AccuPipePred: A Framework for the Accurate and Early Detection of Stuck Pipe for Real-Time Drilling Operations
- Arturo Magana-Mora (Drilling Technology Team, EXPEC ARC, Saudi Aramco) | Salem Gharbi (Drilling Technology Team, EXPEC ARC, Saudi Aramco) | Abrar Alshaikh (Drilling Technology Team, EXPEC ARC, Saudi Aramco) | Abdullah Al-Yami (Drilling Technology Team, EXPEC ARC, Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 7.2.1 Risk, Uncertainty and Risk Assessment, 6 Health, Safety, Security, Environment and Social Responsibility, 1.10 Drilling Equipment, 6.1.5 Human Resources, Competence and Training, 1.6 Drilling Operations, 6.1 HSSE & Social Responsibility Management, 7.2 Risk Management and Decision-Making, 7 Management and Information
- stuck pipe, real-time, automation, machine-learning
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Thorough preplanning and best drilling practices are effective in reducing stuck pipe incidents, data analytics offer additional insight into further reducing the significant non-productive time (NTP) that results from this unplanned event. The severity of the stuck pipe problem may stop the drilling operations for a short time, or in more difficult cases, the drill string has to be cut and the borehole is sidetracked or plugged and abandoned. Consequently, detecting the early signs of this problem, in order to take the right actions, may considerably or entirely reduce the risk of a stuck pipe.
Although computational models have been proposed for the early detection of the stuck pipe incidents, the models are derived from a reduced set of wells with stuck pipe incidents, which may result in under-trained models that predict a large number of false positive alarms. A sufficient amount of data or wells that statistically represent the parameters surrounding stuck pipe incidents under different circumstances is required in order to derive a generalizable and accurate prediction model. For this, we first derived a framework to automatically and systematically extract relevant data from the historical data. As such, our framework searches through the historical data and localizes the surface drilling and rheology parameters surrounding the stuck pipe incidents. Moreover, we performed feature selection by selecting the top-ranked parameters from the analysis of variance, which measures the capability of the drilling and rheology parameters to discriminate between stuck pipe incidents and normal drilling conditions, such as, weight on bit, revolutions per minute, among others.
Using the relevant features selected by the analysis of variance, we derived a robust and fast classification model based on random forests that is able to accurately detect stuck pipe incidents. The implemented framework, which includes the automated data extraction module, the analysis of variance for feature selection, and prediction, is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew in order to minimize or avoid the NTP due to a stuck pipe.
|File Size||1 MB||Number of Pages||10|
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