Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs

Abstract

The most crucial aspect in determining field development plans is the oil recovery factor (RF). However, RF has a complex relationship with the reservoir rock and fluid properties. The application of artificial neural networks is able to produce complex correlations between reservoir parameters that affect the recovery factor. This research provides a new approach to improve the accuracy of the ANN model in the form of steps including removing outlier data, selecting input parameters, selecting transferring functions, selecting the number of neurons, and determining hidden layers. By applying these steps, an ANN model was selected with nine input parameters consisting of oil viscosity, water saturation, initial oil formation volume factor, formation thickness, initial pressure, permeability, specific gravity of oil, porosity, and original oil in place. Furthermore, based on the correlation coefficient, a tangent sigmoid transferring function, 30 neurons, and two hidden layers were determined. The proposed ANN correlation gives the best accuracy compared to the previous correlations. This is proved by the highest correlation coefficient of 0.91657.

Keywords

• Artificial neural network
• Recovery factor
• Reservoir
• Sandstone
• Solution gas drive