Análise de agrupamentos dos dados de DFA oriundos de perfis elétricos de indução de poços de petróleo

The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis

Análise de agrupamentos dos dados de DFA oriundos de perfis elétricos de indução de poços de petróleo

The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis