Understanding Uncertainties Using Performance Predictive Models for Smart Waterflooding
Committee MemberChen, Zhangxing
Low Salinity Waterflooding
Enhanced Oil Recovery
Improved Oil Recovery
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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%.
CitationKadeethum, T. (2016). Understanding Uncertainties Using Performance Predictive Models for Smart Waterflooding (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/28577
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