Correlation Approach Predicts Viscosity of Crude-Oil Systems Offshore Norway
- Chris Carpenter (JPT Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- August 2018
- Document Type
- Journal Paper
- 51 - 53
- 2018. Society of Petroleum Engineers
- 2 in the last 30 days
- 68 since 2007
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191296, “An Improved Correlation Approach To Predict Viscosity of Crude-Oil Systems on the NCS,” by Jørgen Bergsagel Møller, SPE, Knut Kristian Meisingset, SPE, and Ibnu Hafidz Arief, Equinor, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has not been peer reviewed.
An accurate estimation of viscosity values is imperative for optimal production and transport design of hydrocarbon fluids. Consequently, precise and robust empirical correlation models are highly desired. While the literature contains numerous correlation models, most of these are inadequate for predicting accurate oil viscosity with unbiased data. This paper aims to develop new and improved empirical viscosity correlations through available field measurements on the Norwegian Continental Shelf (NCS).
The most-accurate viscosity correlation method makes use of the material balance of compositional information, which implies that a comprehensive pressure/volume/temperature (PVT) report is required. Such PVT reports usually include oil viscosity, which makes the correlation model redundant in many cases. Often, the only information available related to the fluid property is the solution gas/oil ratio (GOR), temperature, American Petroleum Institute (API) gravity, and pressure.
The empirical correlations in the literature have established categories to correlate oil viscosity for dead, gas-saturated, and undersaturated oil. The dead-oil correlations are used to predict the viscosity at standard conditions, when no gas is left in solution. All correlations from the literature ex press dead-oil viscosity as a function of API gravity and temperature. The second category is defined as a function of dead-oil viscosity and solution GOR and is applied when the fluid is at, or below, the saturation pressure. The latter correlation is normally expressed as a function of saturated oil viscosity, bubblepoint pressure, and reservoir pressure. The undersaturated-oil-viscosity correlations are applied when the reservoir pressure increases beyond the saturation pressure. This study investigates the statistical performance of 10 dead-oil, eight saturated, and five undersaturated oil-viscosity correlations.
Traditionally, the correlation models present an explicit mathematical expression based on field measurements to predict viscosity, while this paper presents three different correlation methods. Two methods are recognized as surrogate models to predict the viscosity properties, called the radial basis function network (RBFN) and kriging. In contrast to the traditional correlation models, these techniques do not present a mathematical correlation, because the models make use of a statistical approach with more consideration of the variation in input variables to predict the output. The kriging method demonstrated results superior to those of the empirical correlations. The third and last approach is an optimization algorithm called particle-swarm optimization (PSO). The technique recalculates the coefficients of the discussed correlations from the literature while maintaining the functional form of the expressions.
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