An Automated Kick Alarm System Based on Statistical Analysis of Real-Time Drilling Data

Abstract

Abstract Early influx/kick detection has been one of the most important focus areas for drilling risk management because it enables the crew to take corrective actions that can minimize the danger and cost associated with the event. This work presents an automated influx alarm system that can analyze real-time drilling data using a statistical approach and raise early kick alarms of different levels. 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 three indicators: normalized rate of penetration (D-exponent), flow rate, and mud pit volume. An overall kick index is then calculated as a weighted summation of the variations in three kick indicators. An alarm is triggered after the calculated kick index surpasses a preset threshold. This automated system is tested against historical drilling data from 15 wells, of which a total of 23 kick events are recorded. The results show that all actual kick events are captured at an average of 8.5 minutes before the recorded time. The ultimate goal of this work is to apply such a highly automated system for early kick detection from real-time drilling data, without requiring additional downhole sensors or expensive flow meters.