Petroleum reservoirs demonstrate a very complex behavior that changes with time in a non-linear manner. Application of neural networks for field-wise analysis of waterflooding projects is very appropriate because a structural model between injection and production does not need to be specified in order to predict performance. The neural network approach recognizes that individual well behavior may depend on the well history and the injection/production conditions of surrounding wells. The outcome of this neural network analysis could determine injection and production policies that would lead to determining the minimum injection water leading to maximum oil recovery.

No abstract