Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks

The cementation factor is one of the basic parameters for calculating water saturation and then hydrocarbon saturation of reservoirs. The best way to determine the cementation factor is through laboratory measurements. To generalize this coefficient for samples without laboratory measurements, experimental relationships versus petrophysical properties by researchers can be somewhat helpful. The method of artificial neural networks, with the help of training, validation, and data analysis, has given better results in determining the cementation factor of carbonate samples. It is one of the best methods to use petrophysical data as training data and make acceptable predictions with analytical methods. Therefore, laboratory measurement of the cementation factor has been performed for 159 carbonate cores from the Sarvak formation in southwest Iran. For the studied samples, the cementation factor in porosity was determined as a quadratic equation with the highest correlation coefficient. In this study, the compatibility of the experimental relationship shows better conformity by considering the permeability of each sample. Improvement of empirical relationships by the authors, correlation coefficients between the laboratory data, and the experimental relationships have been increased. Therefore, it is better to use improved experimental relationships for the studied carbonate samples. Artificial neural network methods have been used to process the data, best adapt the laboratory data, and present a suitable model. The Bayesian Regularization algorithm with five hidden layers has the least error in the test, validation, and testing stages.