Intelligent early warning model and intelligent reasoning method based on integrated learning for loss circulation

The optimal and fast drilling techniques for horizontal wells has accelerated the large-scale development of tight oil and gas in Changqing Oilfield. However, due to the development of formation pores and micro-fractures in some areas, the pressure bearing capacity of the formation is low, and the risk of lost circulation is extremly high, which seriously restricts the speed and efficiency when drilling horizontal wells. In order to further reduce the risk of loss circulation, and improve the timeliness and accuracy of loss circulation warning, when lost circulation occurs, a method for intelligent early warning and reasoning under the constraint of causality was proposed. Based on the mechanism of loss circulation, the characteristic parameters of loss circulation risk and their changing rules are analyzed, which are used as input parameter constraints to accurately locate the occurrence time of loss circulation by using the working condition identification model and characteristic changing rules. Based on BP neural network and long-short-term memory network (LSTM), a risk early warning model for loss circulation was established, and the causes of the early warning were explained by using the causal inference algorithm, realizing warning constraint combining with the risk mechanism. The results show that the early warning accuracy rate of the LSTM integrated network on loss circulation reaches 95.6%, and the intelligent reasoning method based on integrated learning can accurately explain the causes of the early warning, which is of great significance for timely adopting measures to prevent loss circulation at the drilling site, and of great significance for ensuring the optimal and fast drilling of horizontal wells.