Wang Zhi

Application of an intelligent early-warning method based on DBSCAN clustering for drilling overflow accident

Oil and gas are still the necessities of production in today’s society. However, the exploration and mining of them are extremely complex and dangerous. Overflow accidents are undoubtedly one of the biggest threats to safe drilling operations during the oil and gas exploration. Due to the complexity of geological information or lack of adjacent well data in drilling process, the problem of overflow warning model based on sample information can not be established. Data mining is the process of revealing meaningful new patterns, relationships and trends by analyzing data, therefore, based on the correlation between the occurrence of overflow accidents and the change trend of casing pressure, a method of intelligent warning based on improved DBSCAN clustering method for drilling overflow accidents is proposed. The early warning method uses time-series scanning and stratification to rule the idea of clustering, not only improve the efficiency of clustering, but also enhance the clustering effect. According to the results of clustering fitting and the sensitivity of overflow accident, output the warning result of overflow accident. The data analysis is made by using the field data. The experimental results show that the flood warning method based on improved DBSCAN clustering can effectively predict the overflow accidents.