A New Neural Network Approach to Reservoir Permeability Estimation from Well Logs

Abstract

Abstract Reservoir permeability is one of the most important properties of oil and gas fields for reservoir characterization. In un-cored intervals or wells, reservoir description and evaluation methods using well log data represent a significant technical as well as economic advantage because well logs can provide a continuous record over the entire well where coring is impossible. Permeability determination from well logs in heterogeneous formation has a difficult and complex problem to solve by conventional statistical methods. Recently artificial neural networks (ANNs) have been successfully used to solve many of complex problems in reservoir permeability estimation. 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 polynomial neural network (PNN) for permeability prediction from well logs to alleviate limitations of the conventional neural network approach. The PNN evolutionally synthesizes network size, connectivity, processing element types, and coefficients for globally optimized structure through training. This self-organizing approach automatically presents internal relationships among data in the polynomial forms, and enhances data approximation and explanation capabilities of resulting data-based learning models. This technique is demonstrated with an application to the well data in offshore Korea. The comparative study with conventional neural networks reveals that the proposed model gives a relatively positive performance although the prediction accuracy of the PNN model is affected by errors in measurement data. The PNN is a practical and powerful tool for predicting reservoir permeability of a heterogeneous formation utilizing well logs.