Increasing Production Efficiency via Compressor Failure Predictive Analytics Using Machine Learning
- D. Pandya (Shell Global Solutions) | A. Srivastava (Shell Business Operations) | A. Doherty (Shell U.K. Limited) | S. Sundareshwar (Shell UK Ltd.) | C. Needham (Shell U.K. Oil Products) | A. Chaudry (Shell Global Solutions) | S. KrishnaIyer (Shell Business Operations)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 30 April - 3 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Offshore Technology Conference
- 4.1.4 Gas Processing, 1.6 Drilling Operations, 4.1 Processing Systems and Design, 4.1.6 Compressors, Engines and Turbines, 7.6.6 Artificial Intelligence, 6 Health, Safety, Security, Environment and Social Responsibility, 6.1.5 Human Resources, Competence and Training, 6.1 HSSE & Social Responsibility Management, 4 Facilities Design, Construction and Operation, 1.6 Drilling Operations
- Advanced analytics, Machine learning, Predictive Maintenance, Compressor
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Unintended loss of uptime (trips) in gas compression systems is one of the top causes for unscheduled deferment across hydrocarbon production facilities. Compression failures and the deferments they cause have been at similar levels for the past 5–10 years. Causes for compressor failures could be attributed to lack of or inappropriate maintenance, incorrect operating practices and integrity issues, as identified in the Oil and Gas UK compressor study. The focus of this research paper is on compressor systems on production facilities that have major production deferments associated with them. In this paper, an advanced machine learning approach is presented for determining anomalous behavior to predict a potential trip and probable root cause with sufficient warning to allow for intervention. One class support vector machines (SVM) and rate of change based outlier detection have been utilized to classify abnormal operation and detect the specific variables contributing to instability respectively. Development of the algorithms started with data from more than 2,000 sensors on the low-pressure compressor as well as processes tied to the compressor. This initial data set was reduced to more relevant tags by feature selection methodologies. Two separate, one class SVM models were then trained on one years normal working data to identify abnormal behavior considering the multivariant approach. An outlier detection algorithm was developed to identify and rank major contributors for potential faulty behavior of the compressor. The algorithms are trained and tested in R and a near real-time, online implementation is scheduled using Alteryx platform which provides new predictions every 10 minutes. The results are then visualized on a Spotfire dashboard and when initiated, the model flags abnormal behavior via automated email to the end user. At present, the algorithms have improved the identification efficiency with a mediam detection time of seven hours. With upper detection time as high as few days, investigation and remedial action is possible. As this field progresses and identification time increases, the application of machine learning for compressor failure has potential to revolutionize maintenance strategies and mitigate against the now-periodic downtime of compressors across the industry. Current efforts to identify anomalous compressor behavior and degradation of performance are limited to traditional exception based surveillance, using pre-determined limits and manual univariate observation of critical compressor variables. This paper presents application of scalable machine learning as more advanced methods of failure classification. We can now utilize the vast catalog of historical and real-time data to build smart algorithms allowing engineers to go beyond basic anomaly detection mechanisms. This predictive maintenance approach has huge potential to save production deferments caused by downtime associated with rotating equipment failures.
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