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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Neural Networks for Flow Bottom Hole Pressure Prediction
Medhat Awadalla, Hassan Yousef
International Journal of Electrical and Computer Engineering (IJECE) (2016) Vol. 6, Iss. 4, pp. 1839-1839
Open Access | Times Cited: 11

Showing 11 citing articles:

Bottomhole Pressure Prediction of Carbonate Reservoirs Using XGBoost
Hao Sun, Luo Qiang, Zhaohui Xia, et al.
Processes (2024) Vol. 12, Iss. 1, pp. 125-125
Open Access | Times Cited: 7
Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques
Nagham Amer Sami, Dhorgham Skban Ibrahim
Petroleum Research (2021) Vol. 6, Iss. 4, pp. 417-422
Open Access | Times Cited: 34
Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring
Leonardo Goliatt, Reem Sabah Mohammad, Sani I. Abba, et al.
Fuel (2023) Vol. 350, pp. 128623-128623
Closed Access | Times Cited: 14
Modelling the flowing bottom hole pressure of oil and gas wells using multivariate adaptive regression splines
Okorie E. Agwu, Saad Alatefi, Ahmad Alkouh, et al.
Journal of Petroleum Exploration and Production Technology (2025) Vol. 15, Iss. 2
Open Access
Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques
Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem
Journal of Petroleum Exploration and Production Technology (2019) Vol. 10, Iss. 4, pp. 1411-1428
Open Access | Times Cited: 38
Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction
Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem
Neural Computing and Applications (2019) Vol. 31, Iss. 12, pp. 8561-8581
Closed Access | Times Cited: 31
Machine learning insight into inhibition efficiency modelling based on modified graphene oxide of diaminohexane (DAH-GO) and diaminooctane (DAO-GO)
Kabiru Haruna, Sani I. Abba, Jamil Usman, et al.
Carbon Trends (2024) Vol. 16, pp. 100373-100373
Open Access
Production Optimization in Oil and Gas Wells: A Gated Recurrent Unit Approach to Bottom Hole Flowing Pressure Prediction
B. A. Abdullahi, M. C. Ezeh
SPE Nigeria Annual International Conference and Exhibition (2024)
Closed Access
Artificial Intelligence Models for Flowing Bottomhole Pressure Estimation: State-of-the-Art and Proposed Future Research Directions
Okorie E. Agwu, Saad Alatefi, Ahmad Alkouh, et al.
International Journal on Advanced Science Engineering and Information Technology (2024) Vol. 14, Iss. 6, pp. 1868-1879
Closed Access
Radial basis Function Neural Network for Predicting Flow Bottom Hole Pressure
Medhat Awadalla
International Journal of Advanced Computer Science and Applications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 3
New Method for Flow Rate and Bottom-Hole Pressure Prediction Based on Support Vector Regression
Bing Zhang, Jinlong Wang, Ningsheng Zhang
Springer series in geomechanics and geoengineering (2020), pp. 3812-3829
Closed Access | Times Cited: 3
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