The classification of reservoir fluids is a matter of considerable practical importance.
By following the rule of thumb, fluid types in a reservoir can be identified,
but laboratory observations are often required to verify them because of the imprecise
and uncertainty that exist in reservoir parameters.
- We have proposed the application of Artificial Neural Networks to classify the reservoir fluid types
based on laboratory observation and field data.
- More than 700 samples of different types of reservoir fluid types were used to develop the ANN model.
- Different types of architecture for different groups of input data were tested using fitness criteria to identify an optimal architecture.
- The optimal neural network was able to classify the reservoir fluid with high accuracy.