Modeling Porosity Distribution in the A'nan Oilfield: Use of Geological Quantification, Neural Networks, and Geostatistics

Abstract

Summary A'nan Oilfield is located in the northeast of the Erlian Basin in North China. The porosity distribution of the oil-bearing stratum is primarily controlled by complex distribution patterns of sedimentary lithofacies and diagenetic facies. This paper describes a methodology to provide a porosity model for the A'nan Oilfield using limited well porosity data, with the incorporation of the conceptual reservoir architecture. Neural network residual kriging or simulation is employed to tackle the problem. The integrated technique is developed based on a combined use of radial basis function neural networks and geostatistics. It has the flexibility of neural networks in handling high-dimensional data, the exactitude property of kriging and the ability to perform stochastic simulation via the use of kriging variance. The results of this study show that the integrated technique provides a realistic description of porosity honoring both the well data and the conceptual framework of the geological interpretations. The technique is fast, straightforward and does not require any tedious cross-correlation modeling. It is of great benefit to reservoir geologists and engineers.