Time series data analysis for automatic flow influx detection during drilling

Automatic and early detection of flow influx during drilling is important for improving well-control safety. In this paper, a new method that can automatically analyze real-time drilling data and detect the flow influx event is 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 parameter group (FPG), respectively. Additionally, two real-time trend-analysis methods, the divergence of moving average (DMA), and the divergence of moving slope average (DMSA) are applied to quantify trend evolutions of the two indicators. The kick event is identified based on the anomalous trends held by the two kick indicators. A final kick-risk index (KRI) is calculated in real time to indicate the probability of kick events and to trigger the alarm. The method is tested against four offshore kick events. With KRI threshold setting as 0.8, the average detection time is 64% less than the reported detection time. The application of DPG kick indicator allows the early kick detection without additional downhole sensors or costly flow meters.