Using Generalized Linear Regression Of Multiple Attributes For Modeling And Prediction The Formation Permeability In Sandstone Reservoir

摘要

In this paper, we present an efficient flexible algorithm for modeling the formation permeability withrespect to other well and core petrophysical properties for a well in sandstone formation in West Africa.In Generalized Linear Regression, the monotonic link function, glm, has been used to generate therelationship between the core permeability and the explanatory variables such as caliper log (CCL), deepinduction log, medium induction log, gamma rays, neutron porosity, core porosity, deep resistivity,medium resistivity, spontaneous potential (SP), density & corrected density, in addition to rock facies.Meanwhile, the glm function has been adopted for attaining normal distribution for the continuousvariables and multinomial distribution for the discrete (rock facies). The link function relates the expectedvalue of the distribution to the predictor variables. Fitting of a GLM model was achieved throughoptimization of maximum likelihood estimates by an iteratively reweighted least-squares mechanism.The Root mean square errors (RMSE) is not a sufficient indicator of bias for an estimate becauseRMSE could be changed due to a change in variance, with no change in bias. Consequently, variance,analysis of deviance, and RMSE have been considered for model comparisons. In GLM results, asignificant overall reduction in model deviance with less variance, residual deviance, and RMSE thatreflects the reduction in both variance and bias and the validity of the fitted linear model.