The application of improved neural network in hydrocarbon reservoir prediction

Xiaobo Peng

doi:10.1117/12.2011062

20 March 2013 The application of improved neural network in hydrocarbon reservoir prediction

Xiaobo Peng

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87683I (2013) https://doi.org/10.1117/12.2011062
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

Abstract

This paper use BP neural network techniques to realize hydrocarbon reservoir predication easier and faster in tarim basin in oil wells. A grey – cascade neural network model is proposed and it is faster convergence speed and low error rate. The new method overcomes the shortcomings of traditional BP neural network convergence slow, easy to achieve extreme minimum value. This study had 220 sets of measured logging data to the sample data training mode. By changing the neuron number and types of the transfer function of hidden layers, the best work prediction model is analyzed. The conclusion is the model which can produce good prediction results in general, and can be used for hydrocarbon reservoir prediction.

Citation Download Citation

Xiaobo Peng "The application of improved neural network in hydrocarbon reservoir prediction", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87683I (20 March 2013); https://doi.org/10.1117/12.2011062

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