Developing a Formula Based on a Hybrid Neural Genetic Algorithm for the Prediction of Minimum Miscibility Pressure

Abstract

Minimum miscibility pressure is a fundamental parameter in miscible injection projects. Experimental determination of minimum miscibility pressure is very costly and time-consuming; therefore, attempts have been made to utilize artificial neural networks for determination of minimum miscibility pressure. Despite the wide range of applications and flexibility of artificial neural networks, design and structural optimization of neural networks is still strongly dependent upon the designer's experience. To mitigate this problem, this article presents a new approach based on a hybrid neural genetic algorithm to determine the minimum miscibility pressure for pure and impure carbon dioxide injections. Then, equations for minimum miscibility pressure prediction by using the optimize weights of network have been generated. With the formulas obtained, the user may use such results without running the artificial neural network software. The new model yielded the accurate prediction of the experimental slim-tube carbon dioxide minimum miscibility pressure with the lowest relative mean squared error and average absolute errors among all tested carbon dioxide minimum miscibility pressure correlations.