Abstract:
Practicable and realistic reservoir characterization is essential for optimal reservoir
management. In this study, a randomized back-propagation neural network model is
developed for fonnation permeability prediction. The model has only one hiddenlayer,
and tbe inputs to the model are core porosity, facies identifier, sample
thickness, and well sample location. A number of sensitivity studies for permeability
prediction are performed. Prediction errors from the model are analyzed and a post.
processing scheme for error mitigation is investigated. Neural network responses
were compared with those using conventional methods for permeability
determination. There are some specific advantages of using the developed model.
Characterization of prediction space is observed to be better. However, the
limitations of the study were also highlighted. A variety of applications of artificial
neural networks in reservoir engineering problems are reviewed in this study.
