Automatic and early detection of flow influx during drilling is important for improving well-control safety. Inthis paper, a new method that can automatically analyze real-time drilling data and detect the flow influx eventis presented. The new method combines the physics-based dimension reduction and time-series data mining approaches. Two kick indicators are defined, representing the drilling parameter group (DPG) and flow parametergroup (FPG), respectively. Additionally, two real-time trend-analysis methods, the divergence of movingaverage (DMA), and the divergence of moving slope average (DMSA) are applied to quantify trend evolutions ofthe two indicators. The kick event is identified based on the anomalous trends held by the two kick indicators. Afinal kick-risk index (KRI) is calculated in real time to indicate the probability of kick events and to trigger thealarm. The method is tested against four offshore kick events. With KRI threshold setting as 0.8, the averagedetection time is 64% less than the reported detection time. The application of DPG kick indicator allows theearly kick detection without additional downhole sensors or costly flow meters.

Feifei Zhang is currently a professor at Yangtze University, China. He was a senior professional technologist in the R&D department at Halliburton, which is one of the world's largest oil field service companies. His main research interests isintelligent drilling and drilling automation, which is to integrate the artificial intelligence technology with the oil and gas domain knowledge and develop automation models for the drilling operations. He has authored or coauthored more than 18 technical papers and filed10patentapplications. He is currently the associate editor of SPE Drilling & Completion, which is one of the top journals in the energy industry. He also serves as technical reviewer for several other journals. He holds a PhD degree in petroleum engineering from the University of Tulsa.