Step-by-step algorithm for creating and tuning a PVT model for a reservoir hydrocarbon system

Abstract

The reservoir fluid PVT model is necessary to all types of hydrodynamic modelling (field development, well flow, well test, basin modelling, etc.). The PVT model, when not properly tuned, can result in significant inaccuracies in calculating PVT properties and field production of volatile oil and gas-condensate systems. The process of tuning the reservoir fluid PVT model is a complex and time-consuming task. Various methods, such as regression and machine learning (ML), have been employed for reservoir oil PVT model tuning; however, a definitive approach has not yet been identified. This paper introduces a novel and efficient step-by-step approach for developing and tuning reservoir fluid PVT which enables engineers to tune PVT models much faster than before. The new proposed approach can assist in the initialisation of a PVT model by employing effective methods for initial data pre-processing. Furthermore, it can accurately reproduce the results obtained from field measurements and basic laboratory studies conducted on representative samples, in a model using a cubic equation of state. Tuning the PVT model enables the reliable modelling of the PVT properties of all five types of reservoir fluids (black oil, volatile oil, gas condensate, wet gas, dry gas) in various applications; the applications include the design and monitoring of field development, multiphase flow calculations in wells and field pipelines, and basin modelling. It is possible to algorithmise and automate the application of this approach in specialised software. This study considered eight Russian reservoir oil and gas-condensate systems, for which the PVT models were tuned, using the proposed approach. The comparison between proposed approach and other tuning methods in modern PVT simulators (PVTi, PVTsim, Multiflash, PVT Designer) is shown in the article. These examples show the effectiveness of the proposed approach.