Combining the Pilot Point and Gradual Deformation Methods for Calibrating Permeability Models to Dynamic Data

Ravalec-Dupin, M. Le; Hu, Lin

doi:10.2516/ogst:2007015

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Indeed, possible correlations between them are not taken into account in the optimization process. As proposed in [17], applying the gradual deformation to perturb the values attributed to pilot points avoids these limitations. Adding an a priori term in the objective function enables to release the constraint on the point locations, as described in [1] for instance.…”

Section: Adaptive Pilot Point Methodologymentioning

confidence: 98%

“…The application of the gradual deformation method to the pilot point methodology was studied in [17], where the authors gradually vary the values of the Gaussian field at fixed locations. Here, we generalize their approach to deform also the locations of the pilot points, given their probability density function (the filtered normalized residual map).…”

Section: Gradual Deformation Of Pilot Point Locationsmentioning

confidence: 99%

“…Let us note that conditional kriging on data with volume support can also be applied to indicator functions to perturb facies proportions as proposed in [22]. These techniques were applied to match 4D seismic-related data in real-field cases [21] and synthetic cases [10,15,17] for instance. The choice of the spatial supports associated to the perturbed parameters (deformation parameters, pilot points, or blocks) was driven by the error on seismic data.…”

Section: Introductionmentioning

confidence: 99%

“…The choice of the spatial supports associated to the perturbed parameters (deformation parameters, pilot points, or blocks) was driven by the error on seismic data. In [17], the pilot points were chosen among locations with the highest seismic-related data misfit. In [10], the definition of the regions is performed in two steps.…”

Section: Introductionmentioning

confidence: 99%

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Local adaptive parameterization for the history matching of 3D seismic data

Veiga

Gervais

2011

Comput Geosci

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Matching seismic data in assisted history matching processes can be a challenging task. One main idea is to bring flexibility in the choice of the parameters to be perturbed, focusing on the information provided by seismic data. Local parameterization techniques such as pilot-point or gradual deformation methods can be introduced, considering their high adaptability. However, the choice of the spatial supports associated to the perturbed parameters is crucial to successfully reduce the seismic mismatch. The information related to seismic data is sometimes considered to initialize such local methods. Recent attempts to define the regions adaptively have been proposed, focusing on the mismatch between simulated and reference seismic data. However, the regions are defined manually for each optimization process. Therefore, we propose to drive the definition of the parameter support by performing an automatic definition of the regions to be perturbed from the residual maps related to the 3D seismic data. Two methods are developed in this paper. The first one consists in clustering the residual map with classification algorithms. The second method proposes to drive the generation of pilot point locations in an adaptive way. Residual maps, after proper normalization, are considered as probability density functions of the pilot point locations. Both procedures lead to a complete adaptive and highly flexible perturbation technique for 3D seismic matching. A synthetic study based on the PUNQ test case is introduced to illustrate the potential of these adaptive strategies.

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Section: Adaptive Pilot Point Methodologymentioning

confidence: 98%

Section: Gradual Deformation Of Pilot Point Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local adaptive parameterization for the history matching of 3D seismic data

Veiga

Gervais

2011

Comput Geosci

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“…Therefore, finding a calibrated realization can be accomplished by a series of 1D optimizations. These methods have been successfully applied in hydrogeology and petroleum engineering [ Le Ravalec‐Dupin and Hu , 2007; Le Ravalec‐Dupin , 2010; Llopis‐Albert and Cabrera , 2009; Ronayne et al , 2008].…”

Section: Methodsmentioning

confidence: 99%

Bayesian inverse problem and optimization with iterative spatial resampling

Mariethoz

Renard

Caers

2010

Water Resources Research

117

109

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[1] Measurements are often unable to uniquely characterize the subsurface at a desired modeling resolution. In particular, inverse problems involving the characterization of hydraulic properties are typically ill-posed since they generally present more unknowns than data. In a Bayesian context, solutions to such problems consist of a posterior ensemble of models that fit the data (up to a certain precision specified by a likelihood function) and that are a subset of a prior distribution. Two possible approaches for this problem are Markov chain Monte Carlo (McMC) techniques and optimization (calibration) methods. Both frameworks rely on a perturbation mechanism to steer the search for solutions. When the model parameters are spatially dependent variable fields obtained using geostatistical realizations, such as hydraulic conductivity or porosity, it is not trivial to incur perturbations that respect the prior spatial model. To overcome this problem, we propose a general transition kernel (iterative spatial resampling, ISR) that preserves any spatial model produced by conditional simulation. We also present a stochastic stopping criterion for the optimizations inspired from importance sampling. In the studied cases, this yields posterior distributions reasonably close to the ones obtained by a rejection sampler, but with a greatly reduced number of forward model runs. The technique is general in the sense that it can be used with any conditional geostatistical simulation method, whether it generates continuous or discrete variables. Therefore it allows sampling of different priors and conditioning to a variety of data types. Several examples are provided based on either multi-Gaussian or multiple-point statistics.

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Statistical comparison of variogram-based inversion methods for conditioning to indirect data

Lauzon

Marcotte

2022

Computers & Geosciences

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Combining the Pilot Point and Gradual Deformation Methods for Calibrating Permeability Models to Dynamic Data

Cited by 5 publications

References 20 publications

Local adaptive parameterization for the history matching of 3D seismic data

Local adaptive parameterization for the history matching of 3D seismic data

Bayesian inverse problem and optimization with iterative spatial resampling

Statistical comparison of variogram-based inversion methods for conditioning to indirect data

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