Quantitative description of the non-linear flow properties of viscoelastic surfactant solutions

Author(s):  
P. Fischer ◽  
H. Rehage
2000 ◽  
Vol 2000.37 (0) ◽  
pp. 233-234
Author(s):  
Hironori SUGATA ◽  
Tsutomu TAKAHASHI ◽  
Masataka SHIRAKASHI

2017 ◽  
Vol 10 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Wang Shou-long ◽  
Li Ai-fen ◽  
Peng Rui-gang ◽  
Yu Miao ◽  
Fu Shuai-shi

Objective:The rheological properties of oil severely affect the determination of percolation theory, development program, production technology and oil-gathering and transferring process, especially for super heavy oil reservoirs. This paper illustrated the basic seepage morphology of super heavy oil in micro pores based on its rheological characteristics.Methods:The non-linear flow law and start-up pressure gradient of super heavy oil under irreducible water saturation at different temperatures were performed with different permeable sand packs. Meanwhile, the empirical formulas between start-up pressure gradient, the parameters describing the velocity-pressure drop curve and the ratio of gas permeability of a core to fluid viscosity were established.Results:The results demonstrate that temperature and core permeability have significant effect on the non-linear flow characteristics of super heavy oil. The relationship between start-up pressure gradient of oil, the parameters representing the velocity-pressure drop curve and the ratio of core permeability to fluid viscosity could be described as a power function.Conclusion:Above all, the quantitative description of the seepage law of super heavy oil reservoir was proposed in this paper, and finally the empirical diagram for determining the minimum and maximum start-up pressure of heavy oil with different viscosity in different permeable formations was obtained.


Author(s):  
Michiel J. van Esdonk ◽  
Jasper Stevens

AbstractThe quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.


2017 ◽  
Vol 773 ◽  
pp. 68-80 ◽  
Author(s):  
S. Acharya ◽  
D. Adamová ◽  
J. Adolfsson ◽  
M.M. Aggarwal ◽  
G. Aglieri Rinella ◽  
...  
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