Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model

Ginting, Victor; Pereira, Felipe; Presho, Michael; Wo, Shaochang

doi:10.1007/s10596-011-9236-4

Cited by 42 publications

(17 citation statements)

References 28 publications

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“…The MCMC sampling procedure may become inefficient when a large number of parameters (heterogeneity at grid cell level) is being considered. The blocking MCMC [75] or the use of coarse scale models [77,93,94] are based on the use of upscalling techniques in order to reduce the number or parameters.…”

Section: Markov Chain Monte Carlo Sampling Methodsmentioning

confidence: 99%

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

et al. 2011

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Parameter identification is one of the key elements in the construction of models in geosciences. However, inherent difficulties such as the instability of ill-posed problems or the presence of multiple local optima may impede the execution of this task. Regularization methods and Bayesian formulations, such as the maximum a posteriori estimation approach, have been used to overcome those complications. Nevertheless, in some instances, a more in-depth analysis of the inverse problem is advisable before obtaining estimates of the optimal parameters. The Markov Chain Monte Carlo (MCMC) methods used in Bayesian inference have been applied in the last 10 years in several fields of geosciences such as hydrology, geophysics or reservoir engineering. In the present paper, a compilation of basic tools for inference and a case study illustrating the practical application of them are given. Firstly, an introduction to the Bayesian approach to the inverse problem is provided together with the most common sampling algorithms with MCMC chains. Secondly, a series of estimators for quantities of interest, such as the marginal densities or the normalization constant of the posterior distribution of the parameters, are reviewed. Those reduce the computational cost significantly, us-

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Section: Markov Chain Monte Carlo Sampling Methodsmentioning

confidence: 99%

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

et al. 2011

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“…Other approaches are: artificial neural networks (Al-Anazi and Babadagli, 2009), Markov Chain Monte Carlo (Ginting et al, 2011), probability perturbation method (Suzuki et al, 2007), recovery curve method (Ghaedi et al, 2015), Discrete Fracture Network flow simulator (Lange, 2009), Ensemble Kalman Filter (Lu and Zhang, 2015;Nejadi et al, 2015), and Kernel principal component analysis (Paico, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Schiozer

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

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“…This is performed with the DREAM (ZS) software (Laloy and Vrugt, 2012), which is an efficient Markov Chain Monte Carlo (MCMC) sampler. MCMC methods have been successfully applied in various inverse problems, including the assessment of uncertainty stemming from model calibration (e.g., Vrugt et al, 2003Vrugt et al, , 2008Keating et al, 2010;Schoups and Vrugt, 2010;Ginting et al, 2011;Arora et al, 2012).…”

Section: Statistical Calibrationmentioning

confidence: 99%

Global sensitivity analysis and Bayesian parameter inference for solute transport in porous media colonized by biofilms

Younès

Delay

Fajraoui

et al. 2016

Journal of Contaminant Hydrology

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Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model

Cited by 42 publications

References 28 publications

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Global sensitivity analysis and Bayesian parameter inference for solute transport in porous media colonized by biofilms

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