Estimation of bubble point pressure from PVT data using a power-law committee with intelligent systems

Bubble point pressure is the most crucial pressure–volume–temperature (PVT) property of reservoir fluid, which plays a critical role in almost all tasks related to reservoir and production engineering. Therefore, an accurate, quick, and easy way of predicting bubble point pressure from available PVT parameters is desired. In this study, an improved methodology is followed for making a quantitative formulation between bubble point pressure (target) and some available PVT data (inputs) such as proportion of solution gas–oil-ratio over gas gravity, temperature, and stock-tank oil gravity. At the first stage of this research, bubble point pressure was predicted from PVT data using different intelligent systems, including neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a power-law committee with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search tool. The proposed methodology, power-law committee with intelligent systems, comprises a parallel framework that produces a final output by combining the results of individual intelligent systems. To achieve this objective, a power-law formula structure was designated to integrate outputs of intelligent systems. A hybrid genetic algorithm-pattern search tool was then employed to find the optimal coefficients of this formula. A database of 361 worldwide data points was employed in this study, while 282 data points were used for model construction (i.e., training data), and 79 data points were employed to assess the reliability of the model (test data). Results showed that outputs of intelligent systems are in good agreement with reality. However, by little additional computation, power-law committee with intelligent systems is capable of significantly improving the accuracy of target prediction.

► In this study, we construct a hybrid model formulating PVT data to bubble point pressure (Pb).
► Neural network, fuzzy logic, neuro-fuzzy, and genetic algorithm constitute the framework of this hybrid model.
► We used an extension of committee machine (CM) concept to construct our model.
► It was shown CM improves the accuracy of target prediction through combination of intelligent systems.
► The proposed methodology provides an accurate fast and cost effective way for calculating Pb.