Evolving new strategies to estimate reservoir oil formation volume factor: Smart modeling and correlation development

The precise estimation of oilfield PVT properties is of primary importance for improving field evaluation and development strategies. In the present work, adaptive network-based fuzzy inference system (ANFIS) and genetic programming (GP) models were established for the accurate assessment of reservoir oil formation volume factor (OFVF). A large database including nearly 1200 datapoints from all over the globe was collected for modeling. The performance of the new models was compared to correlations in the existing literature via several graphical and statistical paradigms. Variance approach analysis was implemented to determine the sensitivity of the target parameter as the output with respect to each input variable. It is shown that the highest forecasting accuracy is attained using the developed ANFIS model and GP-based empirical correlation with an average absolute relative deviation (AARD%) of 1.8% and 2.1%, respectively, in comparison to the results obtained from other approaches described in the literature. Lastly, the mathematical techniques designed for estimating OFVF can be of high importance for experts working with reservoir simulation and PVT analysis.