Research on Intelligent Diagnosis and Decision-Making Method for Oilfield Water Injection System Faults

Water injection is a commonly used development method in oilfields. Water injection systems are large and complex, with horizontally and vertically interconnected pipeline networks often buried underground. Faults in pipeline networks cannot be detected and handled in time, thereby posing significant safety hazards to production. This study focuses on fault diagnosis and decision-making within a water injection system. We established a fault tree for an oilfield water injection system and proposed an optimized BP neural network with a Self-Adaptive Differential Evolution Algorithm for the first time. This method constructs a two-layer fault diagnosis model for a water injection system. The model diagnosed fault positions and types based on parameters such as the fault point flow, pressure, and pipeline flow. Compared with the traditional BP algorithm, this algorithm has better diagnostic accuracy and faster convergence speed. Simultaneously, the decision tree CART method was employed to classify decision types based on multiple parameter indicators of the fault points and generated decisions. We designed and implemented a fault diagnosis and decision platform for an oilfield water injection system. Finally we built an experimental pipeline network model with EPANET to simulate the system fault conditions. The diagnostic performance of the proposed algorithm was tested. The results showed that the proposed method achieved a 99% accuracy rate in diagnosing faults in a water injection system. This method significantly improves the scientific management of water injection systems, holding great potential for broad application and value in achieving smart oilfields.