Estimation of reservoir properties from seismic attributes and well log data using artificial neural networks

Porosity, permeability are key factors to build a 3D geological model for a reservoir. The best method to get these properties would be to measure them on core samples in the laboratory. However, this method is costly and time consuming.To correlate reservoir properties with the continuously recorded well log data geologists generally use linear or non-linear regressions. This talk reports a comparative study of two types of neural networks, a Multiple-Layer Perception MLP, and a General Regression Neural Network GRNN. The viability of these techniques are demonstrated on log data and seismic from a reservoir in south of Algeria. This study utilizes the basic logs (GR, DT, VSH, RHOB, LLD and NPHI and five attributes to predict porosity, permeability and lithofacies in cored and uncored wells. The agreement between the core data and the predicted values by neural networks demonstrate a successful implementation and validation of the network s ability to map a complex non-linear relationship between well logs and permeability and porosity. Also the results show that the application of the General Regression Neural Network GRNN gives a relatively better performance than the Multiple-Layer Perception MLP.