Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 network applications to reservoirs: Physics-based models and data models
Tahar Aı̈fa
Journal of Petroleum Science and Engineering (2014) Vol. 123, pp. 1-6
Open Access | Times Cited: 24

Showing 24 citing articles:

Attention-based LSTM network-assisted time series forecasting models for petroleum production
Indrajeet Kumar, Bineet Kumar Tripathi, Anugrah Singh
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106440-106440
Closed Access | Times Cited: 55
Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network
Wei Liu, Wei David Liu, Jianwei Gu
Journal of Petroleum Science and Engineering (2020) Vol. 189, pp. 107013-107013
Closed Access | Times Cited: 136
Applicability of deep neural networks on production forecasting in Bakken shale reservoirs
Shuhua Wang, Zan Chen, Shengnan Chen
Journal of Petroleum Science and Engineering (2019) Vol. 179, pp. 112-125
Closed Access | Times Cited: 106
A novel deep learning method based on 2-D CNNs and GRUs for permeability prediction of tight sandstone
Yinhong Tian, Guiwen Wang, Hongbin Li, et al.
Geoenergy Science and Engineering (2024) Vol. 238, pp. 212851-212851
Closed Access | Times Cited: 9
When Petrophysics Meets Big Data: What can Machine Do?
Chicheng Xu, Siddharth Misra, Poorna Srinivasan, et al.
SPE Middle East Oil and Gas Show and Conference (2019)
Closed Access | Times Cited: 59
Lithology identification using principal component analysis and particle swarm optimization fuzzy decision tree
Quan Ren, Hongbing Zhang, Dailu Zhang, et al.
Journal of Petroleum Science and Engineering (2022) Vol. 220, pp. 111233-111233
Closed Access | Times Cited: 34
Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China
Wenpeng Bai, Shiqing Cheng, Xin-Yang Guo, et al.
Petroleum Science (2024) Vol. 21, Iss. 4, pp. 2554-2570
Open Access | Times Cited: 4
Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling
Jiří Navrátil, Alan King, Jesus Rios, et al.
Frontiers in Big Data (2019) Vol. 2
Open Access | Times Cited: 30
STNet: Advancing Lithology Identification with a Spatiotemporal Deep Learning Framework for Well Logging Data
Qingwei Pang, Chenglizhao Chen, Youzhuang Sun, et al.
Natural Resources Research (2024)
Closed Access | Times Cited: 3
Adaptive modeling of waterflooding process in oil reservoirs
Farzad Hourfar, Behzad Moshiri, Karim Salahshoor, et al.
Journal of Petroleum Science and Engineering (2016) Vol. 146, pp. 702-713
Closed Access | Times Cited: 14
A systematic approach for modeling of waterflooding process in the presence of geological uncertainties in oil reservoirs
Farzad Hourfar, Karim Salahshoor, Hosein Zanbouri, et al.
Computers & Chemical Engineering (2018) Vol. 111, pp. 66-78
Closed Access | Times Cited: 13
Application of Locality Preserving Projection-Based Unsupervised Learning in Predicting the Oil Production for Low-Permeability Reservoirs
Yuan Zhang, Jinghong Hu, Qi Zhang
SPE Journal (2020) Vol. 26, Iss. 03, pp. 1302-1313
Closed Access | Times Cited: 12
An End-to-End Deep Sequential Surrogate Model for High Performance Reservoir Modeling: Enabling New Workflows
Jiří Navrátil, G. De Paola, Georgos Kollias, et al.
SPE Annual Technical Conference and Exhibition (2020)
Closed Access | Times Cited: 9
System Identification Based Proxy Model of a Reservoir under Water Injection
Berihun Mamo Negash, Lemma Dendena Tufa, M. Ramasamy, et al.
Modelling and Simulation in Engineering (2017) Vol. 2017, pp. 1-10
Open Access | Times Cited: 9
Advanced fluid prediction using interpretable spatio-temporal network and sHapley additive exPlanations through well logging data
Qingwei Pang, Chenglizhao Chen, Shanchen Pang
Physics of Fluids (2024) Vol. 36, Iss. 8
Closed Access
An intelligent model for predicting wax deposition thickness during turbulent flow of oil
Amir Hossein Saeedi Dehaghani
Petroleum Science and Technology (2017) Vol. 35, Iss. 16, pp. 1706-1711
Closed Access | Times Cited: 3
Feature engineering process on well log data for machine learning-based SAGD performance prediction
Namhwa Kim, Hyundon Shin, Kyungbook Lee
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212057-212057
Closed Access | Times Cited: 1
The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness
Sergey Krivoshchekov, Alexander Kochnev, Evgeny Ozhgibesov
Energies (2022) Vol. 15, Iss. 5, pp. 1917-1917
Open Access | Times Cited: 2
Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching
Dongmei Zhang, Yuyang Zhang, Bohou Jiang, et al.
Energies (2020) Vol. 13, Iss. 17, pp. 4290-4290
Open Access | Times Cited: 2
An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry
Rui L. Lopes, Hamed Nikhalat‐Jahromi, Alí­pio Jorge
(2016)
Open Access | Times Cited: 1
Long-Term Forecasting and Optimization of Non-Stationary Well Operation Modes Through Neural Networks Simulation
Roman Yurievich Ponomarev, Vladimir Evgenievich Vershinin
SPE Russian Petroleum Technology Conference (2021)
Closed Access | Times Cited: 2
DEEP GENERATIVE MODELS FOR RESERVOIR DATA: AN APPLICATION IN SMART WELLS
Allan Gurwicz
(2020)
Open Access | Times Cited: 1
Improved fixed-point seismic inversion constrained by instantaneous phase
Song Pei, Xingyao Yin, Kun Li
Journal of Petroleum Science and Engineering (2022) Vol. 215, pp. 110653-110653
Closed Access | Times Cited: 1
A novel approach to estimate reservoir permeability using machine learning method
R. Nasimi, Sassan Azadi, Mehdi Farzinfar, et al.
International journal of nonlinear analysis and applications (2021) Vol. 12, pp. 133-145
Closed Access | Times Cited: 1
Page 1

Scroll to top