Examinando por Materia "PRUEBAS"

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  • Artículo de Publicación Periódica
    On a goodness-of-fit test for normality with unknown parameters and type-II censored data
    (2010-07) Castro-Kuriss, Claudia; Kelmansky, Diana M.; Leiva, Víctor; Martínez, Elena J.
    "We propose a new goodness-of-fit test for normal and lognormal distributions with unknown parameters and type-II censored data. This test is a generalization of Michael's test for censored samples, which is based on the empirical distribution and a variance stabilizing transformation. We estimate the parameters of the model by using maximum likelihood and Gupta's methods. The quantiles of the distribution of the test statistic under the null hypothesis are obtained through Monte Carlo simulations. The power of the proposed test is estimated and compared to that of the Kolmogorov-Smirnov test also using simulations. The new test is more powerful than the Kolmogorov-Smirnov test in most of the studied cases. Acceptance regions for the PP, QQ and Michael's stabilized probability plots are derived, making it possible to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an illustrative example is presented."
  • Ponencia en Congreso
    On some goodness-of-fit tests and their connection to graphical methods with uncensored and censored data
    (2019) Castro-Kuriss, Claudia; Huerta, Mauricio; Leiva, Víctor; Tapia, Alejandra
    "In this work, we present goodness-of-fit tests related to the Kolmogorov-Smirnov and Michael statistics and connect them to graphical methods with uncensored and censored data. The Anderson-Darling test is often empirically more powerful than the Kolmogorov-Smirnov test. However, the former one cannot be related to graphical tools by means of probability plots, as the Kolmogorov-Smirnov test does. The Michael test is, in some cases, more powerful than the Anderson-Darling and Kolmogorov- Smirnov tests and can also be related to probability plots.We consider the Kolmogorov-Smirnov and Michael tests for detecting whether any distribution is suitable or not to model censored or uncensored data. We conduct numerical studies to show the performance of these tests and the corresponding graphical tools. Some comments related to big data and lifetime analysis, under the context of this study, are provided in the conclusions of this work."
  • Ponencia en Congreso
    Polymer viscosity: understanding of changes through time in the reservoir and a way to predict them
    (2019-09) Katz Marquez, Román E.
    Polymer rheological behavior in an Enhanced Oil Recovery (EOR) project is one of the critical factors to determine whether the polymer injection would be effective to increase the oil production in a field. Due to complications on the measurement of this parameter and its variation within the reservoir, the challenge of understanding viscosity behavior relies on lab and field tests that become key factors to solve this issue. This study was conducted during an injectivity test for an EOR project in Los Perales field (Santa Cruz, Argentina) in three wells with different operational and subsurface conditions, and tests were performed twice a day for 30 days each in order to obtain sufficient time span of data. From lab rheology tests performed at reservoir conditions, where the main objective was to analyze viscosity changes through time, two different tendencies were observed: one that affects in early times and another that becomes preeminent at late times. With these results, a describing equation was developed to predict viscosity evolution over time. The equation consists of three terms including thermal variation, chemical degradation and the final viscosity towards which the polymer tends. Although the equation properly describes both lab and field polymer solution, there is a considerable difference, especially when the effects mentioned become preponderant. This difference is attributed to both the water used for the mixture and the possible impurities that may be incorporated during the maturation or transfer of the polymer. Since most of the data used was obtained from field tests, this emphasizes the appliance of the equation on the field. Impurities turn out to be crucial, specially oxygen (O2) and hydrogen sulfide (H2S) combined. Their presence highly impacts the asymptotic viscosity, so a correlation between H2S content and final viscosity was also developed. Finally, an analysis of the temperature influence on the viscosity was conducted. A correlation between the final viscosity and temperature was found and used to incorporate temperature variations in the predictions and therefore to relate measurements performed at different conditions. The primary advantage of this study is that the equation and correlations enable the prediction of the polymer solution viscosity at any time. This allows the estimation of actual polymer viscosity in the reservoir from a routine measurement at any temperature and impurities content. The versatility of this equation is what makes it novel and useful in an industry going towards EOR projects.