Advances in Real-Time Event Detection While Drilling

Abstract Accurate, quick, and reliable event detection is essential to both conventional and automated drilling processes. Methods for real-time detection of events using Bayesian inference have been described previously. These systems were initially designed to provide alarms to rig crews with little or no operator setup or maintenance. These requirements have resulted in certain limitations that restrict their use to relatively simple situations. For example, the drillstring washout detection method was based primarily on the behavior of hydraulic coefficients of a simple hydraulic model computed from standpipe pressure and flow, which meant that it was limited to only scenarios of drilling without a mud motor. New sensors and enhancements to the Bayesian inference techniques significantly improve event detection. Although applicable to a broader set of events, improvements to these detection methods can be explained using the example of a new drillstring washout detection module. The new module combines a method of detection of on-bottom drilling with enhanced signal characterization of standpipe pressure and flow to create an algorithm that can effectively handle drilling with a motor. Another improvement in the washout detection algorithm is the use of MWD turbine RPM when it is available. Unaffected by the differential pressure and depth changes, the turbine RPM data have proven to be more effective in detecting washouts occurring in the drillstring above the MWD tool. Testing both on data from several previously recorded washouts in drillstrings and on real-time data from wells with washout demonstrates the new capability of the algorithm to detect these difficult-to-characterize events while keeping false detections to a minimum.