Automatic Slip Status and Stand Detection in Real-Time Drilling
- Jie Zhao (Schlumberger) | Sylvain Chambon (Schlumberger) | Yuelin Shen (Schlumberger) | Sai Venkatakrishnan (Schlumberger) | Mohammad Khairi Hamzah (Schlumberger)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 6-9 May, Houston, Texas
- Publication Date
- Document Type
- Conference Paper
- 2019. Offshore Technology Conference
- 1.6 Drilling Operations, 1.10 Drilling Equipment, 1.10 Drilling Equipment
- Pipe Change, Real-Time Drilling, Drilling Inference, Slip Status Detection, Stand Detection
- 3 in the last 30 days
- 274 since 2007
- Show more detail
- View rights & permissions
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.
|File Size||1 MB||Number of Pages||14|
Andersen, K., Sjowall, P. A., Maidla, E. E.. 2009. Case History: Automated Drilling Performance Measurement of Crews and Drilling Equipment. Presented at the SPE/IADC Drilling Conference and Exhibition, Amsterdam, The Netherlands, 17–19 March. SPE-119746-MS. https://doi.org/10.2118/119746-MS
G.L. de Oliveira, G., Zank, C. A. C., Costa, A. F. . 2016. Offshore Drilling Improvement Through Automating the Rig State Detection Process – Implementation Process History and Proven Success. Presented at the IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, USA, 1–3 March. SPE-178775-MS. https://doi.org/10.2118/178775-MS
El Afifi, S., Albassam, B., Fahmy, F. A.. 2015. Enhance the Drilling & Tripping Performance on Automated Rigs with fully automated performance measurement. Presented at the SPE Middle East Intelligent Oil & Gas Conference & Exhibition, Abu Dhabi, UAE, 15–16 September. SPE-176786-MS. https://doi.org/10.2118/176786-MS
Hegde, C., Awan, O., Wiemers, T. 2018. Application of Real-Time Video Streaming and Analytics to Breakdown Rig Connection Process. Presented at the Offshore Technology Conference, Houston, Texas, USA, 30 April–3 May. OTC-28742-MS. https://doi.org/10.403/28742-MS
Lakhanpal, V. and Samuel, R. 2017. A New Approach to Harness Data for Measuring Invisible Lost Time in Drilling Operations. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 9–11 October. SPE-187270-MS. https://doi.org/10.2118/187270-MS