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A New Algorithm of AHP-BP Applied in Petroleum Engineering

Jing Ning, Fan Honghai, Zhai Yinghu, Geng Zhi


Artificial neural network has been widely used at present to solve many engineering problems. However, simple neural network model is hard to get the desired result. This paper proposes one combined model of AHP (Analytic Hierarchy Process) and BPNN (Back Propagation Neuron Network) based on data mining. This model can improve the rate of convergence and the reliability of results. The validity of this method has been demonstrated with data from an existing oilfield in north-west China for predicting the rate of penetration (ROP). Furthermore, the model can be used in post-well analysis to identify areas where potential drilling performance was not achieved, and help in identifying improvements for future projects.


Petroleum Engineering, Optimization Algorithm, Data Mining, Neural Network, AHP

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