IDEAS home Printed from https://ideas.repec.org/a/epw/energy/v2y2022i4id7077.html

Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow

Author

Listed:
  • Hillary Mengich
  • Michael Kabugu
  • Mary Nelima Ondiaka

Abstract

Pipe sticking in drilling operations occurs by mechanical and differential forces caused by the loss in circulation of drilling fluids or muds. Rheological properties of recirculating drilling muds (RDMs) determine the drilling hydraulics and hole cleaning effectiveness. Uncertainty in real-time data on the rheological properties of RDMs challenges estimation of potential pipe sticking problems, a problem that can be improved using machine learning methods. This study reports first-time application of a supervised artificial neural networks (ANN) algorithm with TensorFlow to estimate plastic viscosity (PV), apparent viscosity (AV), yield point (YP), flow consistency index (k), and flow behavior index (n), five rheological properties of recirculating water-based drilling mud (R-WBDM). Model input variables were density (MD), marsh funnel viscosity (MFV), and percent solids content (% S) of R-WBDM. Model performance was tested using the root mean square error (RMSE) and coefficient of determination (R2) methods. Sensitivity analysis demonstrated the strength of each predictor variable. Five optimal models showed good generalization capability in estimating the n, k, PV, AV, and YP with RMSE of 0.022, 0.270, 7.890, 8.870, and 10.149, and R2 of 0.995, 0.956, 0.756, 0.724, and 0.701 in a similar order, respectively. High sensitivity was observed in PV, AV, YP, and k models, and n-model, to changes in MFV (0.615) and % S (0.067), respectively. Results show the potential use of ANN with TensorFlow to support decision-making in geothermal drilling engineering on pipe sticking problems using easy-to-determine physical properties as predictor variables.

Suggested Citation

  • Hillary Mengich & Michael Kabugu & Mary Nelima Ondiaka, 2022. "Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow," European Journal of Energy Research, European Open Science, vol. 2(4), pages 49-56, July.
    • Handle: RePEc:epw:energy:v:2:y:2022:i:4:id:7077
    DOI: 10.24018/ejenergy.2022.2.4.77
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejenergy/article/view/7077
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejenergy/article/download/7077/1030
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejenergy.2022.2.4.77?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kristjan Korjus & Martin N Hebart & Raul Vicente, 2016. "An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    2. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    3. Musaab I. Magzoub & Raj Kiran & Saeed Salehi & Ibnelwaleed A. Hussein & Mustafa S. Nasser, 2021. "Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach," Energies, MDPI, vol. 14(5), pages 1-19, March.
    4. Salaheldin Elkatatny, 2019. "Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    5. Ahmed Gowida & Salaheldin Elkatatny & Emad Ramadan & Abdulazeez Abdulraheem, 2019. "Data-Driven Framework to Predict the Rheological Properties of CaCl 2 Brine-Based Drill-in Fluid Using Artificial Neural Network," Energies, MDPI, vol. 12(10), pages 1-17, May.
    6. Emad A. Al-Khdheeawi & Doaa Saleh Mahdi, 2019. "Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network," Energies, MDPI, vol. 12(16), pages 1-10, August.
    7. Andreas Nascimento & David Tamas Kutas & Asad Elmgerbi & Gerhard Thonhauser & Mauro Hugo Mathias, 2015. "Mathematical Modeling Applied to Drilling Engineering: An Application of Bourgoyne and Young ROP Model to a Presalt Case Study," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salaheldin Elkatatny, 2019. "Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    2. Francisca Barceló & Rosa Gomila & Ivan de Paul & Xavier Gili & Jaume Segura & Albert Pérez-Montaña & Teresa Jimenez-Marco & Antonia Sampol & José Portugal, 2018. "MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    3. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.
    4. Vitale, Morena & Shi, Huxiao & Castro Rodriguez, David J. & Barresi, Antonello A. & Demichela, Micaela, 2026. "Material degradation: Findings from historical accident analysis in process industries," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    5. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    6. Salma Albreiki & Mecit Can Emre Simsekler & Abroon Qazi & Ali Bouabid, 2024. "Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-20, March.
    7. Abhinash Jenasamanta & Subrajeet Mohapatra, 2022. "An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    8. Rahmat Nurcahyo & Anton Satria Prabuwono & Intan Novita Dewi & Farizal & Nurhadi Wibowo & Muhammad Habiburrahman & Khairi Hindriyandhito & Yeong Min Jang, 2025. "Enhancing Digital Wallet Services Based on Electronic Word of Mouth Using Social Media Reviews," SAGE Open, , vol. 15(3), pages 21582440251, July.
    9. Abroon Qazi, 2025. "Innovation forecasting: mapping pathways with global indicators," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-16, December.
    10. Kalahasthi, Lokesh Kumar & Sánchez-Díaz, Iván & Pablo Castrellon, Juan & Gil, Jorge & Browne, Michael & Hayes, Simon & Sentís Ros, Carles, 2022. "Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 307-329.
    11. Qianying Jin & Kristiaan Kerstens & Ignace Van de Woestyne, 2024. "Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1213-1239, December.
    12. Arwa Al Nuairi & Mecit Can Emre Simsekler & Abroon Qazi & Andrei Sleptchenko, 2024. "A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector," Annals of Operations Research, Springer, vol. 342(3), pages 1797-1817, November.
    13. Qazi, Abroon & Simsekler, Mecit Can Emre, 2023. "Nexus between drivers of COVID-19 and country risks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    14. Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Fadwa Alrowais & Abdelhameed Ibrahim & Nima Khodadadi & Wei Hong Lim & Nuha Alruwais & Doaa Sami Khafaga, 2022. "Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households," Energies, MDPI, vol. 15(23), pages 1-25, December.
    15. Liu, Yupeng & Yang, Lijun & Liao, Ruijin & Hu, Chengyu & Xiao, Yanlin & He, Chunwang & Wu, Xu & Zhang, Yuan & Li, Siquan, 2025. "Degradation mechanism of sodium-ion batteries and state of health estimation via electrochemical impedance spectroscopy under temperature disturbances," Energy, Elsevier, vol. 332(C).
    16. Georgios Friligkos, 2023. "A framework for applying the Logistic Regression model to obtain predictive analytics for tennis matches," Technium, Technium Science, vol. 15(1), pages 60-74.
    17. Lamia Lamrani & Christian Bongiorno & Marc Potters, 2025. "Optimal Data Splitting for Holdout Cross-Validation in Large Covariance Matrix Estimation," Papers 2503.15186, arXiv.org, revised Sep 2025.
    18. Lu Jiang & Xinyu Kang & Shan Huang & Bo Yang, 2022. "A refinement strategy for identification of scientific software from bioinformatics publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3293-3316, June.
    19. Mochamad Denny Surindra & Gusti Ahmad Fanshuri Alfarisy & Wahyu Caesarendra & Mohamad Iskandar Petra & Totok Prasetyo & Tegoeh Tjahjowidodo & Grzegorz M. Królczyk & Adam Glowacz & Munish Kumar Gupta, 2025. "Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3345-3358, June.
    20. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:energy:v:2:y:2022:i:4:id:7077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support Team (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejenergy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.