Explicit Neural Network-Based Models for Bubble Point Pressure and Formation Volume Factor Prediction

Most available artificial intelligence models for bubble-point pressure (P_b) and oil formation volume factor (B_ob) are multiple-input single-output models that are not reproducible or with complex topology. In this study, a multiple-input multiple-output genetically optimized neural network to predict oil P_b and B_ob was developed using solution gas–oil ratio, API-oil gravity, gas gravity and reservoir temperature from 781 published datasets. The overall performance of the developed neural network resulted in a correlation coefficient (R) of 0.99918 and a mean square error (MSE) of 5.9941 × 10^–4. The developed P_b and B_ob models predictions resulted in a coefficient of determination (R²) of 0.9985 and R-value of 0.99423 for P_b, then R² of 0.9842 and R-value of 0.99207 for B_ob. Also, the P_b model had MSE of 2.0 × 10^–4, root mean square error (RMSE) of 4.45 × 10^–3 and average absolute percent relative error (AAPRE) of 0.4447, while B_ob had 1.0 × 10^–4, 3.9 × 10^–3 and 0.0391 for MSE, RMSE and AAPRE, respectively. Furthermore, the generalization capacity of the developed models with new datasets resulted in R², R, MSE, RMSE and AAPRE of 0.9613, 0.98046, 0.03713, 0.1927 and 19.2698, respectively, for P_b, as the B_ob model had R² of 0.9499, R of 0.97463, MSE of 0.0218, RMSE of 0.14763 and AAPRE of 8.20211. Again, the developed P_b and B_ob models' generality performance showed that they outperformed some published correlations: Standing [1], Glaso [2], Al-Marhoun [3], Dokla and Osman [4], Almehaideb [5], Al-Shammasi [6], etc. Thus, these developed explicit P_b and B_ob models for oil reservoir PVT prediction can be applied.