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dc.contributor.advisorSrinivasan, Sanjayen
dc.creatorReinlie, Shinta Tjahyaningtyasen
dc.date.accessioned2008-08-28T22:59:20Zen
dc.date.available2008-08-28T22:59:20Zen
dc.date.issued2006en
dc.identifierb64885264en
dc.identifier.urihttp://hdl.handle.net/2152/2625en
dc.descriptiontexten
dc.description.abstractContinuous dynamic data such as well flowing bottom-hole pressure carry information that characterizes reservoir heterogeneity. A novel approach to analyze continuous monitoring pressure data and to update reservoir models based on incremental information is presented. First, the pressure transient data is analyzed to identify the size and shape of permeability heterogeneity in the presence of fluctuations in rate and pressure. Unlike the complicated pressure or rate deconvolution algorithms presented in the literature, a simple semi-analytical approach is presented here that attempts to reconstruct the bottom hole pressure response after removing the effect of rate fluctuation. Using the reconstructed pressure profile, estimates of the radius to the boundary of the heterogeneous region and the effective average permeability are obtained by applying a simple optimization procedure for fitting the pressure and pressure derivative plots. Once the configuration of the reservoir heterogeneity in the vicinity of wells has been identified that information is used to condition high-resolution reservoir models. The conditional probability distribution that characterises the uncertainty in permeability value at any location is perturbed using the dynamic pressure response as conditioning information. The gradual deformation of the conditional probability distribution is carried out within a p-field simulation framework. This approach retains the computational efficiency of the traditional gradual deformation algorithm, while at the same time is amenable to modelling non-Gaussian permeability fields that exhibit severe discontinuities such as facies/indicator type distributions. Sequential updating of reservoir model answers the challenge posed by the continuous nature of data acquired from permanent monitoring system. We proposed a variant of ensemble Kalman Filter which incorporates prior geological information as additional constraints in state matrix updating. The application of the proposed method is demonstrated on a realistic 3-D example. Results show that both p-field approach and ensemble Kalman Filter when used for history matching algorithm yield reservoir models that exhibit well responses that match dynamic data as well as honour geological information imposed as constraints.
dc.format.mediumelectronicen
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshOil reservoir engineering--Mathematical modelsen
dc.subject.lcshStochastic analysisen
dc.titleAnalysis of continuous monitoring data and rapid, stochastic updating of reservoir modelsen
dc.description.departmentPetroleum and Geosystems Engineeringen
dc.identifier.oclc85767765en
dc.type.genreThesisen
thesis.degree.departmentPetroleum and Geosystems Engineeringen
thesis.degree.disciplinePetroleum Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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