Radial basis Function Neural Network for Predicting Flow Bottom Hole Pressure

The ability to monitor the flow bottom hole pressure in pumping oil wells provides important information regarding both reservoir and artificial lift performance. This paper proposes an iterative approach to optimize the spread constant and root mean square error goal of the radial basis function neural network. In addition, the optimized network is utilized to estimate this oil well pressure. Simulated experiments and qualitative comparisons with the most related techniques such as feedforward neural networks, neuro-fuzzy system, and the empirical model have been conducted. The achieved results show that the proposed technique gives better performance in estimating the flow of bottom hole pressure. Compared with the other developed techniques, an improvement of 7.14% in the root mean square error and 3.57% in the standard deviation of relative error has been achieved. Moreover, 90% and 95% accuracy of the proposed network are attained by 99.6% and 96.9% of test data, respectively.

@article{Awadalla2019,
  title     = {Radial basis Function Neural Network for Predicting Flow Bottom Hole Pressure},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {1},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Medhat H A Awadalla},
  doi       = {10.14569/IJACSA.2019.0100128},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100128}
}