Development of artificial neural network models for predicting water saturation and fluid distribution

We have developed artificial neural network (ANN) models to predict water saturation from log data. Two Middle Eastern sandstone reservoirs were investigated. In the first case, an ANN model was tested on the Haradh formation in Oman using wireline logs and core Dean–Stark data. In the second case, the ANN was used to model the saturation–height function in a complex sandstone reservoir. first case study, the model is based on a three-layered neural network structure. The model was successfully tested yielding a prediction of water saturation with a root mean square error (RMSE) of around 0.025 (fraction of pore volume P.V.) and a correlation factor of 0.91 to the test data. Furthermore, the ANN model was shown to be superior to conventional statistical methods such as multiple linear regression, which gave a correlation factor of 0.41. second case, the model yielded a saturation–height function with an RMSE of 0.079 (fraction P.V.) in saturation when using core porosity and height above free water level. This is a considerable improvement over conventional methods. The error was also greatly reduced when permeability and a lithology indicator were introduced. A minimum error of 0.045 (fraction P.V.) was obtained when using core data such as height, porosity, permeability, lithology and a functional link. We then used gamma ray, neutron, density, resistivity wireline data and the cation exchange capacity as inputs. Our best case which gave an RMSE error of 0.046 (fraction P.V.) was obtained. The ANN was then used to predict the hydrocarbon saturation in the Gharif formation and good results were obtained. The neural network model proved the robustness of saturation prediction in another field for the same formation.