Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction
DOI:
https://doi.org/10.24297/ijct.v15i12.4354Keywords:
Feed-forward neural networks, Radial Basis neural Network, Empirical models, Flowing, Bottom-Hole PressureAbstract
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 FBHPaccurately.
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 3.4Â RMES and 97% of the test data achieved 90% accuracy.
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References
[1] M. Ahmadi , R. Soleimani, M. Lee, T. Kashiwao, and A. Bahadori, Determination of oil well production performance
using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum, Volume 1, Issue
2, pp. 118–132, June 2015.
[2] O. Adewale, “Optimization of natural gas field development using artificial neural networks†MSc Paper, The
Pennsylvania State University, USA, 2010.
[3] O. Adekomaya, A.S. Fadairo, and O. Falode, “Predictive Tool for Bottom-Hole Pressure in Multiphase Flowing Wells"
Petroleum & Coal, ISSN 1337-7027, 50 (3), pp. 67-73, 2008.
[4] A. Mehta, H. Mehta, T. Manjunath, and C. Ardil, “A Multi-Layer Artificial Neural Network Architecture Design for Load
Forecasting in Power Systems†International Scholarly and Scientific Research & Innovation, 5(2), pp. 207-220, 2011.
[5] D. Beggs and J.P. Brill, “A study of two-phase flow in inclined pipes†Journal of Petroleum Technology, 25(5), pp. 607-
617, 1973.
[6] S. Bikbulatov, M. Khasanov, and A. Zagurenko, “Flowing Bottomhole Pressure Calculation for a Pumped Well under
Multiphase Flow†Cornell University Library Web site http://arxiv.org/abs/physics/0504083v1, 2005.
[7] T. Gabor, “Considerations on the Selection of an Optimum Vertical Multiphase Pressure Drop Prediction Model for Oil
Wellsâ€. Society of Petroleum Engineers, 1-5, 2001.
[8] Dae-Hyun Jeong, Young-Il Kwon, Geuntae Cho, Young-Ho Moon. An Analysis of the Effect of Joint Research
Networks on the Diffusion of Knowledge: Focusing on the Renewable Energy Field, Indian Journal of Science and
Technology, 2015, 8(S1), pp. 445-51.
[9] Balakrishna Moorthy C, Ankur Agrawal, Deshmukh M K. Artificial Intelligence Techniques for Wind Power Prediction: A
Case Study, Indian Journal of Science and Technology, 2015, 8(25), pp. 1-10.
[10] Farhad Soleimanian Gharehchopogh, Seyyed Reza Khaze, Isa Maleki. A New Approach in Bloggers Classification
with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms, Indian Journal of Science and
Technology, 2015, 8(3), pp. 237-46.
[11] A.R. Hassan and C.S. Kabir, “Determining Bottom-hole Pressures in Pumping Wells†Society of Petroleum Engineers,
25(6), pp. 823-838, 1985.
[12] S. Haykin, “Neural and Learning Machines†(3d ed), London, Pearson, 2009.
[13] I. Jahanandish, B. Salimifard, and H. Jalalifar, “Predicting bottomhole pressure in vertical multiphase flowing wells
using artificial neural networks†Journal of Petroleum Science and Engineering, vol. 75, pp. 336–342, 2011.
[14] H. Jeff, “Introduction to Neural Networks†Chesterfield: Heaton Research. Retrieved from
http://www.heatonresearch.com/book/programming-neural-networks-java-2.html, April 24, 2015
[15] M. Mohammadpoor, K. Shahbaz, F. Torabi, and A. Qazvini, “A New Methodology for prediction of buttonhole flowing
pressure in vertical Multiphase flow in Iranian Oil Fields using Artificial Neural Networks (ANNs)†Society of Petroleum
Engineers, 2010.
[16] Petroleum Exploration Company, “ Products, Retrieved from http://www.petex.com/products/?ssi=3, January 16,
2015.
[17] D. Himansu, N. Ajay, N. Bighnaraj, H. S. Behera, “A Novel PSO Based Back Propagation Learning-MLP (PSO-BPMLP)
for Classification. Proceedings of the International Conference on CIDM, pp. 461-471, 2014.
[18] M.A., Ahmadi, A., Bahadori, “Determination of oil well production performance using artificial neural network (ANN)
linked to the particle swarm optimization (PSO) tool Petroleum†http://dx.doi.org/10.1016/j.petlm, 2015.
[19] M.A. Ahmadi, M. Ebadi, S.M. Hosseini, “Prediction breakthrough time of water coning in the fractured reservoirs by
implementing low parameter support vector machine approach†Fuel, 117 (2014), pp. 579–589, 2014.
using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum, Volume 1, Issue
2, pp. 118–132, June 2015.
[2] O. Adewale, “Optimization of natural gas field development using artificial neural networks†MSc Paper, The
Pennsylvania State University, USA, 2010.
[3] O. Adekomaya, A.S. Fadairo, and O. Falode, “Predictive Tool for Bottom-Hole Pressure in Multiphase Flowing Wells"
Petroleum & Coal, ISSN 1337-7027, 50 (3), pp. 67-73, 2008.
[4] A. Mehta, H. Mehta, T. Manjunath, and C. Ardil, “A Multi-Layer Artificial Neural Network Architecture Design for Load
Forecasting in Power Systems†International Scholarly and Scientific Research & Innovation, 5(2), pp. 207-220, 2011.
[5] D. Beggs and J.P. Brill, “A study of two-phase flow in inclined pipes†Journal of Petroleum Technology, 25(5), pp. 607-
617, 1973.
[6] S. Bikbulatov, M. Khasanov, and A. Zagurenko, “Flowing Bottomhole Pressure Calculation for a Pumped Well under
Multiphase Flow†Cornell University Library Web site http://arxiv.org/abs/physics/0504083v1, 2005.
[7] T. Gabor, “Considerations on the Selection of an Optimum Vertical Multiphase Pressure Drop Prediction Model for Oil
Wellsâ€. Society of Petroleum Engineers, 1-5, 2001.
[8] Dae-Hyun Jeong, Young-Il Kwon, Geuntae Cho, Young-Ho Moon. An Analysis of the Effect of Joint Research
Networks on the Diffusion of Knowledge: Focusing on the Renewable Energy Field, Indian Journal of Science and
Technology, 2015, 8(S1), pp. 445-51.
[9] Balakrishna Moorthy C, Ankur Agrawal, Deshmukh M K. Artificial Intelligence Techniques for Wind Power Prediction: A
Case Study, Indian Journal of Science and Technology, 2015, 8(25), pp. 1-10.
[10] Farhad Soleimanian Gharehchopogh, Seyyed Reza Khaze, Isa Maleki. A New Approach in Bloggers Classification
with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms, Indian Journal of Science and
Technology, 2015, 8(3), pp. 237-46.
[11] A.R. Hassan and C.S. Kabir, “Determining Bottom-hole Pressures in Pumping Wells†Society of Petroleum Engineers,
25(6), pp. 823-838, 1985.
[12] S. Haykin, “Neural and Learning Machines†(3d ed), London, Pearson, 2009.
[13] I. Jahanandish, B. Salimifard, and H. Jalalifar, “Predicting bottomhole pressure in vertical multiphase flowing wells
using artificial neural networks†Journal of Petroleum Science and Engineering, vol. 75, pp. 336–342, 2011.
[14] H. Jeff, “Introduction to Neural Networks†Chesterfield: Heaton Research. Retrieved from
http://www.heatonresearch.com/book/programming-neural-networks-java-2.html, April 24, 2015
[15] M. Mohammadpoor, K. Shahbaz, F. Torabi, and A. Qazvini, “A New Methodology for prediction of buttonhole flowing
pressure in vertical Multiphase flow in Iranian Oil Fields using Artificial Neural Networks (ANNs)†Society of Petroleum
Engineers, 2010.
[16] Petroleum Exploration Company, “ Products, Retrieved from http://www.petex.com/products/?ssi=3, January 16,
2015.
[17] D. Himansu, N. Ajay, N. Bighnaraj, H. S. Behera, “A Novel PSO Based Back Propagation Learning-MLP (PSO-BPMLP)
for Classification. Proceedings of the International Conference on CIDM, pp. 461-471, 2014.
[18] M.A., Ahmadi, A., Bahadori, “Determination of oil well production performance using artificial neural network (ANN)
linked to the particle swarm optimization (PSO) tool Petroleum†http://dx.doi.org/10.1016/j.petlm, 2015.
[19] M.A. Ahmadi, M. Ebadi, S.M. Hosseini, “Prediction breakthrough time of water coning in the fractured reservoirs by
implementing low parameter support vector machine approach†Fuel, 117 (2014), pp. 579–589, 2014.
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Published
2016-09-23
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 &Amp; TECHNOLOGY, 15(12), 7263–7283. https://doi.org/10.24297/ijct.v15i12.4354
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Research Articles
