A Surrogate Integrated Production Modeling Approach to Long-Term Gas-Lift Allocation Optimization

摘要

The conventional static gas-lift allocation optimization approaches are not appropriate for long-term gas-lift projects. A good choice for long-term optimization should predict gas-lift performance dynamically as a function of production time and other variables. A good solution approach for problem is a hybrid of surrogate integrated production modeling and genetic algorithm (GA). Hybrid GAs have received significant interest in recent years and are being increasingly used to solve real-world problems. GA incorporates other techniques within its framework to produce a hybrid that reaps the best from the combination. This study discusses a new method known as surrogate integrated production modeling that uses an artificial neural network to predict gas-lift performance based on a database of oil production. Then, a hybrid of the neural network and GA is used for long-term gas-lift allocation optimization in a group of wells under real constraints.