Assessment of Petroleum Reservoir Recovery Factor Using Complexity Scoring and Artificial Neural Network

Abstract

The Recovery factor (R.F) is an important parameter needed for assessing the commercial viability of a petroleum reservoir. This parameter is however very difficult to determine as existing models require large number of parameters that must be known accurately. This paper demonstrates the use of complexity scoring approach and artificial neural network in predicting the recovery factor of petroleum reservoirs. Various oilfields with known complexity parameters and recovery factors were considered. These complexity parameters were then scored based on carefully defined criteria. The scored parameters were then used to train a carefully designed artificial neural network (ANN) which was then validated and tested for RF prediction. Results show good prediction of RF with this approach.