Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means

Remoção de matéria orgânica em sistemas de lagoas de estabilização no Nordeste brasileiro

Stabilization pond is the main technology used for treatment wastewater, in northeast Brazil, due to lower cost of deployment, operation and maintenance compared to other technologies. Most systems of stabilization ponds has been in operation for some time, on average 10 years of operation, receiving high organic loads and do not have good removal efficiencies of the main parameters for which have been designed. Therefore it is necessary to work to quantify the efficiency of current systems. This study evaluated the biodegradability of organic matter in raw sewage, the removal of organic matter in reactors and determination of the kinetic constant removal of organic matter (k), both in reactors and in raw sewage, based on the analysis made in the laboratory and through mathematical methods proposed in the literature, in nine systems stabilization ponds, located in Rio Grande do Norte. In relation the degradation kinetics in stabilization ponds, it was observed that many papers published in the literature were obtained in pilot-scale systems, which often, due to the action of external factors such as wind and temperature, these can t be considered as a reference in the analysis of the kinetic constant K, so the need for more research into systems of scale. This study had three distinct phases and simultaneous, routine monitoring, study of the daily cycle and the determination of kinetic constant of degradation of organic matter (K). The monitoring showed that the removal efficiencies of organic matter on most systems were lower than suggested by the literature, the best efficiencies of around 76% (BOD) and 72% (COD) and the worst of the order of 48% (BOD) and 55% (COD). The calculation of K in raw sewage (Ke) was within the range of variation expected in the literature (0.35 to 0.60 days-1). Already for the results obtained for K in the reactors (Kr), there were well below the values recommended in the literature (0.25 to 0.40 d-1 for complete mix and from 0.13 to 0.17 d-1 for flow dispersed), in line with the overloads that organic systems are subject

Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means