Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

Reservoir permeability is one of the most important parameters of oil and gas fields for reservoir characterization. In un-cored intervals, reservoir description and evaluation methods using well log data represent a significantly technical and economic advantage because well logs can provide a continuous record over the entire well. Permeability estimation from well logs in heterogeneous formation has a difficult and complex problem to solve by conventional statistical methods. Recently artificial neural networks(ANN) have been successfully used to solve the complex problems in reservoir permeability determination. However, the applications of the neural network to mapping complex nonlinear relationship have revealed a number of unsolved technical limitations despite of the high versatility. This paper proposes a group method of data handling(GMDH) based on an advanced polynomial neural network(PNN) for permeability prediction from well logs to alleviate limitations of the conventional neural network approach.