Deep neural networks predicting oil movement in a development unit

ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Adam: A Method for Stochastic Optimization

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Auto-Encoding Variational Bayes

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

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Layer NormalizationPreprint

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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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Empirical Evaluation of Rectified Activations in Convolutional Network

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An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations

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A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices

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Basic Applied Reservoir Simulation

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A Capacitance Model To Infer Interwell Connectivity From Production and Injection Rate Fluctuations

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Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw ImagesPreprint

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Data Driven Production Forecasting Using Machine Learning

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Discrete Empirical Interpolation for nonlinear model reduction

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Surrogate and Reduced‐Order Modeling: A Comparison of Approaches for Large‐Scale Statistical Inverse Problems

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INSIM: A Data-Driven Model for History Matching and Prediction for Waterflooding Monitoring and Management with a Field Application

Zhao, H., Kang, Z., Zhang, X., Sun, H., Cao, L. L., & Reynolds, A. C. (2015) · SPE Reservoir Simulation Conference · doi:10.2118/173213-ms · Cited by 95 · see citing works

Full field reservoir modeling of shale assets using advanced data-driven analytics

Esmaili, S., & Mohaghegh, S. D. (2015) · Geoscience Frontiers · doi:10.1016/j.gsf.2014.12.006 · Cited by 89 · see citing works

Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks

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Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

Cao, F., Luo, H., & Lake, L. W. (2015) · SPE Reservoir Evaluation & Engineering · doi:10.2118/173315-pa · Cited by 70 · see citing works

Development of a Fully Coupled Two-phase Flow Based Capacitance Resistance Model CRM

Cao, F., Luo, H., & Lake, L. W. (2014) · SPE/DOE Improved Oil Recovery Symposium · doi:10.2118/169485-ms · Cited by 70 · see citing works

Evaluating the critical performances of a CO2–Enhanced oil recovery process using artificial neural network models

Van, S. L., & Chon, B. H. (2017) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2017.07.034 · Cited by 65 · see citing works

Robust Locally-Linear Controllable EmbeddingPreprint

Banijamali, E., Shu, R., Ghavamzadeh, M., Bui, H., & Ghodsi, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.05373 · Cited by 59 · see citing works

GTApprox: Surrogate modeling for industrial design

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Large scale variable fidelity surrogate modeling

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A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Esmaeilzadeh, S., Salehi, A., Hetz, G., Olalotiti-Lawal, F., Darabi, H., & Castiñeira, D. (2019) · Day 2 Wed, April 24, 2019 · doi:10.2118/195329-ms · Cited by 30 · see citing works

Characterizing Reservoir Connectivity and Forecasting Waterflood Performance Using Data-Driven and Reduced-Physics Models

Artun, E. (2016) · SPE Western Regional Meeting · doi:10.2118/180488-ms · Cited by 24 · see citing works

History Matching With Cumulative Production Data

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Surrogate modeling of multifidelity data for large samples

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A novel approach for solving nonlinear flow equations: The next step towards an accurate assessment of shale gas resources

Bezyan, Y., Ebadi, M., Gerami, S., Rafati, R., Sharifi, M., & Koroteev, D. A. (2018) · Fuel · doi:10.1016/j.fuel.2018.08.157 · Cited by 18 · see citing works

New Approach to PVT Correlation Selection

Khabibullin, R. A., Khasanov, M., Brusilovsky, A. I., Odegov, A., Serebryakova, D., & Krasnov, V. (2014) · SPE Russian Oil and Gas Technical Conference and Exhibition · doi:10.2118/171241-ms · Cited by 8 · see citing works

Reduced order modeling of subsurface multiphase flow models using deep residual recurrent neural networksPreprint

Kani, J. N., & Elsheikh, A. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10422 · Cited by 2 · see citing works

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

Koroteev, D., & Tekic, Z. (2021) · Energy and AI · doi:10.1016/j.egyai.2020.100041 · Cited by 285 · see citing works

Deep-learning-based surrogate model for reservoir simulation with time-varying well controls

Jin, Z., Liu, Y., & Durlofsky, L. J. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107273 · Cited by 147 · see citing works

Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field

Sabah, M., Mehrad, M., Ashrafi, S. B., Wood, D. A., & Fathi, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.108125 · Cited by 88 · see citing works

Log data-driven model and feature ranking for water saturation prediction using machine learning approach

Miah, M. I., Zendehboudi, S., & Ahmed, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107291 · Cited by 87 · see citing works

Application of Machine Learning Techniques in Mineral Classification for Scanning Electron Microscopy - Energy Dispersive X-Ray Spectroscopy (SEM-EDS) Images

Li, C., Wang, D., & Kong, L. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.108178 · Cited by 78 · see citing works

Shear wave velocity prediction based on adaptive particle swarm optimization optimized recurrent neural network

Wang, J., Cao, J., & Yuan, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107466 · Cited by 63 · see citing works

Rock physics model-based prediction of shear wave velocity utilizing machine learning technique for a carbonate reservoir, southwest Iran

Azadpour, M., Saberi, M. R., Javaherian, A., & Shabani, M. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107864 · Cited by 49 · see citing works

Application of Artificial Intelligence in the Oil and Gas Industry

Hussain, M. N., Alamri, A. H., Zhang, T., & Jamil, I. (2024) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-031-50300-9_19 · Cited by 48 · see citing works

An integrated fracture parameter prediction and characterization method in deeply-buried carbonate reservoirs based on deep neural network

Yasin, Q., Ding, Y. F., Baklouti, S., Boateng, C. D., Du, Q., & Golsanami, N. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109346 · Cited by 43 · see citing works

Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review

Krishna, S., Ridha, S., Vasant, P., Ilyas, S. U., & Sophian, A. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107818 · Cited by 41 · see citing works

Application of artificial neural network to multiphase flow metering: A review

Bahrami, S., Alamdari, S., Farajmashaei, M., Behbahani, M., Jamshidi, S., & Bahrami, B. (2024) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2024.102601 · Cited by 40 · see citing works

Drilling performance improvement in offshore batch wells based on rig state classification using machine learning

Yin, Q., Yang, J., Hou, X., Tyagi, M., Zhou, X., Cao, B., Sun, T., Li, L., & Xu, D. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107306 · Cited by 40 · see citing works

Evaluation of active learning algorithms for formation lithology identification

Xu, T., Chang, J., Feng, D., Lv, W., Kang, Y., Liu, H., Li, J., & Li, Z. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108999 · Cited by 39 · see citing works

Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

Zhang, K., Lin, N., Tian, G., Yang, J., Wang, D., & Jin, Z. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109964 · Cited by 39 · see citing works

Advances in Machine-Learning-Driven CO 2 Geological Storage: A Comprehensive Review and Outlook

Lin, K., Wei, N., Zhang, Y., Ali, M., Chen, Q., Wang, W., Song, Z., Yin, Y., & Thanh, H. V. (2025) · Energy & Fuels · doi:10.1021/acs.energyfuels.5c02370 · Cited by 37 · see citing works

Multiphase flowrate measurement with time series sensing data and sequential model

Wang, H., Hu, D., Zhang, M., & Yang, Y. (2021) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2021.103875 · Cited by 36 · see citing works

Potential application of generative artificial intelligence and machine learning algorithm in oil and gas sector: Benefits and future prospects

Ochieng, E. G., Ominde, D., & Zuofa, T. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102710 · Cited by 38 · see citing works

Data-driven lithology prediction for tight sandstone reservoirs based on new ensemble learning of conventional logs: A demonstration of a Yanchang member, Ordos Basin

Gu, Y., Zhang, D., Lin, Y.-B., Ruan, J., & Bao, Z. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109292 · Cited by 34 · see citing works

Machine learning for recovery factor estimation of an oil reservoir: A tool for derisking at a hydrocarbon asset evaluation

Makhotin, I., Orlov, D., Koroteev, D. A., Burnaev, E. V., Karapetyan, A., & Antonenko, D. A. (2021) · Petroleum · doi:10.1016/j.petlm.2021.11.005 · Cited by 29 · see citing works

End-to-end neural network approach to 3D reservoir simulation and adaptation

Illarionov, E. A., Temirchev, P., Voloskov, D., Kostoev, R., Simonov, M., Pisarenko, D., Orlov, D., & Koroteev, D. A. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109332 · Cited by 29 · see citing works

Prediction of drilling plug operation parameters based on incremental learning and CNN-LSTM

Liu, S., Yuandeng, W., & Huang, R. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212631 · Cited by 28 · see citing works

AI-Based Estimation of Hydraulic Fracturing Effect

Erofeev, A. S., Orlov, D., Perets, D. S., & Koroteev, D. A. (2021) · SPE Journal · doi:10.2118/205479-pa · Cited by 28 · see citing works

Improving the accuracy of short-term multiphase production forecasts in unconventional tight oil reservoirs using contextual Bi-directional long short-term memory

Kocoglu, Y., Gorell, S. B., Emadi, H., Eyinla, D. S., Bolouri, F., Kocoglu, Y. C., & Arora, A. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212688 · Cited by 26 · see citing works

Machine Learning for Proxy Modeling of Dynamic Reservoir Systems: Deep Neural Network DNN and Recurrent Neural Network RNN Applications

Chaki, S., Zagayevskiy, Y., Shi, X., Wong, T., & Noor, Z. (2020) · International Petroleum Technology Conference · doi:10.2523/iptc-20118-ms · Cited by 26 · see citing works

Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from brazilian carbonate reservoirs

Bom, C. R., Valentín, M. B., Fraga, B., Campos, J., Coutinho, B., Dias, L. O., Faria, E. L., de Albuquerque, M. P., de Albuquerque, M. P., & Correia, M. D. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108361 · Cited by 24 · see citing works

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

Makhotin, I., Orlov, D., & Koroteev, D. A. (2022) · Energies · doi:10.3390/en15031199 · Cited by 23 · see citing works

Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East

Wei, C., Huang, R., Ding, M., Yang, J., & Xiong, L. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110442 · Cited by 23 · see citing works

The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells

Najjarpour, M., Jalalifar, H., & Norouzi-Apourvari, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107160 · Cited by 23 · see citing works

Fault and fracture network characterization using seismic data: a study based on neural network models assessment

Yasin, Q., Majdański, M., Sohail, G. M., & Thanh, H. V. (2022) · Geomechanics and Geophysics for Geo-Energy and Geo-Resources · doi:10.1007/s40948-022-00352-y · Cited by 22 · see citing works

Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs

Jia, D., Zhang, J., Li, Y., Wu, L., & Qiao, M. (2023) · Sustainability · doi:10.3390/su15010784 · Cited by 22 · see citing works

Self-adaptive and time divide-and-conquer physics-informed neural networks for two-phase flow simulations using interface tracking methods

Zhou, W., Miwa, S., & Okamoto, K. (2024) · Physics of Fluids · doi:10.1063/5.0214646 · Cited by 21 · see citing works

Modeling of meandering river deltas based on the conditional generative adversarial network

Gao, X., He, W., & Hu, Y. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107352 · Cited by 18 · see citing works

Forecasting the evolution of chaotic dynamics of two-phase slug flow regime

Ali, N., Viggiano, B., Tutkun, M., & Cal, R. B. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108904 · Cited by 15 · see citing works

Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study

Sudakov, O., Koroteev, D. A., Belozerov, B. V., & Burnaev, E. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22808-8_24 · Cited by 14 · see citing works

Estimating hydrocarbon recovery factor at reservoir scale via machine learning: Database-dependent accuracy and reliability

Roustazadeh, A., Ghanbarian, B., Shadmand, M. B., Taslimitehrani, V., & Lake, L. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107500 · Cited by 13 · see citing works

A Critical Review of Capacitance-Resistance Models

Al‐Ghamdi, A. A., Hiba, M., Aly, M., & Awotunde, A. A. (2021) · SPE Russian Petroleum Technology Conference · doi:10.2118/206555-ms · Cited by 13 · see citing works

Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models

Krishna, S., Irfan, S. A., Keshavarz, S., Thonhauser, G., & Ilyas, S. U. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00542-z · Cited by 10 · see citing works

Top-of-line corrosion via physics-guided machine learning: A methodology integrating field data with theoretical models

Silakorn, P., Jantrakulchai, N., Wararatkul, N., Wanwilairat, S., Kangkachit, T., Techapiesancharoenkij, R., Rakthanmanon, T., & Hanlumyuang, Y. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110558 · Cited by 10 · see citing works

Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models

Abadía-Heredia, R., Corrochano, A., López‐Martín, M., & Clainche, S. L. (2025) · Physics of Fluids · doi:10.1063/5.0253876 · Cited by 9 · see citing works

Proxy Capacitance-Resistance Modeling for Well Production Forecasts in Case of Well Treatments

Gubanova, A. E., Orlov, D., Koroteev, D. A., & Shmidt, S. (2022) · SPE Journal · doi:10.2118/209829-pa · Cited by 8 · see citing works

Artificial Intelligent in Upstream Oil and Gas Industry: A Review of Applications, Challenges and Perspectives

Kenioua, A., Ammar, T. B., & Laid, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96311-8_24 · Cited by 6 · see citing works

3D Reservoir Model History Matching Based on Machine Learning Technology (Russian)

Illarionov, E. A., Temirchev, P., Voloskov, D., Gubanova, A. E., Koroteev, D. A., Simonov, M., Akhmetov, A., & Margarit, A. (2020) · SPE Russian Petroleum Technology Conference · doi:10.2118/201924-ru · Cited by 6 · see citing works

Fast modelling of gas reservoir performance with proper orthogonal decomposition based autoencoder and radial basis function non-intrusive reduced order models

Samuel, J.-S., & Muggeridge, A. H. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.110011 · Cited by 6 · see citing works

Adaptation of Steady-State Well Flow Model on Field Data for Calculating the Flowing Bottomhole Pressure

Baryshnikov, E. S., Kanin, E. A., Osiptsov, A. A., Vainshtein, A. L., Burnaev, E. V., Paderin, G. V., Prutsakov, A. S., & Ternovenko, S. O. (2020) · SPE Russian Petroleum Technology Conference · doi:10.2118/201942-ms · Cited by 5 · see citing works

Prediction of the volume fraction of liquid-liquid two-phase flow in horizontal pipes using Long-Short Term Memory Networks

Hernández-Salazar, C. A., Carreño-Verdugo, A., & González‐Estrada, O. A. (2024) · Revista UIS Ingenierías · doi:10.18273/revuin.v23n3-2024002 · Cited by 5 · see citing works

Adaptive POD-Galerkin Technique for Reservoir Simulation and Optimization

Voloskov, D., & Pissarenko, D. (2021) · Mathematical Geosciences · doi:10.1007/s11004-021-09958-6 · Cited by 4 · see citing works

Capturing intrinsic features from field data for predicting the production of natural gas

Wang, X., Wang, Y., Pang, L.-S., Jiang, T., Chen, Y., Wang, Y., Mei, Q., Qing, S.-L., & Jiang, W. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.211736 · Cited by 4 · see citing works

A hybrid model based on unsupervised learning for seismic response feature extraction and gas-bearing reservoir prediction using longitudinal and transverse waves seismic data in low-degree exploration areas: A case study

Zhang, K., Lin, N., Yang, J., Fu, C., & Zhang, D. (2024) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2024.105382 · Cited by 3 · see citing works

A novel multiphase prediction model for shale oil production

Qi, N., Lei, Z., Li, X., Chen, Z., Zhou, X., Wang, L., Zhou, M., Pei, X., Wang, L., & He, S. (2025) · Fuel · doi:10.1016/j.fuel.2025.137048 · Cited by 3 · see citing works

Efficiency of Using a Proxy Model for Modeling of Reservoir Pressure

Yudin, E. V., Markov, N. S., Kotezhekov, V., Kraeva, S. O., Makhnov, A., Trubnikov, N. P., & Gorbushin, L. A. (2021) · SPE Russian Petroleum Technology Conference · doi:10.2118/206553-ms · Cited by 3 · see citing works

Data-Driven Proxy Modeling of Water Front Propagation in Porous Media

Saberali, B., Zhang, K., & Golsanami, N. (2022) · International journal of computational fluid dynamics · doi:10.1080/10618562.2022.2153835 · Cited by 3 · see citing works

3D Reservoir Model History Matching Based on Machine Learning Technology

Illarionov, E. A., Temirchev, P., Voloskov, D., Gubanova, A. E., Koroteev, D. A., Simonov, M., Akhmetov, A., & Margarit, A. (2020) · SPE Russian Petroleum Technology Conference · doi:10.2118/201924-ms · Cited by 3 · see citing works

Prediction of the electromagnetic responses of geological bodies based on a temporal convolutional network model

Yuan, C., Wang, X.-B., Deng, F., Wang, K.-P., & Yang, R. (2021) · Acta Geophysica · doi:10.1007/s11600-021-00708-0 · Cited by 2 · see citing works

ПЕРКОЛЯЦИОННЫЙ ПОДХОД ПРИ ГИДРОДИНАМИЧЕСКОМ МОДЕЛИРОВАНИИ ВОЗДЕЙСТВИЯ НА ПРИЗАБОЙНУЮ ЗОНУ СКВАЖИНЫ

Сентемов, А. А., & Дорфман, М. Б. (2022) · Bulletin of the Tomsk Polytechnic University Geo Assets Engineering · doi:10.18799/24131830/2022/7/3612 · Cited by 2 · see citing works

Estimation of Oil Reservoir Transmissivity and Storativity Fields Using a Radial Basis Function Network Based on Inverse Problem Solving

Kosyakov, V. P., & Legostaev, D. Y. (2024) · Lobachevskii Journal of Mathematics · doi:10.1134/s199508022460225x · Cited by 2 · see citing works

Production rate forecasting using pressure and saturation estimates near the wellbore

Voloskov, D., Gladchenko, E. S., Akhmetov, D., Illarionov, E. A., Pechko, K. A., Afanas’ev, A. N., Simonov, M., Orlov, D., & Koroteev, D. A. (2025) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2025.213946 · Cited by 2 · see citing works

Gradient Boosting to Boost the Efficiency of Hydraulic FracturingPreprint

Makhotin, I., Koroteev, D. A., & Burnaev, E. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02223 · Cited by 2 · see citing works

High pressure experiments and data-driven flow regime identification in gas-liquid two-phase pipeline-riser systems

Chang, Y., Huang, B., Xu, Q., Liu, Y., Chen, H., Li, N., Thévenin, D., & Guo, L. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.122402 · Cited by 1 · see citing works

Predicting Water Cut Dynamics Using Hybrid Model Combining Machine Learning and Proxy Flow Model

Kosyakov, V. P., & Legostaev, D. Y. (2025) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080225606940 · Cited by 1 · see citing works

Machine learning in physico-chemical processes metamodeling

Bolkisev, A. (2019) · Химическая физика и мезоскопия · doi:10.15350/17270529.2019.4.51

Method of Multi-objective Design of Strain Gauge Force Sensors Based on Surrogate Modeling Techniques

Игоревич, Г. С., & Gavriushin, S. S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-80478-7_14

DATA-DRIVEN ULTRASONIC NON-DESTRUCTIVE EVALUATION OF PIPES AND WELDS IN THE CONTEXT OF THE OIL AND GAS INDUSTRY

Ferreira, G. R. B. (2021) · doi:10.17771/pucrio.acad.57224

Data Science Adoption and Operationalization in the O&G Industry: Challenges and Solutions

Zouch, M. (2022) · International Petroleum Technology Conference · doi:10.2523/iptc-22172-ms

Известия Томского политехнического университета. Инжиниринг георесурсов. Т. 333, № 7

(2022) · Bulletin of the Tomsk Polytechnic University Geo Assets Engineering · doi:10.18799/24131830/2022/7

Simulation of liquid production and water cut dynamics using fluid flow model and neural networks

Legostaev, D. Y., & Kosyakov, V. P. (2023) · Tyumen State University Herald Physical and Mathematical Modeling Oil Gas Energy · doi:10.21684/2411-7978-2023-9-2-75-92

Construction of adaptive reduced-order reservoir models based on POD‑DEIM approach

Voloskov, D., & Koroteev, D. A. (2023) · Georesursy · doi:10.18599/grs.2023.4.4

Production Well Placement and History Matching by Hyperparametric Optimization and Machine Learning

Donskoi, A., Medvedev, A., Shchudro, T., Terekhov, K. M., & Vassilevski, Y. V. (2024) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080224010116

Разработка многоуровневых симуляторов промышленной безопасности с элементами искусственного интеллекта для прогнозирования рисков на морских нефтедобывающих платформах

Минияров, А. Ф., Купцов, Д. В., Загитов, Т. Р., Киселев, М. О., & Галаветдинов, Д. Р. (2025) · Management of Education · doi:10.25726/r5571-4694-6805-a

Spatio-Temporal Prediction of Two-Phase Flow in Heterogeneous Reservoirs Using Deep Neural Networks

Chen, W., Chen, X., Xiao, Q., Yang, H., Tu, J., & Liu, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229380

Artificial intelligence for the energy sector/industry

Sharshir, S. W., Joseph, A., & Elsayad, M. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34019-2.00003-1

Enhancing Fractional Flow Curve Modeling with Advanced Data-driven Techniques: A Comparative Evaluation of Machine Learning Frameworks

John, C., Akinsete, O. O., Fadayomi, A. A., & Aderemi, S. (2026) · International Journal of Oil Gas and Coal Engineering · doi:10.11648/j.ogce.20261401.11

Advancing reservoir characterization and optimal sweet spot identification: A review of artificial intelligence and machine learning applications

Kalule, R., Iskandarov, J., Al-Shalabi, E. W., Abderrahmane, H. A., Alfarisi, O., & Gibrata, M. A. (2026) · Petroleum · doi:10.1016/j.petlm.2026.05.007

Development characteristics and distribution prediction of natural fractures within deep shale gas reservoirs of Luzhou area, southern Sichuan Basin

Ning, W., Ju, W., Liu, Y., Wang, Y., & Guo, W. (2026) · Geosciences Journal · doi:10.1007/s12303-026-00108-w

Applications of Artificial Intelligence in Upstream Oil and Gas Industries

Salimi, P., Nargessi, Z., & Alamolhoda, F. (2026) · International Interdisciplinary Conference on Artificial Intelligence: Engineering, Health, Finance and Humanities (IICAI) · doi:10.1109/iicai70155.2026.11620989

A deep learning based surrogate model for reservoir dynamic performance prediction

Wang, S., Xiang, J., Wang, X., Feng, Q., Yang, Y., Cao, X., & Hou, L. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212516 · Cited by 39 · see citing works

Rapid Prediction Method for Reservoir Flow Field Parameters Based on Deep Learning

Xia, T., Lu, F.-M., Wang, J.-J., Hu, R.-S., & Wang, Z.-F. (2025) · Springer series in geomechanics and geoengineering · doi:10.1007/978-981-96-4759-0_13

Surrogate Reservoir Simulation for Waterflooding Production based on Fourier Neural Operator

Ye, T., & Xiao, Y. (2024) · EAGE Annual Conference & Exhibition · doi:10.3997/2214-4609.202410972 · Cited by 3 · see citing works

Hybrid Simulation-AI Surrogate Workflow for Rapid Uncertainty Quantification of Multiphase Flow

Kula, E. F., Paydayesh, M., & Blunt, M. J. (2026) · EAGE Digitalization Conference & Exhibition · doi:10.3997/2214-4609.202639098

Data-Driven Proxy Models Based on Recurrent Neural Network for Reservoir Simulators

Tueros, J. A. R., Viana, J. D. F., Silva, V. B., & Araújo, A. F. R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15993-9_15 · Cited by 1 · see citing works