Artificial Neural Network Model for Risk-Based Inspection Screening Assessment of Oil and Gas Production System
- Andika Rachman (University of Stavanger) | R. M. Chandima Ratnayake (University of Stavanger)
- Document ID
- International Society of Offshore and Polar Engineers
- The 28th International Ocean and Polar Engineering Conference, 10-15 June, Sapporo, Japan
- Publication Date
- Document Type
- Conference Paper
- 2018. International Society of Offshore and Polar Engineers
- machine learning, artificial neural network, integrity management, maintenance, Risk-based inspection, lean, oil and gas
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- 89 since 2007
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Screening assessment is a part of risk-based inspection (RBI) methodology, which enables the filtering of equipment that makes a significant contribution to the overall risk of the system. The screening assessment directs detailed assessment to focus on higher-risk items; thus, resources (e.g., time and labor) can be allocated more effectively and efficiently. However, performing RBI screening assessment requires considerable time and resources, and its results are prone to high variability, due to its qualitative nature. To mitigate the aforementioned challenges and to make the knowledge work involved lean (i.e. to minimize waste), an artificial neural network (ANN) model is suggested for performing the RBI screening assessment. The development of the ANN model is demonstrated by using a dataset containing the data and information from an RBI assessment conducted for onshore and offshore hydrocarbon production and process systems. It is revealed that the suggested model is capable of achieving respectable performance with 90.65% accuracy, 82.76% precision, and 76.59% recall.
The advancement of inspection and maintenance strategies from a conventional calendar-based approach to more sophisticated plans, such as condition-monitoring, reliability-centered maintenance, and risk-based approach, has been driven by the increase in the number, size, and complexity of physical assets (Khan and Haddara, 2003). These advanced strategies help organizations to maximize the availability and reliability of the equipment, while still ensuring that operations are safe for the personnel, society, and environment. One of these strategies is risk-based inspection (RBI), which shifts the inspection planning paradigm from time-based inspection planning to more proactive inspection planning, by using a risk-based approach (Tan, Li, Wu, Zheng, and He, 2011). RBI allows organizations to focus their inspection resources on equipment with a high risk of failure, thus preventing over-inspection of lower-risk equipment and underinspection of higher-risk equipment (Chang, Chang, Shu, and Lin, 2005).
A generic RBI planning process is presented in Fig. 1. For detailed information and an explanation regarding RBI methodology, refer to the API RP 580 (2016b) and DNV GL RP-G101 (2017). Normally, there are two types of risk assessment in an RBI planning process: (1) screening assessment and (2) detailed assessment. Screening and detailed assessment differ in terms of the level of detail in estimating the equipment's risk level. Screening assessment typically utilizes qualitative analysis, which has a simpler procedure, fewer data requirements, and faster analysis time than the quantitative approach. Meanwhile, detailed assessment typically uses a quantitative approach that requires more data and has a more rigorous computation and analysis procedure than qualitative analysis. Computer software is commonly used to generate outputs related to detailed assessment.
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