Intelligent Operation and Maintenance of Oil-Gas-Water Multiphase Pipelines: A Hybrid Modeling Approach

In the mid to late stages of oilfield development, the frequency of incidents and leaks in oil-gas-water multiphase pipelines poses significant risks. Traditional manual pipeline operations face considerable challenges, necessitating the adoption of intelligent technologies. This study employs a hybrid modeling approach, combining mechanistic models with data-driven AI models. Using a novel fusion method, we create a hybrid model incorporating a genetic algorithm-optimized Simpson formula for mechanistic modeling and a Long Short-Term Memory (LSTM) neural network to address the challenge of soft flow measurement in multiphase pipelines. Further, a hybrid model comprising Random Forest (RF), Gradient Boosting Trees (XGBoost), and Artificial Neural Network (ANN) achieves intelligent and efficient operation and maintenance of oil-gas-water multiphase pipelines. On-site validation in the HXL oil-site demonstrates the hybrid model's high accuracy in predicting pipeline flow (93.7% accuracy, average error of 2.89 m
³
/d). The model also exhibits a diagnostic accuracy exceeding 90% for incidents such as pipeline blockages, leaks, and ruptures, with a leak location detection precision surpassing 90%. This study highlights the significant and superior performance of a hybrid modeling approach, establishing it as a crucial technique for intelligent operation and maintenance of oil-gas-water multiphase pipelines.