A theoretical framework for data-driven artificial intelligence decision making for enhancing the asset integrity management system in the oil & gas sector

Abstract

Asset integrity and reliability is one of the 20 elements of Process Safety Management (PSM) as defined by the Center for Chemical Process Safety (CCPS). We combine expert knowledge and data analytics (Artificial Intelligence, Machine Learning, and Keyword Analysis) to create a reaction network for Asset Integrity Management (AIM) and provide a theoretical and practical basis for handling uncertainty in large data sets such as company incident databases. The purpose of the current study is to control and minimize the total number of incidents that occur within an oil and gas operation by applying a multidisciplinary approach to explore and develop AIM. This systematic approach can improve AIM to better understand PSM as a whole and the underlying dynamics ever-present in the system. In this study, AIM is divided into 2 major groups – asset and human factors – and then, in order to get more detailed results, each group is divided into 9 and 5 subcategories, respectively. To analyze the relationships between the different factors of AIM, two score-based (Tabu and Hill Climbing) and one hybrid (Max-Min Hill-Climbing) Bayesian networks are used to develop one final viable solution. The findings of these techniques point towards the same results for reducing incident rates. Four factors related to assets, including construction, testing, inspection, and maintenance, account for more than half the incidents (54.78%). Additionally, there must be a greater emphasis placed on the impact of human factors as they are directly (23.58%) and indirectly (11.10%) responsible for accidents as well as other technological malfunctions. By focusing on AIM which is a key element of PSM, it will be possible to gain a better understanding of one of the most significant and problematic sources of risk in process safety.