Title : Understanding PVT properties and phase behaviour of reservoir fluids: Implications for reservoir engineering and production optimization
Abstract:
Accurate characterization of reservoir fluids remains a fundamental requirement for reliable reservoir evaluation, production forecasting, and enhanced recovery planning. This study presents an integrated workflow for the analysis, quality control, and modeling of Pressure–Volume–Temperature (PVT) data, with emphasis on improving the reliability of laboratory measurements and their consistency with equation of state (EOS) models. The work is based on comprehensive laboratory testing of representative bottom-hole fluid samples, including Constant Composition Expansion (CCE), Constant Volume Depletion (CVD), Differential Liberation (DL), separator tests, and viscosity measurements.
A systematic Quality Assurance and Quality Control (QA/QC) framework was applied to ensure data integrity. This includes material balance verification, consistency checks across depletion stages, phase behavior validation, and detection of experimental anomalies. Special attention is given to common sources of error such as sample contamination, recombination inaccuracies, and volumetric inconsistencies. The study demonstrates how rigorous QA/QC procedures significantly enhance confidence in laboratory-derived properties.
Subsequently, validated PVT data were used for EOS development and regression within a compositional simulation environment. Critical parameters, including critical properties, acentric factors, and binary interaction coefficients, were tuned to achieve a consistent match with experimental observations. The impact of fluid characterization, component lumping strategies, and regression methodology on model accuracy is discussed in detail. The resulting EOS model was successfully validated against multiple laboratory datasets, ensuring its applicability for reservoir simulation and field development planning.
The study highlights the importance of integrating laboratory data with advanced modeling techniques to achieve reliable fluid descriptions. It also emphasizes the role of automation tools, such as Excel-based QA/QC systems, in streamlining data processing and minimizing human error. The proposed workflow provides a robust and practical approach that can be adopted in PVT laboratories and reservoir engineering studies to enhance data quality and modeling reliability.
Overall, this work contributes to improving the accuracy of reservoir fluid characterization and supports better decision-making in hydrocarbon development projects, particularly for complex fluids such as gas condensates and volatile oils.
