Heavy Oil Viscosity Prediction Using Surface Response Methodology

Abstract Modern petroleum engineering practices require accurate reservoir phase behavior properties to simulate and optimize various production and processing operations. Among these reservoir fluid properties, viscosity is an important property during the design of pipelines, production and processing equipment, well testing, and reservoir simulation. Direct viscosity measurement of the reservoir fluid requires representative reservoir fluid sampling that is expensive and often unavailable. Therefore, common procedure in industry is using developed correlations to predict the viscosity of the crudes. However, the major shortcomings of these correlations lie in their extremely simplistic or complex nature that reduces their applicability. In addition, commonly used correlations in industry were developed on the basis of data from special regions of the world that limit their applications as a universal approach for viscosity estimation. In this study, the main objective is developing a simple and efficient approach for prediction of medium to heavy oil viscosity by using Response Surface Methodology technique. For this purpose two datasets, 45 phase behavior data of medium to heavy crudes (12-25 °API) from Alberta and Saskatchewan and 110 data points from literature(6-22 °API), have been used during training, testing, and validation processes. The obtained results in this study indicate that the response surface methodology approach is successful in prediction of dead, live, and under-saturated crude viscosities over the range of data used for training process. In other words it can be safely concluded that response surface methodology can be used as an efficient tool for prediction of viscosity in the medium to heavy range of western Canadian crudes. Introduction Simulation and optimization of crude oil production and processing require proper understanding of reservoir fluid phase behavior. Among these properties crude oil viscosity is considered as one of the most important characteristics of reservoir fluid that controls fluid flow in porous media and influences the design of downhole and surface facilities and transportation systems. The routine practice in industry is fluid sampling from the reservoir and using laboratory measured viscosity values for various design purposes. However, there are cases where such direct measurements are not available. Therefore, as a common approach the PVT correlations are applied to predict the crude oil properties. Fundamentally, there are two approaches for crude oil viscosity predictions. The first approach uses oilfield data, such as reservoir temperature, produced oil API gravity, solution gas-oil ratio, to predict the oil viscosity (Beal [1], Glaso [2], and Kartoatmodjo and Schmidt [3]). The second approach is empirical and/or semi-empirical correlations that are using other data for prediction of crude oil viscosity, such as reservoir fluid composition, pour point temperature, normal boiling point, critical temperature, and acentric factor of components (Lorenz et al. [4] Little and Kennedy [5], and Pederson et al. [6]). These correlations are either generated by using random data sets or phase behavior data from specific geographical areas or specific class/type of oils. The major shortcomings of these correlations lie in their extremely simplistic or complex nature that reduces their applicability.

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