Hazardous Events Prevention and Management Through an Integrated Machine Learning and Big Data Analytics Framework

Abstract This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to tackle and manage both major upsets that would lead to significant inefficiency and loss. The tool is developed for complex upstream production system, where upset could be caused by a huge amount of heterogeneous factors, exploiting data driven monitoring systems to identify the weak signals of the upcoming events. The framework proposed is mainly composed by a strong pipeline divided in 3 modules operating before (predictive phase), during and after the event. The former aims to reduce the probability of an event, the second works on the severity and the third one has a dual function: reporting upsets and feedback gathering system to be used to further improve the analytics implemented. The Predictive component alerts operators when it recognizes a dangerous pattern among the parameters considered. The other two components can support this one and can be exploited to detect early signs of deviations from the proper operating envelope, while predictive performances are not satisfying. Moreover, during an event occurrence, operators can promptly identify the causes of the upset through the entire production system. This allows a faster reaction and consequently a significant reduction in magnitude. The solution proposed provides 2 complementary methodologies: an agnostic anomaly detection system, helping to map plant functional unit anomalous behavior, as a dynamic operating envelope, and identifying the most affected ones; A real time root-cause analysis, as a vertical solution, obtained learning from the monitoring of the different specific functional unit; The tool is also able to provide an automatic event register using information provided by the root-cause system, including operator feedbacks that will improve the performances of each module of the framework. The entire pipeline developed has been applied on-line, working with real time data coming from an operating oilfield, with special focus on blowdown and flaring system. The robust architecture generated is able to overcome some main issues related to the complexity of Upstream production assets such as lack of data, quick dynamic of physical phenomena analysed and randomness of upsets. The first test demonstrates that the tool accuracy allows to identify and suggest actions on 35% of the most dangerous flaring events occurring. Moreover, the effectiveness increase significantly proving a real time root-cause analysis considering both strong and weak signals that cause dangerous overpressures through the treatment plant.