Oil viscosity -
It was found that it is not possible to generalize a correlation for the heavy oil viscosity using only API and temperature. However, the proposed. TEMPERATURE RELATION OF SOME IRAQI CRUDE OILS Key Words: Viscosity, Temperature, Predictive Correlation, Iraqi crude oil, polyfit. modified equation was used to calculate the viscosity of several oil samples where . as a function of temperature and density (volume), requiring a density.
Dead or gas-free oil viscosity is determined as a function of crude oil API gravity and temperature. The viscosity of the gas saturated oil is found as a function of dead oil viscosity and solution gas-oil ratio GOR. Undersaturated oil viscosity is determined as a function of gas saturated oil viscosity and pressure above saturation pressure. Table 2 Table 3 Comparison of different methods Fig. The results illustrate the trend for dead oil viscosity and temperature.
As temperature decreases, viscosity increases. These tendencies make these methods unsuitable for use in the temperature range associated with pipelines. Dead oil viscosity correlations are somewhat inaccurate because they fail to take into account the chemical nature of the crude oil. Only methods developed by Standing  and Fitzgerald    take into account the chemical nature of crude oil through use of the Watson characterization factor.
Data should be acquired at temperatures over the range of interest. This method is recommended when measured dead oil viscosity data are available. Bubblepoint oil viscosity methods Table 4 Table 5 Correlations for bubblepoint oil viscosity typically take the form proposed by Chew and Connally.
NEW MODELS TO PREDICT THE VISCOSITY OF EXTRA-HEAVY OIL IN COLOMBIAN FIELDS
The following are the main models reported in the literature to predict the viscosity of undersaturated oils based on experimental data. All these models assume Newtonian behavior of undersaturated fluids at calculation temperatures and pressures. The database used consisted of points of undersaturated oils ranging from 15 to 60 API.
The undersaturated oil viscosity model created by Kartoatmodjo and Schmidt is based on the proposal developed by Standingusing non-linear regression techniques with viscosity data from PVT reports of undersaturated heavy oils 14 - 59 API. The new model reported an average absolute error of 2.
In the development of their viscosity model, Petrosky oil from the Gulf of Mexico 25 - 46 API ; the authors reported average relative and absolute errors between The first viscosity model for extra-heavy oil was created by De Ghetto, Paone and Villa The model was developed based on a non-linear regression analysis, which takes into account properties such as API gravity and dead oil viscosity for the prediction.
Elsharkawy and Alikhan presented a model to predict the viscosity of undersaturated oils, by using a database created with viscosity points for undersaturated oils between 12 and 22 API from the Middle East. The undersaturated oil viscosity model proposed by Hossain et al. Finally, Bergman and Sutton proposed a new model to predict the viscosity of undersaturated oils, designed particularly for oil in extreme conditions Deep Water based on a data base of oil samples from around the world, ranging from 15 to 48 API.
This was done to detect any deficiencies and verify that they belonged to extra-heavy oil, thus avoiding shortcomings in the predictive capacity of the different models that were modified. In second place, by selecting the papers that are mostly used in the industry to predict the viscosity of undersaturated oils, a comparative analysis was conducted to select the model with the best prediction with respect to the experimental viscosity information in the PVT reports.
The best model was selected based on statistical analysis, taking Relative Error Er as a starting point and taking into account the model with the lowest Average Absolute Error Eap as the first criterion of selection.
The statistical measurements were obtained as follows: This was obtained using the following expression: RESULTS Initially in this paper, there was PVT testing information from 14 heavy oil production wells, with a total of 16 points to analyze see Table 1 ; some from the San Fernando Formation in Colombia and others taken from databases in the literature of similar samples of extra-heavy oil.
The oils analyzed, as illustrated in Table 1had API gravities ranging from 6. The range of validity defined by each author for these models is listed in Table 2. The model proposed by De Ghetto had the best performance with an average absolute error of 7. This leads to the conclusion that this is the best model to make the adjustment to predict the viscosity of undersaturated extra-heavy oils.
You can see that the other models had high error percentages, as they were operating outside the permissible ranges of validity for each of them.
Although this behavior was somewhat predictable, the decision was made to take these models into account for the present analysis because occasionally, an extrapolated model can have good behavior with respect to the data analyzed. In addition, the information available for other extra-heavy oils exclusive models is very scarce except for the De Ghetto model. Adjustment of the Selected Model The model proposed by De Ghetto to predict the viscosity of extra-heavy oils was the one that came closest to the viscosity data obtained by PVT testing.
Therefore, in the selection stage, it was selected as the best one to make the adjustment. De Ghetto expresses the above premise as follows: The values were found directly with the equation of the line that describes each of the points of the PVT tests, and analyzed as illustrated below: In addition, the De Ghetto model was optimized for each point, by using the statistical program as illustrated in Table 3.
After completing the process, the optimal values of the terms X, Y and Z were estimated, using the information from the PVT reports. The methodology used for the regression analyses can be summarized as follows: In Cartesian coordinates, plot the optimal values of X, Y and Z vs.
Once the data dispersion is found, enter the equations corresponding to each of the terms in the statistical package. Once the program has made the adjustment, verify that the statistical parameters are within the desired expectations.
The regression curves relating to the analyses are listed below in Figures 23 and 4. The summary of the statistical information for each regression analysis is listed in Table 4. Although the results of the regression analysis are very accurate, it was observed Figure 2 that for dead oil viscosity values near cP, the regression model has certain deficiencies. The following function was selected: The diagram of the behavior and the information from the regression analysis conducted with this function are illustrated in Figure 5.
Adjusted De Ghetto Model Finally, based on the results of the regression analysis, the De Ghetto model is reformulated with the new coefficients. The adjusted model has been presented as follows: New Proposed Model The model presented based on the correction made for the dead oil viscosity values is presented as follows: Evaluation of the Predictive Capacity of the Models Figures 6 and 7 are part of a comparative analysis of the adjusted models in this article with the four models that initially had a better fit, including the original model proposed by De Ghetto.
This was done to observe the results obtained in the process of adjusting the model for undersaturated extra-heavy oils.