Chain-based machine learning for full PVT data prediction

Journal of Petroleum Science and Engineering(2022)

Cited 9|Views3
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Abstract
Building machine learning (ML) models based on pressure-volume-temperature (PVT) data is of paramount importance to capture trends and predict fluid behavior in a very heterogeneous and highly nonlinear thermodynamic system. PVT samples stored in an oil company database are often not complete and might be missing properties; both black oil and compositional. Before delving into building optimized fluid models, it is required to have a clean and structured PVT database complete with all the required properties. We present multiple novel algorithms developed to accurately predict a complete set of black oil and compositional properties within a PVT database.
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Key words
PVT properties,Machine learning,Exploratory data analysis,Clustering,Chain approach,Compositional delumping,Splitting
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