Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 210-216 |
| Number of pages | 7 |
| Journal | International Journal of Advanced Computer Science and Applications |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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Keywords
- Empirical model
- Feedforward neural networks
- Neuro-fuzzy system
- Radial basis function neural network
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Radial basis function neural network for predicting flow bottom hole pressure. / Awadallah, Medhat.
In: International Journal of Advanced Computer Science and Applications, Vol. 10, No. 1, 01.01.2019, p. 210-216.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Radial basis function neural network for predicting flow bottom hole pressure
AU - Awadallah, Medhat
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Empirical model
KW - Feedforward neural networks
KW - Neuro-fuzzy system
KW - Radial basis function neural network
UR - http://www.scopus.com/inward/record.url?scp=85063001175&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063001175&partnerID=8YFLogxK
U2 - 10.14569/IJACSA.2019.0100128
DO - 10.14569/IJACSA.2019.0100128
M3 - Article
VL - 10
SP - 210
EP - 216
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
SN - 2158-107X
IS - 1
ER -