Interwell connectivity identification in immiscible gas-oil systems using statistical method and modified capacitance-resistance model: A comparative study

2021 ◽  
Vol 198 ◽  
pp. 108175
Author(s):  
Seyed Hamidreza Yousefi ◽  
Fariborz Rashidi ◽  
Mohammad Sharifi ◽  
Mohammad Soroush ◽  
Ashkan Jahanbani Ghahfarokhi
Keyword(s):  
Comparative Study ◽  
Statistical Method ◽  
Gas Oil ◽  
Resistance Model ◽  
Interwell Connectivity

A comparative study of the number and mass of fine particles emitted with diesel fuel and marine gas oil (MGO)

2012 ◽  
Vol 57 ◽  
pp. 22-28 ◽  
Author(s):  
Md. Nurun Nabi ◽  
Richard J. Brown ◽  
Zoran Ristovski ◽  
Johan Einar Hustad
Keyword(s):  
Comparative Study ◽  
Diesel Fuel ◽  
Fine Particles ◽  
Gas Oil

scholarly journals A Comparative study of log volume estimation by using statistical method

2020 ◽  
Vol 7 (1) ◽  
pp. 22-28
Author(s):  
Sharifah Sakinah Syed Ahmad ◽  
Siti Hajar Mohd Mushar ◽  
Nur Hajar Zamah Shari ◽  
Fauziah Kasmin
Keyword(s):  
Forest Management ◽  
Comparative Study ◽  
Statistical Method ◽  
Prediction Accuracy ◽  
Sustainable Forest Management ◽  
Volume Estimation ◽  
Volume Formula ◽  
Precise Estimation ◽  
Precision And Accuracy

Log volume estimation is a measurement of the amount of merchantable volume and precise estimation of log volume plays an important role in sustainable forest management. There are several log volume formula which commonly used in estimating the log volume, however, there are significant differences between each formula. Therefore, this paper evaluates the performance of three different log volume formula which are Smalian’s, Huber’s and Bruce’s formula against several log sectional length. The performance of each log volume formula will be evaluated in terms of the bias, precision and accuracy of the estimation. The result shows that Huber’s formula performs the best for log sectional length of 2 m, 4 m, 6 m and 8 m log sectional length. The log sectional length and prediction accuracy is inversely related whereby the shorter the log sectional length, the better the prediction accuracy is.

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