Real-Time Drilling Models Monitoring Using Artificial Intelligence
- Bader Alotaibi (Saudi Aramco) | Beshir Aman (Saudi Aramco) | Mohammad Nefai (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
- 1.6 Drilling Operations, 7.6.6 Artificial Intelligence, 1.12.6 Drilling Data Management and Standards, 3 Production and Well Operations, 4.3.4 Scale, 3 Production and Well Operations, 1.12 Drilling Measurement, Data Acquisition and Automation
- Big Data, WITSML, Machine Learning, Real-Time Drilling Data, Models Monitoring
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- 306 since 2007
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|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
In recent years, the Drilling and Workover (D&WO) operations are growing significantly. The growth of active operations required and produced more data from D&WO operations. With very large number of rig activities daily transmitting more than 60,000 real-time data points every second, it became necessary to understand and utilize this Big Data in order to predict drilling troubles and discover hidden knowledge. The adaption of the industrial Revolution (IR) 4.0 contributed to the use of advanced and novel approaches such as Artificial intelligence (AI) and Machine learning (ML) models. However, those models require continues improvement as drilling data change. When using the industrial standard and adapted Wellsite Information Transfer Specification Markup Language (WITSML) based Big Data environment, the task to monitor the performance of a model at a large scale becomes challenging due to common reasons such as a large number of wells, different models being deployed and different data stored in different systems.
In this paper, a new approach is introduced using WITSML based Big Data environment. The methods employed utilize an advanced engine to monitor and evaluate active AI/ML models at a large scale. The engine utilizes anomaly detection methods to monitor abnormal behaviors of the models such as sudden high rate of alerts per day/well or a sudden drop in true event detection. The paper will also demonstrate how such technology can help in early detection of model's decay signs or sudden changes in real-time data quality.
The solution improved and automated the process of monitoring and maintaining of AI/ML models in the Drilling domain. It also made the decay detection of models possible and showed how models improve when iterative enhancements are deployed.
|File Size||1 MB||Number of Pages||10|
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