Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique

Oil formation volume factor (OFVF) is considered one of the main parameters required to characterize the crude oil. OFVF is needed in reservoir simulation and prediction of the oil reservoir performance. Existing correlations apply for specific oils and cannot be extended to other oil types. In addition, big errors were obtained when we applied existing correlations to predict the OFVF. There is a massive need to have a global OFVF correlation that can be used for different oils with less error. The objective of this paper is to develop a new empirical correlation for oil formation volume factor (OFVF) prediction using artificial intelligent techniques (AI) such as; artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction. In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions. The results obtained showed that the ANN model yielded the highest correlation coefficient (0.997) and lowest average absolute error (less than 1%) for OFVF prediction as a function of the specific gravity of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir compared with ANFIS and SVM. The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction. It can be used to predict the OFVF with a high accuracy.