The drilling process can be broken down into various activities from top-level activities (e.g., drilling and tripping) to lower-level activities (e.g., in-slip, out-of-slip, making connection, and circulation). The detection of the fundamental drilling unit, a stand, is necessary and essential for recognizing and inferring drilling activities. A new method is proposed to detect slip status, pipe change, and drilling/tripping stands based on real-time streaming data.
The slip status is a critical element because it indicates a connection is made before drilling or tripping a stand. The proposed method is designed to infer the slip status with hookload, standpipe pressure (SPPA), and surface torque (STOR) sensor data. Specifically, the logic using hookload includes two criteria, a hookload standard deviation criterion and a dynamic hookload threshold criterion. This allows addressing the limitations of prior methods at shallow depth and using a manual threshold, which prevents the full automation of slip detection. In addition, the slip status can be confirmed or corrected with a logic using a combination of SPPA and STOR data. Then, a check is performed on whether a stand is added or removed during in-slip period. If needed, the stand detection can also be run to detect where a stand begins and ends.
The method has been extensively tested and validated on many land and deepwater wells with drilling/tripping operations. Without human intervention, the dynamic hookload threshold can be determined automatically and adaptively after one or two drilling or tripping stands. Moreover, the hookload standard deviation criterion works well to detect the change of slip status at shallow depth. It is shown that high accuracy of detection can be achieved when the streaming data have a proper range of sampling rate.
The new method addresses two limitations of the existing methods: (1) it automatically determines the dynamic hookload thresholds and eliminates the need of setting up the hookload threshold manually, and (2) it improves the accuracy of slip status and stand detection at shallow depth. This innovative work enables the automation of the slip status and stand detection process in batch runs or in real time without operator input.