2022 - AVEVA PI World - Amsterdam
Enhance process reliability of Water Treatment System via multiple Machine Learning Based Analytics - ENI
Water Treatment System (WTS) is necessary to treat the seawater, as well as the produced water, in order to reach the required specifications allowing the blend of both waters to be injected into the reservoir. The objective of this paper is to present an integrated digital solution composing monitoring, predictive and advanced analytics tools that have been developed to enhance the performance and the overall availability of the WTS in one Floating Production Storage and Offloading (FPSO) unit. Exploiting the availability of data from our centralized Pi system we developed a suite of solutions including: a predictive models for fouling in Ultra Filtration units and Sulphate Reduction Package allowing the operators to predict the time left until the trains need to be cleaned; forecasting models for Produced Water Coolers heat exchanging coefficients and anomaly detection algorithm for Oil in Water sensor.
Industry
Infrastructure, Oil Gas and Energy
Company
ENI
Speaker
Giuseppina Tomei
Have 8+ of experience (80% in consulting firms) with a proven track record in development projects of advanced analytics and data modeling solutions to support business innovations.
Company
Eni S.p.A.
Speaker
Matteo Boscato
Statistician with 4+ years of international working experience mainly in the financial sector (European Central Bank and Credit Suisse), working on developing and implementing Machine Learning models to detect anomalies in time series data.