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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:

Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms
Mohammad Farsi, Nima Mohamadian, Hamzeh Ghorbani, et al.
Natural Resources Research (2021) Vol. 30, Iss. 5, pp. 3455-3481
Closed Access | Times Cited: 56

Showing 26-50 of 56 citing articles:

Research on prediction methods of formation pore pressure based on machine learning
Honglin Huang, Jun Li, Hongwei Yang, et al.
Energy Science & Engineering (2022) Vol. 10, Iss. 6, pp. 1886-1901
Open Access | Times Cited: 16
Pore pressure prediction assisted by machine learning models combined with interpretations: A case study of an HTHP gas field, Yinggehai Basin
Xiaobo Zhao, Xiaojun Chen, Zhangjian Lan, et al.
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212114-212114
Closed Access | Times Cited: 9
Research progress of machine-learning algorithm for formation pore pressure prediction
Haoyu Pan, Song Deng, Chaowei Li, et al.
Petroleum Science and Technology (2024), pp. 1-19
Closed Access | Times Cited: 2
Novel Deep Learning Framework for Efficient Pressure Zone Detection Via Analysis of Pore Pressure Profiling
Muhammad Hammad Rasool, Rabeea Jaffari, Maqsood Ahmad, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 2
Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks
Han Jin, Cai Liu, Zhiqi Guo
Journal of Applied Geophysics (2024), pp. 105526-105526
Closed Access | Times Cited: 2
Committee Machine Learning: A Breakthrough in the Precise Prediction of CO2 Storage Mass and Oil Production Volumes in Unconventional Reservoirs
Shadfar Davoodi, Hung Vo Thanh, David A. Wood, et al.
Geoenergy Science and Engineering (2024), pp. 213533-213533
Closed Access | Times Cited: 2
Predicting Shale Volume from Seismic Traces Using Modified Random Vector Functional Link Based on Transient Search Optimization Model: A Case Study from Netherlands North Sea
Mohamed Abd Elaziz, Ashraf Ghoneimi, Ammar H. Elsheikh, et al.
Natural Resources Research (2022) Vol. 31, Iss. 3, pp. 1775-1791
Closed Access | Times Cited: 10
Contribution of Fluid Substitution and Cheetah Optimizer Algorithm in Predicting Rock-Physics Parameters of Gas-Bearing Reservoirs in the Eastern Mediterranean Sea, Egypt
Mohamed Abd Elaziz, Ashraf Ghoneimi, Muhammad Nabih, et al.
Natural Resources Research (2023) Vol. 32, Iss. 5, pp. 1987-2005
Open Access | Times Cited: 4
An adaptive physics-informed deep learning method for pore pressure prediction using seismic data
Xin Zhang, Yunhu Lu, Yan Jin, et al.
Petroleum Science (2023) Vol. 21, Iss. 2, pp. 885-902
Open Access | Times Cited: 4
Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory
Marina Yusoff, Darul Ehsan, Muhammad Sharif, et al.
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Research on Adaptive Feature Optimization and Drilling Rate Prediction Based on Real-Time Data
Jun Ren, Jie Jiang, Changchun Zhou, et al.
(2024)
Closed Access | Times Cited: 1
Handling missing data in well-log curves with a gated graph neural network
Chunbi Jiang, Dongxiao Zhang, Shifeng Chen
Geophysics (2022) Vol. 88, Iss. 1, pp. D13-D30
Closed Access | Times Cited: 7
Collaborative-driven reservoir formation pressure prediction using GAN-ML models and well logging data
Fang Shi, Hualin Liao, Fengtao Qu, et al.
Geoenergy Science and Engineering (2024) Vol. 242, pp. 213271-213271
Closed Access | Times Cited: 1
Mesoscopic theoretical modeling and experimental study of rheological behavior of water-based drilling fluid containing associative synthetic polymer, bentonite, and limestone
Ali Kariman Moghaddam, Shadfar Davoodi, Ahmad Ramazani, et al.
Journal of Molecular Liquids (2021) Vol. 347, pp. 117950-117950
Closed Access | Times Cited: 9
Presenting a Hybrid Scheme of Machine Learning Combined with Metaheuristic Optimizers for Predicting Final Cost and Time of Project
Reza Bakhshi, Sina Fard Moradinia, Rasoul Jani, et al.
KSCE Journal of Civil Engineering (2022) Vol. 26, Iss. 8, pp. 3188-3203
Closed Access | Times Cited: 6
Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types
Zeeshan Tariq, Bicheng Yan, Shuyu Sun, et al.
ACS Omega (2022) Vol. 7, Iss. 45, pp. 41314-41330
Open Access | Times Cited: 4
Dataset insight and variable influences established using correlations, regressions, and transparent customized formula optimization
David A. Wood
Elsevier eBooks (2021), pp. 383-408
Closed Access | Times Cited: 5
Artificial Neural Network-Based Caprock Structural Reliability Analysis for CO2 Injection Site—An Example from Northern North Sea
Sajjad Ahmadi Goltapeh, Md Jamilur Rahman, Nazmul Haque Mondol, et al.
Energies (2022) Vol. 15, Iss. 9, pp. 3365-3365
Open Access | Times Cited: 3
A data-driven approach to estimate the rate of penetration in drilling of hydrocarbon reservoirs
Abbas Hashemizadeh, Ehsan Bahonar, Mohammad Chahardowli, et al.
Research Square (Research Square) (2022)
Open Access | Times Cited: 3
Digital graphic monitoring of energy condition of oil reservoirs
Lev A. Zakharov, Inna N. Ponomareva, Dmitriy A. Martyushev
Bulletin of the Tomsk Polytechnic University Geo Assets Engineering (2024) Vol. 335, Iss. 5, pp. 131-141
Open Access
Pore pressure prediction of hydrocarbon reservoirs with empirical models and artificial neural network: case study in the Doba basin, Chad
Justine Bawane Godwe, Luc Leroy Mambou Ngueyep, Jordan Eze Eze, et al.
Discover Geoscience (2024) Vol. 2, Iss. 1
Open Access
Geomechanical methods for pore pressure prediction in complex geological structures: a case study of a field in southwest of Iran
Amin Ahmadi, Mohsen Saemi, Alireza Shahnazi, et al.
Arabian Journal of Geosciences (2024) Vol. 17, Iss. 10
Closed Access
Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations
Hugo Tamoto, Rafael dos Santos Gioria, Cleyton de Carvalho Carneiro
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212138-212138
Closed Access | Times Cited: 1
ENHANCING PORE PRESSURE PREDICTION IN OIL WELL DRILLING: A COMPREHENSIVE STUDY OF WELL PLANNING AND COST-EFFECTIVE MODELING IN THE NIGER DELTA REGION
Kelechi Anthony Ofonagoro, Olawe Alaba Tula, Joachim Osheyor Gidiagba, et al.
Engineering Heritage Journal (2023) Vol. 7, Iss. 2, pp. 167-177
Open Access | Times Cited: 1
Wellbore stability and the establishment of a safe mud weight window
David A. Wood
Elsevier eBooks (2024), pp. 135-168
Closed Access

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