Conventional and machine learning improved prediction of hydrocarbon density using volume-translation at high-pressure high-temperature conditions

ABSTRACT

Non-cubic equations of state (nCEOS) are increasingly showing improved performance at predicting volumetric properties of hydrocarbons, nitrogen, and carbon dioxide at high-pressure high-temperature over volume-translation-based cubic equations of state (VT-CEOS). However, since nCEOS are rather complex, a less mathematically complex and more accurate CEOS is desired. Hence, in this study, we have explored different techniques, including conventional (non-linear regression) and machine learning-based approaches (random forest) to predict a more accurate molar volume deviation term of the VT-CEOS. We used an extensive high-pressure and high-temperature PVT dataset ranging from 50 to 150 MPa and 300 – 500 K respectively in this study. The VT was modeled as a function of reduced temperature only as well as reduced temperature and molecular weight/critical pressure of the pure hydrocarbon components.

Statistical analyses and graphs displayed high performance of the developed predictive models over existing VT-CEOS models applied to HPHT and PC–SAFT. More specifically, the machine learning model gave 99% accuracy while the accuracy of the conventional approach ranged from 60-98%. To the best of the knowledge of the authors, the application of machine learning to estimating volume-translation based on CEOS for pure hydrocarbon components of natural gas and heavy hydrocarbons is nonexistent. This paper presents the first application of physics-based machine learning and the use of features that honors thermodynamic principles for prediction of hydrocarbon density.

KEYWORDS:

• Natural gas
• cubic equation of state
• high-pressure high-temperature
• volume-translation
• machine learning

Additional information

Notes on contributors

Kale Barbara Orodu

Dr. Kale B. Orodu has practical industry experience in hydrocarbon fluid sampling and characterisation. She has B.Eng. (University of Port Harcourt, Nigeria), MSc. (Covenant University, Nigeria), & PhD (Covenant University) in Petroleum Engineering. Dr. Kale B. Orodu's research interests broadly encompass the development of models for characterising petroleum reservoir fluids.

Gerald Kelechi Ekechukwu

Gerald Ekechukwu received his B.Sc. and M.Sc. degrees in Petroleum Engineering from the University of Ibadan, Nigeria, and Imperial College London, respectively. He has been actively involved in research since 2016. He has broad research interests that encompass flow through porous media, applications of machine learning techniques, advanced characterization of reservoir fluids, and advances in in-well technology for well and reservoir surveillance.

Oyinkepreye David Orodu

Prof. Oyinkepreye D. Orodu has varied Oil & Gas industry experience spanning field operations and academia. He received his B.Eng. from the University of Port Harcourt, Nigeria in Chemical Engineering and, MSc. and PhD in Oil & Gas Engineering from Robert Gordon University, UK and China University of Geosciences respectively. Prof. Orodu's current research interest includes “stochastic decision analysis of optimal well utilization”, “Characterization and modelling of flow units”, „fluid characterisation with conventional and machine learning-enabled approach“, and „nanotechnology for improved oil recovery and drilling fluid enhancement”.