화학공학소재연구정보센터
In Situ, Vol.23, No.2, 151-169, 1999
Combining neural networks with kriging for stochastic reservoir modeling
Stochastic reservoir modeling is being increasingly used for modeling reservoir heterogeneity. This paper describes a methodology to model the distribution of reservoir properties using well data and soft geological knowledge in the form of sedimentary and diagenetic patterns. The technique, developed based on a combined use of radial basis function (RBF) neural networks and geostatistical kriging, is demonstrated with an application to interpolating porosity in the A'nan Oilfield, located onshore north China. The integrated technique first uses neural networks to estimate the porosity trends from high-dimensional geological patterns. Optimization of the network performance is done by variogram analysis of the residuals at the conditioning points. Gaussian simulation of the residuals is then performed, and the resulting residual maps are combined with the porosity trends obtained from neural networks. From the case study, the results are realistic and honor the geological rules of the oilfield. The technique is fast and straightforward, and provides a computational framework for conditional simulation.