The Use of Artificial Neural Networks and Genetic Algorithms for Effectively Optimizing Production from Multiphase Flow Wells

Bottom-hole pressure (BHP) and separator pressure (SEPP) are playing an important role in defining the general fashion of production from upstream and downstream systems. The need for accurate prediction of these parameters is a key factor in clearly understanding multiphase flow in tubing. Prediction of pressure drop in multiphase flow is quite difficult and complicated due to the complex relationships between the various parameters involved. As they considered very hard obtaining parameters, bottom-hole pressure and separator pressure are selected for prediction using Artificial Neural Networks. The latter will be utilized in attempt at this study to generate a generic model for predicting bottom-hole and separator pressures in multiphase flow tubing that accounts for all angles of inclination. Artificial Neural Networks provide an easy and trustable means for predicting these parameters with high degree of confidence. Moreover, the output from the ANNs will be utilized plus selected other input parameters as controlling variables for optimizing the production from a multiphase producing field using Genetic Algorithms (GA).