Перейти к основному содержанию
AkademIndex

Продукты

Для разработчиков

AkademBaseОткрытый API экосистемы
← Назад к работе

Работы, на которые ссылается эта работа

Работ: 63

Работа: Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations

  1. Nanoparticle applications as beneficial oil and gas drilling fluid additives: A review

    Mohammed Al-Shargabi, Shadfar Davoodi, David A. Wood +3

    Обзорная статья2022Цитирований: 4
    ABI

  2. Без названия

    ДругоеЦитирований: 4
    ABI

  3. An efficient, cost-effective, and green natural extract in water-based drilling muds for clay swelling inhibition

    Azeem Rana, Mobeen Murtaza, Tawfik A. Saleh +2

    Статья2022Цитирований: 4
    ABI

  4. Modeling Thermal-Induced Wellhead Growth Through the Lifecycle of a Well

    Hao Yu, Zhaoyang Zhao, Arash Dahi Taleghani +2

    Препринт2023Цитирований: 3
    ABI

  5. Coupling model of wellbore heat transfer and cuttings bed height during horizontal well drilling

    Dong Xiao, Liping Chen, Xiaofeng Xu +6

    Статья2024Цитирований: 3
    ABI

  6. Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques

    Hamzeh Ghorbani, Alla Krasnikova, Parvin Ghorbani +7

    Статья2023Цитирований: 3
    ABI

  7. Effect of water-based drilling fluid components on filter cake structure

    Rugang Yao, Guancheng Jiang, Wei Li +2

    Статья2014Цитирований: 3
    ABI

  8. Filter Cake Properties of Water-Based Drilling Fluids Under Static and Dynamic Conditions Using Computed Tomography Scan

    Salaheldin Elkatatny, Mohamed Mahmoud, H. A. Nasr‐El‐Din

    Статья2013Цитирований: 3
    ABI

  9. Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids

    Shadfar Davoodi, Mohammed Murif Al-Rubaii, David A. Wood +3

    Статья2024Цитирований: 3
    ABI

  10. Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

    Hamzeh Ghorbani, Sahar Lajmorak, Simin Ghorbani +7

    Статья2023Цитирований: 2
    ABI

  11. Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents

    Saad Alatefi, Okorie Ekwe Agwu, Menad Nait Amar +1

    Статья2025Цитирований: 2
    ABI

  12. Без названия

    ДругоеЦитирований: 2
    ABI

  13. Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions

    Ahmad Alkouh, Khaled A. Elraies, Okorie Ekwe Agwu +2

    Статья2024Цитирований: 2
    ABI

  14. Carbon capture using ionic liquids: An explicit data driven model for carbon (IV) Oxide solubility estimation

    Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh +2

    Статья2024Цитирований: 2
    ABI

  15. Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen

    Saad Alatefi, Okorie Ekwe Agwu, Ahmad Alkouh

    Статья2024Цитирований: 2
    ABI

  16. Evolving ANFIS model to estimate density of bitumen-tetradecane mixtures

    Peyman Abbasi, Mohammad Madani, Alireza Baghban +1

    Статья2017Цитирований: 2
    ABI

  17. Prediction method of ground settlement for rectangular tunnel construction

    Da Hu, Jing Liu, Yongsuo Li +1

    Статья2025Цитирований: 2
    ABI

  18. Filtration Behavior of Circulating Drilling Fluids

    C. Bezemer, I. Havenaar

    Статья1966Цитирований: 2
    ABI

  19. Effects of particle-size distribution and solid additives in the apparent viscosity of drilling fluids

    David Ribeiro do Nascimento, Beatriz Rosas de Oliveira, Victor G. P. Saide +3

    Статья2019Цитирований: 2
    ABI

  20. Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs

    Nediljka Gaurina-Međimurec, Borivoje Pašić, Petar Mijić +1

    Статья2021Цитирований: 2
    ABI

  21. Reparameterization of static filtration model of aqueous-based drilling fluids for simultaneous estimation of compressible mudcake parameters

    R.F.O. Borges, Bruno Francisco Oechsler, Beatriz Rosas de Oliveira +4

    Статья2021Цитирований: 2
    ABI

  22. Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations

    Badr Bageri, Hany Gamal, Salaheldin Elkatatny +1

    Статья2021Цитирований: 2
    ABI

  23. Lost circulation prediction based on machine learning

    Huiwen Pang, Han Meng, Hanqing Wang +3

    Статья2021Цитирований: 2
    ABI

  24. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost

    Ziqi Li

    Статья2022Цитирований: 2
    ABI

  25. A review on the effect of nanoparticle in drilling fluid on filtration and formation damage

    Mohamad Arif Ibrahim, Mohd Zaidi Jaafar, Muhammad Aslam Md Yusof +1

    Обзорная статья2022Цитирований: 2
    ABI