Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. There are three main petrophysical...

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges reservoir characterization. Permeability is one of most difficult petrophysical parameters predict using conventional logging techniques. Clarifications work flow methodology are presented alongside comprehensive models this study. The purpose ...

The classification of well-log responses into separate flow units for generating local permeability models is often used to predict the spatial distribution of permeability in heterogeneous reservoirs. The present research can be divided into two parts; first, the nuclear magnetic resonance (NMR) log parameters are employed for developing a relationship between relaxation time and reservoir poro...

an improved model of mud dispersion has been introduced in this work. the advantages of this model consist of a new analytical correlation for dispersivity by using resistivity log data and using a new aspect of capacitance dispersion model. mathematical formulations were expressed, solved by numerical model taking advantage of actual log and formation data. achieved results yielded reasonable ...

porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. it is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. there are three main petrophysical...

The problem of matching feature points in multiple images is difficult to solve when their appearance changes due to illumination variance, either by lighting or object motion. In this paper we tackle this ill-posed problem by using the difference of local phase which is known to be stable to a certain extent even under illumination variances. In order to realize a precise matching, we basicall...