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Работы, на которые ссылается эта работа

Работ: 36

Работа: Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

  1. Machine learning-based estimation of nano-lubricants viscosity in different operating conditions

    Amin Bemani, Mohammad Madani, Alireza Kazemi

    Статья2023Цитирований: 25
    ABI

  2. Modeling apparent viscosity of waxy crude oils doped with polymeric wax inhibitors

    Mohammad Madani, Mostafa Keshavarz Moraveji, Mohammad Sharifi

    Статья2020Цитирований: 20
    ABI

  3. A novel hybrid PD-FEM-FVM approach for simulating hydraulic fracture propagation in saturated porous media

    Daosheng Zhang, Zongqing Zhou, Chenglu Gao +4

    Статья2024Цитирований: 5
    ABI

  4. Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme

    Emad A. El-Sebakhy

    Статья2008Цитирований: 5
    ABI

  5. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

    Yoav Freund, Robert E. Schapire

    Статья1997Цитирований: 3
    ABI

  6. A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid

    Mohammad Hemmat Esfe, S. Ali Eftekhari, Maboud Hekmatifar +1

    Статья2021Цитирований: 3
    ABI

  7. Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms

    Mahsa Hajihosseinlou, Abbas Maghsoudi, Reza Ghezelbash

    Статья2024Цитирований: 3
    ABI

  8. Evolution characteristic and mechanism of microstructure, hydraulic and mechanical behaviors of sandstone treated by acid-rock reaction: Application of in-situ leaching of uranium deposits

    Mingwei Hu, Qinghe Niu, Wei Yuan +12

    Статья2024Цитирований: 3
    ABI

  9. Data driven simulations for accurately predicting thermodynamic properties of H2 during geological storage

    Mohamad Reza Soltanian, Amin Bemani, Farzad Moeini +5

    Статья2024Цитирований: 3
    ABI

  10. Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview

    Ali Mohebbi, Hossein Kaydani

    Глава2015Цитирований: 2
    ABI

  11. Advances in kernel methods: support vector learning

    Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola

    Статья1999Цитирований: 2
    ABI

  12. Assessment of asphaltene deposition due to titration technique

    Ali Chamkalani, Mahmood Amani, Mohammad Amin Kiani +1

    Статья2012Цитирований: 2
    ABI

  13. Estimating the Isothermal Compressibility Coefficient of Undersaturated Middle East Crudes Using Neural Networks

    Ridha Gharbi

    Статья1997Цитирований: 2
    ABI

  14. A Water Saturation Prediction Using Artificial Neural Networks and an Investigation on Cementation Factors and Saturation Exponent Variations in an Iranian Oil Well

    M. Mardi, H. Nurozi, S. Edalatkhah

    Статья2011Цитирований: 2
    ABI

  15. Improving the Simulation of Waterflood Performance With the Use of Neural Networks

    K. Aminian, H. I. Bilgesu, S. Ameri +1

    Статья2000Цитирований: 2
    ABI

  16. Analysis of Archie's Parameters Determination Techniques

    G. M. Hamada

    Статья2009Цитирований: 2
    ABI

  17. Effect of Variable Saturation Exponent on the Evaluation of Hydrocarbon Saturation

    Paul F. Worthington, Nadla Pallatt

    Статья1992Цитирований: 2
    ABI

  18. Handwriting recognition using local methods for normalization and global methods for recognition

    C. Choisy, Abdel Belaı̈d

    Статья2002Цитирований: 2
    ABI

  19. Modeling the permeability of heterogeneous oil reservoirs using a robust method

    Arash Kamari, Farzaneh Moeini, Mohammad-Javad Shamsoddini-Moghadam +3

    Статья2015Цитирований: 2
    ABI

  20. Simultaneous Determination of Archie’s Parameters by Application of Modified Genetic Algorithm and HDP Methods

    I. Najafi, A. Abbasi Goodarzi

    Статья2011Цитирований: 2
    ABI

  21. Measurement of the dynamic viscosity of hybrid engine oil -Cuo-MWCNT nanofluid, development of a practical viscosity correlation and utilizing the artificial neural network

    Alireza Aghaei, Hossein Khorasanizadeh, Ghanbar Ali Sheikhzadeh

    Статья2017Цитирований: 2
    ABI

  22. De-risking Pretreatment of Microalgae To Produce Fuels and Chemical Co-products

    Jacob S. Kruger, Skylar Schutter, Eric P. Knoshaug +9

    Статья2024Цитирований: 2
    ABI

  23. Analysis of Pore Types in Lower Cretaceous Qingshankou Shale Influenced by Electric Heating

    Zhentao Dong, Shansi Tian, Haitao Xue +5

    Статья2024Цитирований: 2
    ABI

  24. Без названия

    ДругоеЦитирований: 1
    ABI

  25. Без названия

    ДругоеЦитирований: 1
    ABI