Early Risk Warning Model of Complex Systems for Oil and Gas Production

In order to effectively prevent the leakage incident during complex offshore oil and gas drilling, an intelligent early risk warning model is proposed based on the fusion of multiple indicators. The risk mechanism of the drilling lost circulation is established to analyze the risk influence factors of the lost circulation and its early warning characterization parameters. Such a model is based on neural network algorithm to extract the weight of early warning indicators. The normal and abnormal drilling states are identified through adaptive thresholds, and the risk probability of lost circulation is estimated by a multi-indicator fusion algorithm based on Bayesian reasoning. A case study with focusing on Ledong gas field in the Yinggehai Basin is adapted to illustrate the feasibility of the proposed approach. The results show that the application of this method in a well exploration practice can improve the early warning rate of process anomalies and greatly facilitate the early detection and prevention of complex leakage conditions.