Monthly Archives: February 2019

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Azimi published her 1st paper in PermLab

Category : Uncategorized

Congrates Zahra Azimi for her first publication in PermLab team. Zahra investigated  cross-linked polymer gels as a temporary plugging agent to control the leakage of fluid during drilling, completion, and workover operations. In this study, various concentrations and size of silica nanoparticles are introduced into the sulfonated polyacrylamide (SPAM)/chromium (III) acetate system to produce a nanocomposite with enhanced mechanical properties. First, the rheological behavior of gelant and viscoelastic properties of synthesized nanocomposites are investigated. Then, the surface chemistry and morphology of the synthesized gels is evaluated by Fourier transform infrared (FTIR) spectroscopy and field emission scanning electron microscopy (FESEM), respectively. Finally, the maximum sealing differential pressure for gels for temporary plugging of a wellbore is measured by applying differential pressure across the nanocomposite gel in a designed set-up. The results showed that the precrosslinking reaction and the gelant viscosity are directly related to the size and concentration of the silica nanoparticles as well as the wellbore temperature. Moreover, it is demonstrated that nanocomposites containing 20-30 nm sized particles have a higher mechanical strength and plugging capability in comparison to composites containing silica particles with sizes of 7-10 nm and 60-70 nm.


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Geological Boundary Detection in JPSE

Category : New publication

Congratulations to Mohammad Amin Partovi, Msc student of petroleum engineering, for his recently published work about geological boundary detection from well-logs using pattern recognition techniques in Petroleum Science & Engineering.

In this study, we implemented an automatic well-to-well correlation approach based on pattern extraction from well-logs. The well-log patterns were recognized by calculating several statistical and fractal parameters. As a fractal parameter, we selected the wavelet standard deviation exponent, calculated by the discrete wavelet transform. Furthermore, to select the proper wavelet function, the energy to Shannon entropy ratio criterion was implemented. The statistical pattern recognition parameters of this study include average value, maximum to minimum ratio, coefficient of variation, and the trend angle of well-log data (i.e., Gamma ray log) in a window around the geological boundary. Moreover, the analysis of variance (ANOVA) tool and the Tukey multiple comparison method were implemented to evaluate the effectiveness of each parameter (i.e., fractal or statistical parameters) during the determination of boundary depths. The gamma ray logs from three wells of an oil field were used as a dataset for the evaluation of our algorithm. The outputs of our methodology were also compared with the new detrended fluctuation analysis (DFA) method, which show promising outcomes over it. The results show that the average value of the signal, among the analyzed parameters, is the most effective parameter; however, implementing the combination of all fractal and statistical parameters can improve the accuracy of the geological boundary detection.