An improved technique in porosity prediction: a neural network approach

Genetic reservoir characterization is important in developing, for a given petroleum reservoir, an improved understanding of the total amount and fluid flow properties of hydrocarbon reserves. Application of genetic concepts involves the classification of well log data into different lithofacies groups, followed by a facies-by-facies description of rock properties such as porosity and permeability. This work contrasts the genetic and nongenetic approaches in predicting porosity values of an oil well using backpropagation neural network methods. The performance of both methods are critically evaluated. A systematic technique to optimise the network configuration using weight visualization curves is proposed, thereby enabling the amount of training time to be significantly reduced. In the example problem, the genetic approach provides superior porosity estimates to that based on a nongenetic approach.<
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