approximate equation involving P‐ and S‐wave velocities and density. Parameter sensitivity analysis illustrates that YI and PI can reasonably be estimated from P‐wave reflectivity if a decorrelation scheme is utilized in the inversion algorithm. In addition, a pragmatic AVO inversion using a Bayesian scheme is suggested for the direct inversion of YI and PI from prestack seismic data. Synthetic and field data examples demonstrate the feasibility of the proposed inversion approach in the estimation of YI and PI and show the potential of this approach in fluid discrimination.

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“…Details about this inversion approach are given by Zong et al . (). Here, we present only the necessary theory for the inversion of YI and PI.…”

Section: Bayesian Ava Direct Inversion For Yi and Pimentioning

confidence: 97%

Section: P‐wave Reflectivity In Terms Of Pi and Yimentioning

confidence: 99%

See 1 more Smart Citation

Direct inversion of Young's and Poisson impedances for fluid discrimination

2016

Geofluids

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“…So we introduced the Cauchy prior function p Cauchy ðmÞ to our Laplace mixed-domain inversion in this paper. The explicit expression of the Cauchy function can be stated as (Downton 2005;Yang 2008;Alemie and Sacchi 2011;Zong et al 2015;Li et al 2016a),…”

Section: Inversion Strategy Based On the Bayesian Systemmentioning

confidence: 99%

“…Due to the deficiency of field data, many regularization terms are applied to decrease the problem about the lack of low-and high-frequency components, such as prior information on model parameters, bounding and geological constraints (Tarantola 2005;Kim and Kim 2011;Zhang et al 2013;Yuan and Wang 2013;Hamid and Pidlisecky 2015;Yin et al 2013Yin et al , 2015bZong et al 2015;Liu et al 2016;Li et al 2016a). The above-mentioned methods only account for overcoming multiple solutions and avoiding unrealistic values of the local minima in the objective function.…”

Section: Introductionmentioning

confidence: 99%

Bayesian seismic multi-scale inversion in complex Laplace mixed domains

2017

Pet. Sci.

Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion in the pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore, one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.

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Model Parameterization and P-wave AVA Direct Inversion for Young’s Impedance

2017

Pure Appl. Geophys.