Modeling Approach for Niger-Delta Oil Formation Volume Factor Prediction Using Artificial Neural Network

Abstract Reservoir fluid properties are very important in reservoir engineering computations such as material balance calculations, well test analysis, reserve estimates, and numerical reservoir simulations. Ideally, this property should be obtained from actual measurements. Quite often, this measurement is either not available, or very costly to obtain. In such cases, empirically derived correlations are used in the prediction of this property. This work focuses on the use an artificial neural network (ANN) to address the inaccuracy of empirical correlations used for predicting oil formation volume factor. In this modeling approach 802 data set collected from the Niger Delta Region of Nigeria was used. The data set was randomly divided into three parts of which 60% was used for training, 20% for validation, and 20% for testing. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the new Artificial Neural Network to the existing empirical correlations. The ANN model outperformed the existing empirical correlations by the statistical parameters used with a lowest rank of 0.855 and better performance plot.