Rock Physics Driven Joint Inversion to Facies and Reservoir Properties

Kemper, M.; Gunning, James

doi:10.1071/aseg2012ab128

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…To circumvent this issue the estimation process is often solved through a multi-step procedure: first, litho-fluid facies are inferred from the available data (seismic or well log data), then the petrophysical properties are distributed within each facies. Alternatively, over the last years some approaches have been proposed to jointly estimate petrophysical or elastic parameters and litho-fluid facies from the observed data [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

Aleardi¹

2018

Preprint

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We discuss the influence of different statistical models in the prediction of porosity and litho-fluid facies from logged and inverted acoustic impedance (Ip) values. We compare the inversion and classification results that were obtained under three different statistical a-priori assumptions: an analytical Gaussian distribution, an analytical Gaussian-mixture model, and a non-parametric mixtu re distribution. The first model assumes Gaussian distributed porosity and Ip values, thus neglecting their facies-dependent behaviour related to different lithologic and saturation conditions. Differently, the other two statistical models relate each component of the mixture to a specific litho-fluid facies, so that the facies-dependency of porosity and Ip values is taken into account. Blind well tests are used to validate the final predictions, whereas the analysis of the maximum-a-posteriori (MAP) solutions, the coverage ratio, and the contingency analysis tools are used to quantitatively compare the inversion outcomes. This work points out that the correct choice of the statistical petrophysical model could be crucial in reservoir characterization studies. Indeed, for the investigated zone, it turns out that the simple Gaussian model constitutes an oversimplified assumption, while the two mixture models provide more accurate estimates, although the non-parametric one yields slightly superior predictions with respect to the Gaussian-mixture assumption.

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Section: Introductionmentioning

confidence: 99%

Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

Aleardi¹

2018

Preprint

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“…In order to acomplish this objective, a probabilistic inversion from seismic reflection image to rock types (facies) will be studied. This topic dates back to (5), and is still a topic of research (6,7,8,9).…”

Section: Objectives Of This Thesismentioning

confidence: 99%

“…As a consequence the output seismic profile has smaller length than the input elastic and facies profiles, as illustrated in figure 3.4. As demonstrated in section B.5, the seismic conditional probability given the facies profile is computed by: (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) In order to estimate the inverse conditional probability p(π|s) (also known as posterior probability), the Bayes theorem was used:…”

Section: Seismic Inversion To Faciesmentioning

confidence: 99%

“…Equation B-23 defines the signal-to-noise ratio. In order to compute this ratio, one needs an estimate of Σ XX , which comes from the approximate Gaussian covariance matrix Σ * (equation [5][6][7][8][9][10][11][12][13].…”

Section: • Transition Matrixmentioning

confidence: 99%

“…But for multidimensional problems, or complex combinatorial problems, the sampling itself might be hard to be implemented, or even, the probability distribution is known up to an unknown normalizing constant (such as in equation [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In these cases, one uses the technique known as Markov Chain Monte Carlo (MCMC), which instead of sampling independent identically distributed samples of the proposed distribution, uses a Markov Chain, whose stationary distribution is the required distribution.…”

Section: E1 Introductionmentioning

confidence: 99%

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Seismic to Facies Inversion Using Convolved Hidden Markov Model

TALARICO¹

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Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties

et al. 2018

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The joint inversion of seismic data for elastic and petrophysical properties is an inverse problem with a nonunique solution. There are several factors that impact the accuracy of the results, such as the statistical rock-physics relations and observation errors. We have developed a general methodology to incorporate a linearized rock-physics model in a multivariate multimodal prior distribution for Bayesian seismic linearized inversion. The prior distribution is used to define a mixture model for elastic and petrophysical properties and introduce physics-based correlations between the properties. Using the rock-physics prior model and a convolutional seismic forward model in the Bayesian inversion framework, we obtain an analytical expression of the spatially independent conditional distributions to be used as a proposal distribution in a Gibbs sampling algorithm. We then combine the sampling algorithm with geostatistical simulation methods to compute the spatially correlated posterior distribution of the model parameters. We apply our method to a real angle-stack seismic data set to generate multiple geostatistical realizations of facies, P-velocity, S-velocity, density, porosity, and water saturation. The method is validated through a blind well test and a comparison with the standard Bayesian linearized inversion.

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Rock Physics Driven Joint Inversion to Facies and Reservoir Properties

Cited by 4 publications

References 2 publications

Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

Seismic to Facies Inversion Using Convolved Hidden Markov Model

Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties

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