Co-estimation geostatistical methods: a study of the correlation between variables at results precision / Métodos geoestatísticos de co-estimativas: estudo do efeito da correlação entre variáveis na precisão dos resultados

AUTOR(ES)

Jorge Watanabe

DATA DE PUBLICAÇÃO

2008

RESUMO

This master dissertation presents the results of a survey into co-estimation methods commonly used in geostatistics. These methods are ordinary cokriging, collocated cokriging and kriging with an external drift. Besides that ordinary kriging was considered just to illustrate how it does work when the primary variable is poorly sampled. As we know co-estimation methods depend on a secondary variable sampled over the estimation domain. Moreover, this secondary variable should present linear correlation with the main variable or primary variable. Usually the primary variable is poorly sampled whereas the secondary variable is known over the estimation domain. For instance in oil exploration the primary variable is porosity as measured on rock samples gathered from drill holes and the secondary variable is seismic amplitude derived from processing seismic reflection data. It is important to mention that primary and secondary variables must present some degree of correlation. However, we do not know how they work depending on the correlation coefficient. That is the question. Thus, we have tested co-estimation methods for several data sets presenting different degrees of correlation. Actually, these data sets were generated in computer based on some data transform algorithms. Five correlation values have been considered in this study: 0.993; 0.870; 0.752; 0.588 and 0.461. Collocated simple cokriging was the best method among all tested. This method has an internal filter applied to compute the weight for the secondary variable, which in its turn depends on the correlation coefficient. In fact, the greater the correlation coefficient the greater the weight of secondary variable is. Then it means this method works even when the correlation coefficient between primary and secondary variables is low. This is the most impressive result that came out from this research.

ASSUNTO(S)

cross semivariogram multicollocated sampling geoestatística multivariada markov model pearson s correlation coefficient coeficiente de correlação de pearson smoothing effect collocated sampling collocated simple cokriging modelo markoviano efeito de suavização multivariate geostatistics amostragem multicolocalizada krigagem com deriva externa amostragem colocalizada co-estimation cokrigagem colocalizada semivariograma cruzado co-estimativa external drift kriging

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