Gas/Oil Separator Optimization

摘要

The paper presents a novel Genetic-Algorithm/Neural Network based method for predicting and optimizing the performance of the multi-stage gas/oil separation plants (GOSP) in crude oil production. Two neural networks accept the initial and final pressures and temperatures of each stage and the oil composition information to predict the stage gas/oil ratio (GOR). On the other hand, the Genetic Algorithm (GA) searches for the optimal operating pressures and temperatures of the multistage gas/oil separation plant to achieve maximum oil recovery under operation constraints. The tools allow the plant engineers to continuously optimize the operation of the plant with the varying ambient temperatures to increase the economic return of the plant. The method can also be useful simulation tool in optimizing the planning, design and operation of oil production facilities.