Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction

  • M Awadalla Electrical and computer Engineering Department, SQU
  • H Yousef Electrical and computer Engineering Department, SQU
  • A Al-Shidani Computer Information Technology, Research Council of the Sultanate of Oman
  • A Al-Hinai Petroleum Development Oman
Keywords: Feed-forward neural networks, Radial Basis neural Network, Empirical models, Flowing, Bottom-Hole Pressure


This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has been  accomplished on the achieved results to validate the models’ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHP
The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4  RMES and 97% of the test data achieved 90% accuracy.


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How to Cite
Awadalla, M., Yousef, H., Al-Shidani, A., & Al-Hinai, A. (2016). Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 15(12), 7263-7283.