Abstract

Hydrocarbon PVT data are used for a wide range of reservoir engineering applications including early volumetric assessment, well test evaluation, material balance calculations, and equation of state fluid characterization. High quality, accurate PVT data can reduce uncertainty in reservoir fluid properties, provide a sound foundation for reservoir engineering studies, and improve investment efficiency. Poor quality PVT data can result in lost time due to rework and additional studies, inadequate or overly aggressive development plans, and inefficient investment.

While acquiring sufficient volumes of representative reservoir fluid samples is the first step in obtaining reliable PVT data(1), understanding and identifying the quality of the PVT data once obtained from the laboratory is essential to ensuring that data are applied appropriately. A number of techniques exist that can be applied to evaluate PVT data quality and significant experience in using these techniques has been gained over the years. Examples and illustrations of evaluating data quality for fluid compositions and flash data, oil PVT data, and gas condensate PVT data are provided.

Introduction

The techniques described have primarily been used to assess PVT data quality for equation of state fluid characterization, but these techniques have much broader application such as ensuring an appropriate PVT basis for in place volume estimates, analytical reservoir engineering calculations, and regulatory submittals. When preliminary laboratory PVT data are provided to the reservoir engineer, these techniques can be used to screen that data. The reservoir engineer can then use the results to provide feedback to the laboratory and identify when measurements or laboratory calculations may need to be redone.

Equation of state parameters can sometimes be tuned beyond reasonable bounds to match PVT measurements that are not physically realistic. When an equation of state is overly tuned to bad data, at least two issues arise. First, the quality of the match to the remaining good quality data is often worse. Second, and more importantly, the ability to predict properties outside the range of the measurements is compromised. By using these quality and consistency evaluation techniques, discrepancies can usually be resolved thus enabling the reservoir engineer to improve the quality of the fluid characterization and property predictions.

Often reservoir fluid samples and laboratory PVT data are acquired over a period of time. Comprehensive assessment of the data is often performed after data have been acquired from many laboratories using different techniques. This often leads to differences in measurements between laboratories and/or datasets that need to be resolved. When differences in the data are observed, the techniques described can be useful for resolving these differences and in identifying data most appropriate for reservoir engineering applications.

Discussion

The methods described in detail include but are not limited to:

  • - Material balance checks and Hoffman plots(2) to assess consistency of compositional and flash data

  • - Graphical techniques to assess the consistency between constant volume depletion and constant composition expansion data for gas condensates with methods applied to liquid dropout data, Z-Factor data, and density data

  • - Reference volume translation techniques for formation volume factor and solution GOR measurements to assess data quality at low pressure and ensure that differential liberation data for oils are used appropriately

  • - Cross-plot techniques for comparing data from a variety of sources

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