Quantitative Use of 4D Seismic Data for Reservoir Description

Dong, Yiran; Oliver, Dean S.

doi:10.2118/84571-pa

Cited by 44 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu and Oliver [108] used a quasi-Newton method to adjust geologic facies boundaries to match pressure data. Dong and Oliver [45] applied a quasiNewton method to history matching of 4D seismic (pwave impedance) data. Because of the large amount of data, this problem would be nearly impossible with Gauss-Newton.…”

Section: Gradient (Downhill) Methodsmentioning

confidence: 99%

Recent progress on reservoir history matching: a review

2010

Self Cite

View full text Add to dashboard Cite

History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. In this review paper, we will summarize key developments in history matching and then review many of the accomplishments of the past decade, including developments in reparameterization of the model variables, methods for computation of the sensitivity coefficients, and methods for quantifying uncertainty. An attempt has been made to compare representative procedures and to identify possible limitations of each.

show abstract

Section: Gradient (Downhill) Methodsmentioning

confidence: 99%

Recent progress on reservoir history matching: a review

2010

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two alternatives can be envisioned. The first approach encompasses a time-to-depth conversion applied to the reference seismic attributes and a comparison in depth domain (Dong and Oliver, 2005;Stephen et al, 2006;Roggero et al, 2007). With this approach, the conversion is made once and for all before the history matching process.…”

Section: Time-to-depth Conversionmentioning

confidence: 99%

Simultaneous Inversion of Production Data and Seismic Attributes: Application to a Synthetic SAGD Produced Field Case

Tillier

Ravalec

Veiga

2012

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

View full text Add to dashboard Cite

Résumé -Inversion simultanée des données de production et des attributs sismiques : application à un champ synthétique produit par injection de vapeur -L'utilisation conjointe des données de production et des attributs de sismique répétée facilite la compréhension des mouvements de fluide dans les formations géologiques et aide à la construction de modèles numériques fiables représentant ces formations. Ceci a récemment motivé le développement de techniques d'inversion ou de calage dédiées à l'identification de modèles cohérents avec l'ensemble des données disponibles. La méthodologie décrite dans ce papier permet de déterminer les cartes de propriétés pétrophysiques comme les faciès, les porosités ou les perméabilités à partir des données de production et des attributs de sismique répétée. Elle est appliquée avec succès à un champ synthétique d'huile lourde produite par injection de vapeur. Ce cas d'étude, inspiré d'un cas réel, illustre comment définir une méthodologie d'inversion qui respecte l'ensemble des données disponibles. L'étude est centrée sur trois points particuliers de la méthodologie qui sont les clés du succès. Premièrement, le choix de la paramétrisation est essentiel. La paramétrisation doit permettre des variations locales avec peu de paramètres. Deuxièmement, cette étude met en évidence la nécessité d'une formulation alternative pour mesurer l'erreur entre les attributs sismiques de référence et les attributs sismiques simulés. Finalement, nous étudions deux approches pour l'intégration des attributs sismiques de référence. Ils peuvent être intégrés en temps ou bien en profondeur. Nous montrons que, dans le cas étudié, ce dernier point n'influence pas la qualité du résultat obtenu car l'erreur de conversion temps-profondeur est faible. (after a time-to-depth conversion). In the case studied, it is shown that the two approaches are equivalent since time-to depth-conversion error is quite small.

show abstract

“…Heterogeneous porosity and shaliness distributions are assigned to both the channel and nonchannel facies with prescribed variograms. The acoustic impedance and Poison's ratio are calculated using the same rock physics model used in [10]. The generated synthetic seismic attributes are shown in Fig.…”

Section: Examplementioning

confidence: 99%

Application of EM algorithms for seismic facices classification

Han

Zhao²,

et al. 2010

Comput Geosci

View full text Add to dashboard Cite

Identification of the geological facies and their distribution from seismic and other available geological information is important during the early stage of reservoir development (e.g. decision on initial well locations). Traditionally, this is done by manually inspecting the signatures of the seismic attribute maps, which is very time-consuming. This paper proposes an application of the Expectation-Maximization (EM) algorithm to automatically identify geological facies from seismic data. While the properties within a certain geological facies are relatively homogeneous, the properties between geological facies can be rather different. Assuming that noisy seismic data of a geological facies, which reflect rock properties, can be approximated with a Gaussian distribution, the seismic data of a reservoir composed of several geological facies are samples from a Gaussian mixture model. The mean of each Gaussian model represents the average value of the seismic data within each facies while the variance gives the variation of the seismic data within a facies. The proportions in the Gaussian mixture model represent the relative volumes of different facies in the reservoir. In this setting, the facies classification problem becomes a problem of estimating the parameters defining the Gaussian mixture model. The EM algorithm has long been used to estimate Gaussian mixture model parameters. As the standard EM algorithm does not consider spatial relationship among data, it can generate spatially scattered seismic facies which is physically unrealistic. We improve the standard EM algorithm by adding a spatial constraint to enhance spatial continuity of the estimated geological facies. By applying the EM algorithms to acoustic impedance and Poisson's ratio data for two synthetic examples, we are able to identify the facies distribution.

show abstract

Quantitative Use of 4D Seismic Data for Reservoir Description

Cited by 44 publications

References 26 publications

Recent progress on reservoir history matching: a review

Recent progress on reservoir history matching: a review

Simultaneous Inversion of Production Data and Seismic Attributes: Application to a Synthetic SAGD Produced Field Case

Application of EM algorithms for seismic facices classification

Contact Info

Product

Resources

About