Porosity prediction from well logging data via a hybrid MABC-LSSVM model.

ABSTRACT: Porosity is a key parameter for evaluating reservoir performance, but high-precision prediction is highly challenging in complex shale reservoirs due to the strong heterogeneity of the formation and the highly nonlinear relationship between logging parameters and porosity. Traditional prediction methods based on experience or physical models often have low generalization ability and accuracy. This study proposes a hybrid model (MABC-LSSVM) that combines a modified artificial bee colony (MABC) optimization algorithm with a least squares support vector machine (LSSVM) model. Inertia weights and acceleration coefficients are utilized to change the hyperparameters of the optimization model to achieve high-precision prediction of shale reservoir porosity using data-driven methods. The model inp

SUBMITTER: Su W 

PROVIDER: S-EPMC12558454 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature