Prudhoe Bay Oil Production Optimization: Using Virtual intelligence Techniques, Stage One: Neural Model Building

摘要

Field data from the Prudhoe Bay oil field in Alaska was used to develop a neural network model of the cross-country gas transit pipeline network between the production separation facilities and central gas compression plant. The trained model was extensively tested and verified using 30% of the data that was not used during the training process. The results show good accuracy in reproducing the actual rates and pressures at the separation facilities and at the gas compression plant. The correlation coefficient for rate and pressure were 0.997 and 0.998 respectively. This is the first phase in the development of a tool to maximize total field oil production by optimizing the gas discharge rates and pressures at the separation facilities. The second phase, development of a state-of-the-art genetic algorithm to perform the optimization, is currently being tested and will be the subject of a future paper.