Regional probability perturbations for history matching

Hoffman, B. Todd; Caers, Jef

doi:10.1016/j.petrol.2004.11.001

Cited by 57 publications

(33 citation statements)

References 10 publications

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“…The matching process can be further improved by refining the selection of the sub-domains to be modified. Early approaches focused on simple Voronoi polygons around wells (Hoffman and Caers, 2005;Gervais et al, 2007) or drainage areas between injectors and producers (Le Ravalec-Dupin and Fenwick, 2002;Hoffman and Caers, 2005). Then, focusing on the integration of 4D seismic data, proposed to identify areas with strong seismic data mismatch and to define the target subdomains from the streamlines arriving to these areas.…”

Section: Inverse Problem and Parameterizationmentioning

confidence: 99%

Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models

Ravalec

Tillier

Veiga

et al. 2012

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

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Section: Inverse Problem and Parameterizationmentioning

confidence: 99%

Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models

Ravalec

Tillier

Veiga

et al. 2012

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

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“…One way of improving the matching of seismic data is the careful identification of the sub-domains to be adjusted. Early works considered Voronoi diagrams defined from wells (Hoffman and Caers, 2005;Gervais et al, 2007). Then, physically rooted subdomains were defined from drainage areas (Le RavalecDupin and Fenwick, 2002;Hoffman and Caers, 2005).…”

Section: Parameterizationmentioning

confidence: 99%

“…Early works considered Voronoi diagrams defined from wells (Hoffman and Caers, 2005;Gervais et al, 2007). Then, physically rooted subdomains were defined from drainage areas (Le RavalecDupin and Fenwick, 2002;Hoffman and Caers, 2005). determine influence zones by tracing the streamlines arriving to areas with significant 4-D seismic data mismatches.…”

Section: Parameterizationmentioning

confidence: 99%

Integrating Data of Different Types and Different Supports into Reservoir Models

Ravalec

Veiga

Derfoul

et al. 2012

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

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Par exemple, il s'est avéré important de revoir la formulation de la fonction objectif pour mieux quantifier l'erreur entre les attributs sismiques déduits des mesures et les attributs sismiques simulés pour les modèles de réservoir considérés. De plus, le paramétrage des propriétés pétrophysiques attribuées à l'ensemble des mailles constituant le modèle de réservoir est un élément essentiel dont dépend l'efficacité du calage des données de production et des attributs sismiques. Deux exemples d'application sont considérés. Le premier est un cas synthétique, de taille réduite. Il vise à mettre en évidence l'intérêt de l'intégration des attributs sismiques en plus des données de production pour réduire l'incertitude sur les prédictions calculées à partir des modèles contraints. Le second exemple est un cas réel correspondant à un champ en Mer du Nord exploité par Statoil. Il présente la construction d'un modèle de réservoir respectant à la fois des données de production et des impédances acoustiques collectées à deux temps différents. Cet exemple souligne la difficulté du problème, du fait notamment du très grand nombre de paramètres à gérer et du bruit important sur les données sismiques.

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“…Caers (2003) developed the probability perturbation technique, in which the probability of conductivity value at a location conditioned to the dynamic data is modeled using a deformation parameter. This conditional probability is subsequently merged with the pattern inferred from the training image (Caers 2002;Hoffman and Caers 2005;Kashib and Srinivasan 2006). Alcolea and Renard (2010) developed a block moving window algorithm, an extension of the block Markov chain Monte Carlo method by Fu and Gómez-Hernández (2009), for conditioning MPS simulations to piezometric head data as well as to connectivity data.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Geologic and Reservoir State Variables Within an Ensemble-Based Multiple-Point Statistic Framework

Srinivasan

Zhou

et al. 2013

Math Geosci

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Li, L.; Srinivasan, S.; Zhou, H.; Gómez-Hernández, JJ. (2014) Abstract Assessment of uncertainty due to inadequate data and imperfect geological knowledge is an essential aspect of the subsurface model building process. In this work, a novel methodology for characterizing complex geological structures is presented that integrates dynamic data. The procedure results in the assessment of uncertainty associated with the predictions of flow and transport. The methodology is an extension of a previously developed pattern search-based inverse method that models the spatial variation in flow parameters by searching for patterns in an ensemble of reservoir models. More specifically, the pattern-searching algorithm is extended in two directions: (1) state values (such as piezometric head) and parameters (such as conductivities) are simultaneously and sequentially estimated, which implies that real-time assimilation of dynamic data is possible as in ensemble filtering approaches; (2) both the estimated parameter and state variables are considered when pattern searching is implemented. The new scheme results in two main advantages -better characterization of parameters, especially for delineating small scale features, and an ensemble of head states that can be used to update the parameter field using the dynamic data at the next instant, without running expensive flow simulations. An efficient algorithm for pattern search is developed,

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Regional probability perturbations for history matching

Cited by 57 publications

References 10 publications

Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models

Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models

Integrating Data of Different Types and Different Supports into Reservoir Models

Simultaneous Estimation of Geologic and Reservoir State Variables Within an Ensemble-Based Multiple-Point Statistic Framework

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