Discriminating Gas and Water Using Multi‐Angle Extended Elastic Impedance Inversion in Carbonate Reservoirs

Peng, Zhenming; Li, Yalin; Wu, Shenghong; He, Zhenhua; Zhou, Ye

doi:10.1002/cjg2.1253

Cited by 15 publications

(3 citation statements)

References 9 publications

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“…Bayesian inference makes use of prior information from the known rock properties. It provides an approach to improve the resolution of the inversion result [11], thus Bayesian inference is widely adopted in seismic inversion such as pro-stack inversion [3,12,13] and pre-stack inversion [14][15][16]. In AI inversion, the Bayesian AI forward model was proposed.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

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The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

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Section: Introductionmentioning

confidence: 99%

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

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“…Carbonate reservoir is widely developed in China Tarim basin, where a large number of oil fields are discovered in the paleo-karsts Ordovician limestone reservoir (Peng et al 2008;Sun et al 2011). The storage spaces for the carbonate reservoir in this area are mostly secondary dissolution caves and characterized by strong heterogeneity (Zhang et al 2008;Zeng et al 2011;Yang et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Wave Equation Inversion and Its Application on the Heterogeneous Reservoir Model Data

Tang

2016

ESR

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Seismic full waveform inversion seeks to make use of the full information based on full wave field modeling to extract quantitative information from seismograms. Its serious nonlinearity and high dependence on initial velocity model often results in unsatisfactory inversion results in paleo-karsts carbonate reservoir characterized by strong heterogeneity. The paper presents an improved strategy of multi-scale inversion to establish velocity field model of waveform tomography. the forward wave equation algorithm was derived in frequency domain, and then the Matrix formalism for the iterative inverse methods is derived by gradient methods to speed up calculation and to avoid convergence to local minimum value. After massive amount of frequencies tests, the appropriate bandwidth are extracted, and the velocity field calculated at low frequency is used as the input of the high frequency. After the iteration, the accurate velocity field is inverted. Finally, frequency domain wave equation full waveform inversion in mathematical and physical models is conducted in order to verify the inverse program. The method of selecting the inverse frequencies is proved to be effective.

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“…Additionally, Martins (2006) and Cui et al (2010) introduced P-and P-SV wave elastic impedance in weakly anisotropic media, respectively. In recent years, fluid indicators estimated from seismic data play important roles in reservoir characterization and prospect identification, so many methods, such as elastic impedance inversion, have been introduced to extract a variety of fluid factors directly to avoid the cumulative error generated by indirect combination of parameters in the process of reservoir fluid identification (Ma 2003;Peng et al 2008;Zong et al 2011Zong et al , 2012Yin et al 2013b;Zhang et al 2013;Chen et al 2014a, b;Li et al 2014). Goodway et al (1997) proposed kq and lq as fluid indicators.…”

Section: Introductionmentioning

confidence: 99%

Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

Zhang

Pan

et al. 2015

Pet. Sci.

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Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo (MCMC) method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient (PCG) algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that efficiency and stability of inversion are both taken into consideration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.

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Discriminating Gas and Water Using Multi‐Angle Extended Elastic Impedance Inversion in Carbonate Reservoirs

Cited by 15 publications

References 9 publications

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Frequency Domain Wave Equation Inversion and Its Application on the Heterogeneous Reservoir Model Data

Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

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