Prediction of Oil Formation Volume Factor Using an Intelligent Tool: Artificial Neural Network

Abstract

The Oil Formation Volume Factor parameter is a very important fluid property in reservoir engineering computations. 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 of an intelligent tool known as an artificial neural network (ANN) to address the inaccuracy of empirical correlations used for predicting oil formation volume factor. The new intelligent model was developed  using 448 published data from the Middle East, Malaysia, Africa, North Sea, Mediterranean basin, Gulf of Persian fields and 160 data set collected from the Niger Delta Region of  Nigeria. 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 intelligent model to the existing empirical correlations. The ANN intelligent model outperformed the existing empirical correlations by the statistical parameters used with a lowest rank of 0.6313 and better performance plot. Key words : Oil formation volume factor; Empirical correlation; Artificial neural network; Back propagation; Statistical analysis