IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p930-d497223.html
   My bibliography  Save this article

Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Author

Listed:
  • Fahimeh Hadavimoghaddam

    (Department of Oil Field Development and Operation, Faculty of Oil and Gas Field Development, 119991 Moscow, Russia)

  • Mehdi Ostadhassan

    (Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China)

  • Ehsan Heidaryan

    (Department of Chemical Engineering, Engineering School, University of São Paulo (USP), Caixa Postal 61548, São Paulo 05424-970, Brazil)

  • Mohammad Ali Sadri

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Inna Chapanova

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Evgeny Popov

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Alexey Cheremisin

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Saeed Rafieepour

    (McDougall School of Petroleum Engineering, University of Tulsa, Tulsa, OK 74110, USA)

Abstract

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f ( γ A P I , T ) , has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Suggested Citation

  • Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:930-:d:497223
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/930/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/4/930/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Hui & Rose, Sherri & van der Laan, Mark J., 2011. "Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 792-796, July.
    2. Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 351-371, December.
    3. Sinisi Sandra E. & Polley Eric C & Petersen Maya L & Rhee Soo-Yon & van der Laan Mark J., 2007. "Super Learning: An Application to the Prediction of HIV-1 Drug Resistance," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-26, February.
    4. Elizabeth M Sweeney & Joshua T Vogelstein & Jennifer L Cuzzocreo & Peter A Calabresi & Daniel S Reich & Ciprian M Crainiceanu & Russell T Shinohara, 2014. "A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    5. Cheng Ju & Mary Combs & Samuel D. Lendle & Jessica M. Franklin & Richard Wyss & Sebastian Schneeweiss & Mark J. van der Laan, 2019. "Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(12), pages 2216-2236, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    2. Dicho S. Stratiev & Svetoslav Nenov & Ivelina K. Shishkova & Rosen K. Dinkov & Kamen Zlatanov & Dobromir Yordanov & Sotir Sotirov & Evdokia Sotirova & Vassia Atanassova & Krassimir Atanassov & Danail , 2021. "Comparison of Empirical Models to Predict Viscosity of Secondary Vacuum Gas Oils," Resources, MDPI, vol. 10(8), pages 1-17, August.

    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. André Altmann & Michal Rosen-Zvi & Mattia Prosperi & Ehud Aharoni & Hani Neuvirth & Eugen Schülter & Joachim Büch & Daniel Struck & Yardena Peres & Francesca Incardona & Anders Sönnerborg & Rolf Kaise, 2008. "Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-9, October.
    2. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    3. Laan Mark J. van der & Dudoit Sandrine & Vaart Aad W. van der, 2006. "The cross-validated adaptive epsilon-net estimator," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 373-395, December.
    4. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    5. Mahmood Zafar & Khan Salahuddin, 2009. "On the Use of K-Fold Cross-Validation to Choose Cutoff Values and Assess the Performance of Predictive Models in Stepwise Regression," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-21, July.
    6. Ertefaie Ashkan & Asgharian Masoud & Stephens David A., 2018. "Variable Selection in Causal Inference using a Simultaneous Penalization Method," Journal of Causal Inference, De Gruyter, vol. 6(1), pages 1-16, March.
    7. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
    8. Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
    9. Zulj, Valentin & Jin, Shaobo, 2024. "Can model averaging improve propensity score based estimation of average treatment effects?," Working Paper Series 2024:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    10. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
    11. Díaz Muñoz Iván & van der Laan Mark J., 2011. "Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-20, October.
    12. Raymond Salvador & Joaquim Radua & Erick J Canales-Rodríguez & Aleix Solanes & Salvador Sarró & José M Goikolea & Alicia Valiente & Gemma C Monté & María del Carmen Natividad & Amalia Guerrero-Pedraza, 2017. "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
    13. Neugebauer Romain & Chandra Malini & Paredes Antonio & J. Graham David & McCloskey Carolyn & S. Go Alan, 2013. "A Marginal Structural Modeling Approach with Super Learning for a Study on Oral Bisphosphonate Therapy and Atrial Fibrillation," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 21-50, June.
    14. I Díaz & O Savenkov & K Ballman, 2018. "Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes," Biometrika, Biometrika Trust, vol. 105(3), pages 723-738.
    15. Zihao Li & Hui Lan & Vasilis Syrgkanis & Mengdi Wang & Masatoshi Uehara, 2024. "Regularized DeepIV with Model Selection," Papers 2403.04236, arXiv.org.
    16. van der Laan Mark J. & Gruber Susan, 2012. "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-41, May.
    17. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    18. Hind A. Beydoun & May A. Beydoun & Brook T. Alemu & Jordan Weiss & Sharmin Hossain & Rana S. Gautam & Alan B. Zonderman, 2022. "Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study," IJERPH, MDPI, vol. 19(19), pages 1-24, September.
    19. Tasquia Mizan & Sharareh Taghipour, 2021. "A causal model for short‐term time series analysis to predict incoming Medicare workload," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 228-242, March.

    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:gam:jeners:v:14:y:2021:i:4:p:930-:d:497223. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.