Next-Generation Kick Detection During Connections: Influx Detection at Pumps Stop (IDAPS) Software
- Brian A. Tarr (Shell International Exploration and Production) | Douglas W. Ladendorf (Shell International Exploration and Production) | Diego Sanchez (Shell International Exploration and Production) | George M. Milner (CoVar Applied Technologies)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- December 2016
- Document Type
- Journal Paper
- 250 - 260
- 2016.Society of Petroleum Engineers
- Kick Detection, Connection Flow-back Monitoring
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- 340 since 2007
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At least 25% of all influx events on deepwater wells occur while making connections, but few deepwater rig contractors use kick-detection alarms to alert the driller during a connection (Fraser et al. 2014; Brakel et al. 2015). Because of the transient-flow characteristics associated with connections, kick detection during connections is the most challenging to automate effectively. The Influx Detection at Pumps Stop (IDAPS) software was developed to provide early warning of abnormal flowback conditions during connections. Available flow-in, flow-out, pit volume, bit depth, and hole-depth real-time data are used as input data. Particular attention was paid to achieving high probability of detection (PD) at low false-alarm rates (FARs) to minimize nuisance alarms, and fast influx-detection times to reduce kick volumes. The use of IDAPS to reliably detect a formation-fluid influx has improved safety, operational efficiency, and driller situational awareness. IDAPS has been deployed in an operator’s real-time operations center for monitoring critical offshore wells since 2014.
During IDAPS operation, pumps-off occurrences are automatically detected from the ramp-down of pump strokes, and saved as unique events. Machine-learning algorithms are applied to recent pumps-off event flow-out and pit-volume data patterns to adaptively calculate limits for “normal” events. The adaptive nature of these limits allows IDAPS processing to adjust to changes such as increasing hole depth. Each new pumps-off event is evaluated in real-time, and statistically meaningful deviations from the recent“normal” limits generate corresponding possible influx notifications at one of four confidence levels (low, medium, high, and confirmed). In addition, on the basis of data-pattern-recognition algorithms, the software detects and notifies the user of circulation-system data-validity issues that could otherwise impair influx-detection performance [e.g., a malfunctioning flow sensor (including sticking of the commonly used flow-out paddle-style flow sensor)], inconsistent pit volume gains, and others. Overlay plots of current and historical flow and pit-volume data have been shown to be valuable in significantly reducing the time required by the user to validate anomalous pumps-off event data automatically identified by IDAPS.
On the basis of an extensive validation process, that included more than 1,300 historical pumps-off events, the demonstrated FAR for IDAPS was 1 per 195 connections with a 100% influx detection rate, with an associated confirmed influx-detection time as fast as 84 seconds after pumps stopped.
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