In the oil industry, the evaluation of oil viscosity is one of the important issues. Generally, the viscosity of crude oil depends on pressure and temperature. In this study, the prediction issue of oil viscosity has been viewed applying empirical correlations as Beggs-Robinson, Labedi, modified Kartoatmodjo, Elsharkawy and Alikhan, Al-Khafaji. Original field data reports have been obtained from Guneshli oil field of Azerbaijan sector of Caspian Basin. The correlation models used in the evaluation of viscosity of Azerbaijan oil have been implemented in the Python software environment. The obtained values on empirical correlations have been compared to experimental data obtained from Guneshli oil field. Statistical analysis in terms of percent absolute deviation (% AD) and the percent absolute average deviation (% AAD), mean absolute error (% MAE), correlation coefficient (% ), root mean square error (% RMSE) are used to subject the evaluation of the viscosity correlations. According to statistical analysis, it has been known that the Beggs-Robinson model has shown the lowest value on AAD (10.5614%), MAE (12.4427 %), RMSE (20.0853 %). The Labedi model has presented the worst result on every four criterions. Even though the Elsharkawy-Alikhan model has presented the highest result (99.9272%) on correlation coefficient, in the evaluation of viscosity of Azerbaijan crude oil, the Beggs-Robinson model can be considered more acceptable.

At present, an anomaly detection is one of the important problems in many fields. The rapid growth of data volumes requires the availability of a tool for data processing and analysis of a wide variety of data types. The methods for anomaly detection are designed to detect object’s deviations from normal behavior. However, it is difficult to select one tool for all types of anomalies due to the increasing computational complexity and the nature of the data. In this paper, an improved optimization approach for a previously known number of clusters, where a weight is assigned to each data point, is proposed. The aim of this article is to show that weighting of each data point improves the clustering solution. The experimental results on three datasets show that the proposed algorithm detects anomalies more accurately. It was compared to the k-means algorithm. The quality of the clustering result was estimated using clustering evaluation metrics. This research shows that the proposed method works better than k-means on the Australia (credit card applications) dataset according to the Purity, Mirkin and F-measure metrics, and on the heart diseases dataset according to F-measure and variation of information metric.