In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circula-tion accidents during emergency rescue drilling of mine,a machine learning-based early for warning and prediction model of drilling process was established.Firstly,the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed.Secondly,the accident characterization parameters were cleaned and processed.On this basis,XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents.Then,the PSO-LSTM accident development prediction model was established to predict the development trend of downhole pressure parameters after the accident,so as to understand the development status of drilling accidents in advance.Finally,the effectiveness of the early warning prediction model was verified by the actual drilling data.The results show that XGBoost and early warning model could quickly and ac-curately diagnose the kick and loss circulation accidents in the drilling process according to the abnormal changes of four drilling parameters,including the total tank volume,riser pressure,inflow-outflow differential,and power head load.The PSO-LSTM accident development status prediction model could fully learn the development law of downhole pressure parameters.With full consideration to the four error evaluation indicators,such as EMAP,EMA,ERMS and R2,the prediction performance of the PSO-LSTM models is the best compared with BP,RNN and SVM,capable of accurately predicting the development trend of the downhole pressure after the accident,thereby knowing about the severity and development situation of kick and lost circulation accidents.Generally,the research results enrich the early warning and prediction methods of kicks and lost circulation accidents in the drilling process,improve the reliability of surface rescue in mine accident,and have a reference and guiding effect on accident control during the emergency rescue drilling of mine.
关键词
矿山救援/钻进过程/预警预测/XGBoost/PSO-LSTM/机器学习/井涌井漏
Key words
mine accident rescue/drilling process/early warning and prediction/XGBoost/PSO-LSTM/machine learn-ing/kick and lost circulation
维普
万方数据