Understanding Uncertainties Using Performance Predictive Models for Smart Waterflooding

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atmire.migration.oldid5175
dc.contributor.advisorSarma, Hemanta
dc.contributor.advisorMaini, Brij
dc.contributor.authorKadeethum, Teeratorn
dc.contributor.committeememberChen, Zhangxing
dc.contributor.committeememberDong, Mingzhe
dc.date.accessioned2016-12-22T20:02:24Z
dc.date.available2016-12-22T20:02:24Z
dc.date.issued2016
dc.date.submitted2016en
dc.description.abstractSmart waterflooding involves an injection of water that contains an optimum concentration of electrolytes that are tailored to the rock-fluid characteristics of the target reservoir. The objective of this study is to identify uncertainties that could affect the performance of a smart waterflooding process and to determine how such risks can be diminished. To this end, the impact of parameters that contribute to uncertainties was investigated using a modeling approach. The key parameters identified as critical variables in the core-scale to field-scale analyses are the following: permeability, porosity, viscosity ratio, temperature, clay content, carbonate content, sodium, calcium, chlorine, calcite, dolomite, feldspar, pH, initial water saturation, heterogeneities, inter-well spacing, and production/injection pressure. These parameters have a significant effect on smart waterflooding performance. Many of these variables—temperature, mineralogy, aqueous composition, and pH—directly influence smart waterflooding process. However, the rest of the variables affect both the conventional and smart waterflooding processes. Furthermore, there is a significant binary interaction between variables. Each parameter effect was qualified and quantified by the normalized parameter method. Empirical predictive models for, (i) residual saturation, (ii) end-point water-relative permeability, and (iii) oil recovery were developed from a multivariable regression model with R-square prediction greater than 78%.en_US
dc.identifier.citationKadeethum, T. (2016). Understanding Uncertainties Using Performance Predictive Models for Smart Waterflooding (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/28577en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/28577
dc.identifier.urihttp://hdl.handle.net/11023/3503
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Petroleum
dc.subject.classificationSmart waterfloodingen_US
dc.subject.classificationLow Salinity Waterfloodingen_US
dc.subject.classificationPredictive modelsen_US
dc.subject.classificationReservoir Simulationen_US
dc.subject.classificationEnhanced Oil Recoveryen_US
dc.subject.classificationImproved Oil Recoveryen_US
dc.titleUnderstanding Uncertainties Using Performance Predictive Models for Smart Waterflooding
dc.typemaster thesis
thesis.degree.disciplineChemical and Petroleum Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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