Probability Field Simulation

Froidevaux, R.

doi:10.1007/978-94-011-1739-5_7

Cited by 55 publications

(27 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Strictly speaking, the probability field method (Froidevaux, 1993) is a device for conditioning a uniform simulation to well data. In practice, a uniform simulation is often derived from a Gaussian simulation that can be generated by any Gaussian simulation algorithm.…”

Section: Continuous Gaussian Simulationsmentioning

confidence: 99%

“…In the particular case where the proportion support is reduced to the well data support, the conditioning to lithofacies indicator data along wells is directly satisfied without the above step. Note that in this particular case, the truncated Gaussian method becomes equivalent to the probability field method (Froidevaux, 1993) for simulating lithofacies. This is because the local distributions involved in the probability field method are equivalent to the local lithofacies proportions, and in practice the probability field used for sampling local distributions is obtained by transforming a Gaussian field into a uniform field.…”

Section: Truncated Gaussian Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Elements for an Integrated Geostatistical Modeling of Heterogeneous Reservoirs

Ravalec-Dupin

2004

Oil & Gas Science and Technology - Rev. IFP

View full text Add to dashboard Cite

Résumé -Éléments pour une modélisation géostatistique intégrée de réservoirs hétérogènes -L'approche géostatistique pour la modélisation des réservoirs hétérogènes permet, d'une part, d'intégrer l'ensemble des données de natures diverses et d'échelles différentes, et d'autre part, d'évaluer les incertitudes par génération de multiples scénarios possibles. La conception d'un modèle géostatistique de réservoir doit d'abord dépendre de l'environnement de dépôt géologique, ce qui permet de représenter les hétérogénéités majeures qui contrôlent l'écoulement des fluides. Ensuite, les données quantitatives provenant des carottes, des diagraphies, de la sismique, des tests de puits, etc., doivent être utilisées pour l'inférence des paramètres structuraux du modèle. Enfin, le calage des réalisations du modèle aux données hydrodynamiques permet d'augmenter davantage la fiabilité du modèle dans la prévision en production. Cet article présente les éléments d'une méthodologie intégrée pour la modélisation des réservoirs hétérogènes. Nous introduisons d'abord les modèles géostatistiques de base couramment utilisés pour décrire les réservoirs hétérogènes, suivis par un bref aperçu sur l'inférence des paramètres structuraux des modèles. Nous présentons ensuite une approche inverse basée sur la méthode de déformation graduelle pour le calage d'historique. Abstract -Elements for an Integrated Geostatistical Modeling of Heterogeneous Reservoirs

show abstract

Section: Continuous Gaussian Simulationsmentioning

confidence: 99%

Section: Truncated Gaussian Simulationsmentioning

confidence: 99%

Elements for an Integrated Geostatistical Modeling of Heterogeneous Reservoirs

Ravalec-Dupin

2004

Oil & Gas Science and Technology - Rev. IFP

View full text Add to dashboard Cite

show abstract

“…The Probability Field Simulation (PFS) algorithm [32] is used to draw values from the local cdf (s), instead of Monte Carlo simulation, as it accomplishes with both the local cdf (s) and the spatial continuity model of P(x).…”

mentioning

confidence: 99%

Geostatistical Methodology to Characterize Volcanogenic Massive and Stockwork Ore Deposits

Schwartz

Almeida

2017

Minerals

View full text Add to dashboard Cite

Abstract:The Zambujal ore deposit, Neves-Corvo mine, is a zoned volcanogenic sulfide deposit of copper and zinc, with massive ores at the top and stockwork ores at the bottom. Metal grades are strongly zoned by ore types. The main methodology hereby proposed combines geostatistical techniques so that an improved stochastic geological model of the Zambujal deposit encompassing morphology and grades is presented. The model of the morphology was made in two main steps. First, a 3D solid of the boundaries was created and then a 3D grid model of the local sulfide proportion was simulated. This latter variable was modeled by using rock specific gravity as a proxy. After that, a conditional grid model of relative copper grades, i.e., recalculated metal grades assuming only the sulfide content, is also simulated in accordance with the morphology. At the end, the new tool, metal tonnage cut-off surface, is proposed, which combines copper grades within massive ores and stockwork ores. To validate the results found, the global tonnages of copper obtained by Ordinary Kriging and the proposed methodology are compared.

show abstract

“…Unlike the idea of (Hu et al, 2013), v vi which uses the EnKF to directly update uncorrelated uniform random fields (those used to draw from the local conditional marginal distributions in sequential simulation), the new version propose working on correlated uniform random fields, more precisely the same uniform random field used in probability field simulation (Froidevaux, 1993). The comparison of both versions shows that the new proposed one is much better than the original in order to capture the main patterns of conductivity and in reducing uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…A diferencia de la idea de (Hu et al, 2013), que utiliza el EnKF para actualizar directamente campos uniformes no correlacionados (los que se utilizan para el muestreo aleatorio de las distribuciones marginales condicionales locales en la simulación secuencial), la nueva versión propone trabajar en campos aleatorios uniformes correlacionados, más precisamente los mismos campos que se utilizan en la simulación por campos de probabilidades (Froidevaux, 1993). La comparación de las dos versiones demuestra que la nueva versión propuesta es mucho mejor que la original tanto a la hora de capturar los principales patrones de conductividad como en la reducción de la incertidumbre.…”

Section: Introductionunclassified

Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity

Xu¹

View full text Add to dashboard Cite

Ensemble kalman filter (EnKF) has proven to be a powerful inverse method for the characterization of hydraulic conductivities, which works well for non-linear state equation and Gaussian-distributed parameters. It is computationally more efficient than other inverse methods; however, it is still time-consuming. This thesis addresses two issues, how to speed up the EnKF through code parallelization and how to properly address the problem of characterizing non-Gaussian hydraulic conductivity fields. It is organized in four parts.The first part deals with the the parallelization of the ensemble Kalman filter (EnKF) algorithm in the context of hydraulic conductivity characterization using transient piezometric head data. The EnKF consists of two steps, forecast and analysis, and both steps have potential to be parallelized. In the forecast step, due to the Monte Carlo nature of the EnKF, the most straightforward way for parallelization is at the realization level, where each member of the ensemble is sent to a different processor. While in the analysis step, the computations of the covariances are distributed between the different processors. The results of speedup and efficiency show that the parallelized EnKF can significantly reduce the computation time, especially for a large number of ensemble realizations.The second part describes an application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. The objective is to demonstrate the power of transient piezometric head for the characterization of the spatial variability of a channelized bimodal hydraulic conductivity field, where the only existing prior information about conductivity is its univariate marginal distribution. Besides, covariance localization and covariance inflation are used to overcome the appearance of spurious correlations and the underestimation of the final uncertainty by the NS-EnKF. Covariance localization eliminates the effect of spurious correlations between state variables and parameters by constraining the correlation range of the empirical covariance. Covariance inflation is a technique used to avoid filter inbreeding by inflating the empirical covariance. The results show the importance of covariance localization and covariance inflation to reduce filter inbreeding.The third part investigates the inverse method proposed by (Hu et al., 2013) and proposes an improved version. Unlike the idea of (Hu et al., 2013), v vi which uses the EnKF to directly update uncorrelated uniform random fields (those used to draw from the local conditional marginal distributions in sequential simulation), the new version propose working on correlated uniform random fields, more precisely the same uniform random field used in probability field simulation (Froidevaux, 1993). The comparison of both versions shows that the new proposed one is much better than the original in order to capture the main patterns of conductivity and in reducing uncertainty.The fourth and last part proposes a ...

show abstract

Probability Field Simulation

Cited by 55 publications

References 4 publications

Elements for an Integrated Geostatistical Modeling of Heterogeneous Reservoirs

Elements for an Integrated Geostatistical Modeling of Heterogeneous Reservoirs

Geostatistical Methodology to Characterize Volcanogenic Massive and Stockwork Ore Deposits

Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity

Contact Info

Product

Resources

About