Research on porosity prediction method for petroleum reservoirs based on improved gene expression algorithm

Abstract Porosity, an essential parameter of reservoir properties, plays a crucial role in reservoir evaluation. Indirect prediction of reservoir porosity using well-logging data is a fundamental task in assessing hydrocarbon reserves. Multivariate stepwise regression and artificial neural network methods are currently typical prediction approaches. However, these methods suffer from drawbacks such as high workload, extensive training sample requirements, and high computational costs. Genetic Expression Programming (GEP), a novel genetic algorithm, exhibits remarkable function discovery capabilities, high efficiency, and the ability to function without requiring large training samples. This paper proposes a method that utilizes GEP to mine a porosity calculation model from well-logging sample data and subsequently compute the porosity. The effectiveness and efficiency of this method are validated through actual well-logging data.