The use of artificial neural network to predict correlation of cementation factor to petrophysical properties in Yamamma formation

Abstract

The cementation factor has specific effects on petrophysical properties in porous media. The accurate determination of this factor gives reliable saturation results and consequently hydrocarbon reserve calculations. Nasiriya oil field is the studied field, which is one of the giant oil fields in the south of Iraq. Five wells from NS-1 to NS-5 were studied wells. The study was made across Yamamma carbonate formation with depth interval from 3,156 m to 3,416 m. Environmental corrections had been made as per SLB charts 2005. Permeability, porosity, resistivity formation factor and cementation factor had been calculated using interactive petrophysical software. In this study, porosity, permeability and resistivity formation factor relationships to cementation factor were proposed using the artificial neural network model. This methodology provided very efficient performance and excellent prediction of cementation factor value with less than 10-4 mean square error (MSE). The results of this model showed that the cementation factor values ranged between 1.95 and 2.13.