A Single Artificial Neural Network Model Predicts Bubble Point Physical Properties of Crude Oils

Abstract

Abstract Reservoir fluid properties at bubble points play a vital role in reservoir and production engineering computations. Ideally, the bubble point physical properties of crude oils are obtained experimentally. On some occasions, these properties are neither available nor reliable; then, empirically derived correlations or artificial neural network models are used to predict the properties. This study presents a new single multi-input multi-output artificial neural network model for predicting the six bubble point physical properties of crude oils, namely, oil pressure, oil formation volume factor, isobaric thermal expansion of oil, isothermal compressibility of oil, oil density, and oil viscosity. A large database comprising conventional PVT laboratory reports was collected from major producing reservoirs in the Middle East. The model input is constrained mathematically to be consistent with the limiting values of the physical properties. The new model is represented in mathematical format to be easily used as empirical correlations. The new neural network model is compared with popular fluid property correlations. The results show that the developed model outperforms the fluid property correlations in terms of the average absolute percent relative error.