Keywords: Prudhoe Bay, neural networks, surface facility, oil production optimisation, Alaska, gas transit pipelines, pipeline networks, production separation, central gas compression, gas discharge rates, gas discharge pressures
Building the foundation for Prudhoe Bay oil production optimisation using neural networks
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 development builds the foundation for building a tool to maximise total field oil production by optimising the gas discharge rates and pressures at the separation facilities.