Hybrid Intelligence Model for Reservoir Properties Predictions: A Case Study of the Niger Delta

To reliably predict the reservoir’s petrophysical properties performance, an accurate model of the reservoir is necessary. Genetic algorithm (GA) and Artificial neural network (ANN) are two well-known techniques for optimizing and learning, as one complements the weakness of the other. This study aims to develop a model using ANN-GA for the accurate prediction of the three fundamental reservoir properties, such as porosity (φ), permeability (K), and water saturation (Sw). The hybrid model was developed using 1304 datasets obtained in the Niger Delta region. These datasets were fed into MATLAB R2015a with an architecture of 10 inputs, 10 neurons, and three outputs using the feed-forward backpropagation method with Levenberg-Marquardt training algorithm. The criteria for evaluating the ANN-GA network performance include mean squared error (MSE), average absolute percentage relative error (AAPRE), coefficient of determination (R²) and correlation coefficient (R). The developed ANN-GA predicted values, when compared with the field values, showed a significant match. From the results obtained, overall R and MSE values were 0.99039 and 3.5537x10-6 respectively. R values for training, testing, and validation include 0.95765, 0.96674, and 0.95765. Again, the results obtained for the R² were φ of 0.9859, K of 0.9816, and Sw of  0.9759. Also, MSE of 1.59952x10-6, 9.71x10-5 and 4.57x10-7 were obtained for water saturation, permeability, and porosity, respectively. The results further indicated AAPRE of 4.57735 for Sw , 1.252225 for K, and 0.04059 for φ. Thus, the developed model provides a better tool for the prediction of the reservoir petrophysical properties.