COMPARATIVE ANALYSIS OF VOID FRACTION CORRELATIONS FOR HIGH VISCOSITY OIL DATA IN HORIZONTAL PIPELINE

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COMPARATIVE ANALYSIS OF VOID FRACTION CORRELATIONS FOR HIGH VISCOSITY OIL DATA IN HORIZONTAL PIPELINE

1 INTRODUCTION

The oil and gas industry is increasingly looking towards unconventional resources like heavy oil to help satisfy world energy demand as conventional reserves are continuously depleted due to several years of production and consumption. Viscous oil hydrodynamic characteristics are different from conventional oil (light) due mainly to its physical properties .As a result of these significantly different physical properties, heavy oil is more challenging to produce and transport. The major implication of these differences is seen in the design of heavy oil systems as well as in the implementation of technologies which were mostly developed on the basis of hydrodynamic characteristics of liquid oil. High-viscosity oils are discovered and produced all around the world. High-viscosity or “heavy oil” has become one of the most important future hydrocarbon resources, with ever-increasing world energy demand and depletion of conventional oils. Almost all flow models have viscosity as an intrinsic variable. Two-phase flows are expected to exhibit significantly different behavior for higher viscosity oils. Many flow behaviors will be affected by the liquid viscosity, including droplet formation, surface waves, bubble entrainment, slug mixing zones, and even three-phase stratified flow. Furthermore, the impact of low-Reynolds-number oil flows in combination with high-Reynolds-number gas and water flows may yield new flow patterns and concomitant pressure-drop behaviors. Void fraction prediction in high viscous liquid is of great importance .This is because most existing correlations for predicting two phase flow parameters were developed based on observations from low viscosity liquid gas flows which have different hydrodynamic features compared to high viscosity liquid gas flows. Consideration of these prediction models will ensure that pressure drop is accurately predicted (Oyewole 2009)

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