Fluid discrimination study from Fluid Elastic Impedance (FEI)

Zhang, Shixin; Yin, Xingyao; Zhang, Fanchang

doi:10.1190/1.3255350

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…sharp contacts, micro-faults, and fractures), which is mainly due to the lack of sparsity in model coefficients. To cope with this problem, edge-preserving regularization (EPR) techniques have been proven to be an effective alternative solution (Zhang et al 2009;Gholami and Siahkoohi 2010;Pérez, Velis and Sacchi 2013;Gholami, Aghamiry and Abbasi 2018). EPR approaches employ sparse constraints and can generate stable and non-smooth results at boundaries.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian linearized amplitude‐versus‐angle inversion

Mollajan

Memarian

Quintal

2019

Geophysical Prospecting

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The technique of seismic amplitude‐versus‐angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude‐versus‐angle inversion problem is intrinsically ill‐posed and generally stabilized by the use of L2‐norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude‐versus‐angle inversion problem to preserve the sharp geological interfaces. In this regard, a priori constraint term with two regularization functions is presented: the sparse constraint regularization and the low‐frequency model information. In addition, to obtain high‐resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude‐versus‐angle inversion.

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Section: Introductionmentioning

confidence: 99%

Sparse Bayesian linearized amplitude‐versus‐angle inversion

Mollajan

Memarian

Quintal

2019

Geophysical Prospecting

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“…Zhang et al . (, ) extended this concept to estimation of elastic parameters from elastic impedances with different approaches. To enhance the accuracy of EI in the prediction of parameters, Zong et al .…”

Section: Introductionmentioning

confidence: 99%

“…Elastic impedance was initially proposed by Connolly (1999). Zhang et al (2009Zhang et al ( , 2012 extended this concept to estimation of elastic parameters from elastic impedances with different approaches. To enhance the accuracy of EI in the prediction of parameters, Zong et al (2012b) extended it to an EVA inversion scheme.…”

Section: Introductionmentioning

confidence: 99%

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

Zong

Yin

2016

Geofluids

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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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“…The density is difficult to invert, which will have a deleterious influence on the estimation of Young's modulus. Parameters estimated indirectly will bring in more uncertainty in the inversion results (Zhang et al 2009). In order to estimate the Young's modulus (Y), Poisson's ratio (P) and density (D) directly, Zong et al (2012b) derived the linear approximation equation based on Young's modulus, Poisson's ratio, and density and inverted the elastic parameters by Bayesian framework via Cauchy distribution as prior information.…”

Section: Introductionmentioning

confidence: 99%

“…Theune et al (2010) investigated the Cauchy and Laplace statistical distributions for their potential to recover sharp boundaries between adjacent layers. Based on the reflection dipole decomposition described by Chopra et al (2006), Zhang et al (2009Zhang et al ( , 2011 studied the basis pursuit inversion (BPI) of post and pre-stack seismic data, respectively, and got the sparse reflection coefficients and blocky layer elastic parameters, which is a high-resolution inversion method. Pérez et al (2013) proposed a hybrid Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) least-squares strategy that inverts the location of reflection by the FISTA algorithm (Beck and Teboulle 2009) first and then reevaluates the sparse (high resolution) reflection coefficients.…”

Section: Introductionmentioning

confidence: 99%

Pre-stack basis pursuit seismic inversion for brittleness of shale

2015

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Brittleness of rock plays a significant role in exploration and development of shale gas reservoirs. Young's modulus and Poisson's ratio are the key parameters for evaluating the rock brittleness in shale gas exploration because their combination relationship can quantitatively characterize the rock brittleness. The highvalue anomaly of Young's modulus and the low-value anomaly of Poisson's ratio represent high brittleness of shale. The technique of pre-stack amplitude variation with angle inversion allows geoscientists to estimate Young's modulus and Poisson's ratio from seismic data. A model constrained basis pursuit inversion method is proposed for stably estimating Young's modulus and Poisson's ratio. Test results of synthetic gather data show that Young's modulus and Poisson's ratio can be estimated reasonably. With the novel method, the inverted Young's modulus and Poisson's ratio of real field data focus the layer boundaries better, which is helpful for us to evaluate the brittleness of shale gas reservoirs. The results of brittleness evaluation show a good agreement with the results of well interpretation.

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Fluid discrimination study from Fluid Elastic Impedance (FEI)

Cited by 12 publications

References 9 publications

Sparse Bayesian linearized amplitude‐versus‐angle inversion

Sparse Bayesian linearized amplitude‐versus‐angle inversion

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

Pre-stack basis pursuit seismic inversion for brittleness of shale

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