2019 - OSIsoft PI World Gothenburg - Oil & Gas
ENI - PI System to Big Data Analytics: Data Driven reduction of upstream environmental impact
This paper highlights the extension of PI analytics functionalities taking advantage of the power of Machine Learning and Big Data Analytics. These techniques have been applied to the forecast and reduction of the energy consumption in an upstream plant. PI system features such as high availability, smart data interpolation and cleansing capabilities are leveraged along the overall pipeline of any machine learning product development: exploration, training and deployment. The final model is deployed in the Big Data Infrastructure and fed with near real time data. This solution achieves two main results: a reduction of co2 emissions and a smooth transition for experienced plant operators towards advanced Analytics solutions and data science techniques in the Oil & Gas Business.
Lorenzo Lancia is a Data Scientist at the Eni.
He received a BSc in Physics and a MSc in Data Science from La Sapienza, University of Rome, Italy working on developing an unsupervised anomaly detection tool for identifying upset in a computer network.
He is currently working on developing Machine Learning and Data Science solutions in the Oil & Gas Business with focus on predictive maintenance for Upstream plants.
A fast learner and ambitious Production Engineer with a strong background in Nuclear and Energy Engineering.
I worked for two years in Eni Headquarter in production department with focus on energy efficiency and digital transformation. Now I work on a O&G plant as production engineer, giving my strong contribution to enhance energy efficiency and the use of digital solution.