Assessment of Petroleum Reservoir Recovery Factor Using Complexity Scoring and Artificial Neural Network
Journal: International Journal of Scientific Engineering and Research (IJSER) (Vol.3, No. 7)Publication Date: 2015-07-05
Authors : Samuel A. Afari; Kwame Sarkodie; Wilberforce N. Aggrey; Anthony Morgan;
Page : 124-129
Keywords : Recovery factor; Artificial Neural Network (ANN); complexity scoring; structural complexity; MATLAB;
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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.
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