Early Detection of Well Control Kick Events by Applying Data Analytics on Real Time Drilling Data

Abstract

Abstract Kick events are some of the most life-threatening and environmentally disastrous events during drilling. Identifying potential kick events in time is valuable. Several hybrid algorithms combining physics and data analytics have been developed to help identify potential kick events from trends in real-time drilling data. These algorithms encompass all drilling operations like drilling, tripping, circulating, and making connections. The goal is to enable management by exception in real-time monitoring of wells. Real-time drilling data acquired from the sensors on the rig and other static metadata like drill string and casing specifications are used in these hybrid algorithms. The exact data channels from the real-time data used differ based on the drilling operation the algorithm is associated with, but the most important ones are flow rate in (FLOWIN), flow out percentage (FLOWOUT), pump strokes per minute (SPM), active pit volume (PITACT), and trip tank volumes (TTKVOL). Additional data channels like lag depth and rig states are computed from these existing channels to help the algorithms. Other provisions have been made in the algorithms to automatically account for human operations like lining up with trip tanks and addition/removal of pits from active pit volume. To identify the potential kicks: data from trip tank volumes are utilized while tripping, flowback signatures calculated from PITACT are used during connections, and real-time hole displacement (HDISP) calculated from PITACT and lag depth is used while drilling. All the algorithms have been parameterized to facilitate easy tuning of thresholds. An alerting system has been implemented that triggers an alarm when the algorithms identify a potential kick event. This system can also send notifications to a real-time support team or field personnel. Historical offset wells provided by an operator were used for testing the algorithms and fine-tuning the thresholds. We achieved a 12% false-positive rate while correctly identifying all the true well control kick events. False positives are defined as events identified by the algorithms as potential kicks but were due to some explainable operation that was accounted for in the algorithm scope. The user feedback from the alerting system was also used to improve the accuracy of the algorithms. The calculated data channels used to identify the potential kicks can also be displayed as real-time traces or in other suitable visualizations like a trip sheet or flowback fingerprints. These algorithms were designed to be run with minimal user input. This makes them suitable for use by real-time support centers and field personnel. The ability to calculate the lag depth and incorporate that into the analysis is novel and improves the accuracy. The provisions in the algorithm to account for human operations that affect pit volumes also add to the novelty.