Riccardo Borghi — Master Thesis Abstract

An Oil&Gas production system is based on the pressure balance between the reservoir and the delivery point. Since a deviation of the flowing conditions in a single point of the production system has consequences in the entire asset, a big challenge for flow assurance engineers is to preserve a stable pressure in the system, with a particular attention at the separator level.

Given that the description of pressure oscillations in the systems provided by the available simulators, based on first principle, is not always enough accurate and reliable as needed for practical applications, it is fundamental to develop models and tools to help plant operators in understanding the phenomenon and properly managing it. In this context, the objective of the present thesis work is to use large datasets, containing historical measurements of hundreds of plant signals, for identifying the most critical components of the production system with respect to the pressure oscillation phenomenon and extracting knowledge from it.

To this aim, a novel indicator of the intensity of the pressure oscillation phenomenon in the separators has been firstly defined combining Discrete Short Time Fourier Transform and Principal Component Analysis. Then, a method to extract information on the causes of the oscillation phenomenon has been developed. It is based on (i) the prioritization of the plant signals importance with respect to the pressure oscillation by using the Maximum Information Coefficient and the moment-independent Kolmogorov-Smirnov distance; (ii) the aggregation of the signal for the identification of the most critical plant components; (iii) the extraction of rules describing the phenomenon by developing a Classification And Regression Tree model whose inputs are the most important signals in (i).

This method has been verified considering a production plant operated by Eni S.p.A.  where the oscillations in the separator’s pressure are frequent. The results show the effectiveness of the developed indicator of the intensity of oscillation and provide hints about the physical causes.

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