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Machine learning based modeling of the dynamic Poisson’s ratio for petroleum oil field

  • Rohit ORCID logo , Tummuri Naga Venkata Pavan ORCID logo , Shaista Zarrin Khanam , Vamsi Krishna Kudapa , Suresh Kumar Govindarajan ORCID logo and Srinivasa Reddy Devarapu ORCID logo EMAIL logo
Published/Copyright: April 28, 2025
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International Journal of Chemical Reactor Engineering
From the journal International Journal of Chemical Reactor Engineering Volume 23 Issue 8

Abstract

Poisson’s ratio is the fundamental geomechanical property that governs the stress and strain profiles of the geological formations. It is the prime characteristic in geomechanical modelling of the rock deformation and failure criteria. However, the accurate prediction of the Poisson’s ratio from geophysical log data using conventional techniques is a challenging task, owing to their time consuming and characteristic reliability issues. In this context, the robust machine learning (ML) based decision tree (DT) and linear regression (LR) models have been developed in the present work to evaluate the dynamic Poisson’s ratio of Volve field formation. The modelling is carried on geophysical log data gathered from 15/9-F-1B well of the Volve field. 1,500 data points captured in the depth range of 3,250–3,400 m of the 15/9-F-1B well are trained, tested, and validated to establish the reliability of the developed model. The analysis projected a test score of 0.99 for LR based model training and testing. Further, the score metrics projected the lowest maximum error for LR among the various machine learning (ML) methods employed, with a magnitude of 0.0173. The validation analysis projected a good history match of the modeled Poisson’s ratio values with a validation score of 0.982. While LR demonstrated a high test of R 2 0.994, residual analysis revealed systematic errors, indicating potential nonlinearity in the data. Conversely, DT, with a lower test R 2 of 0.904, exhibited well-distributed residuals, effectively capturing nonlinear relationships. However, DT’s tendency to overfit (R 2 = 1.000 on training data) limits its generalization. The findings suggest that while LR provides superior generalization, the presence of nonlinearity necessitates exploring polynomial regression or ensemble tree-based models to enhance predictive accuracy. The present work demonstrates that the use of machine learning to estimate Poisson’s ratio from geophysical logs is a promising strategy and hence, is useful in capturing insightful information from geo-mechanical engineering applications.

Keywords: well logs; geo-mechanics; Poisson ratio; machine learning; regression
Corresponding author: Srinivasa Reddy Devarapu, Department of Petroleum Engineering and Earth Sciences, School of Advanced Engineering, University of Petroleum and Energy Studies, Dehradun, India, E-mail:

Acknowledgments

The authors are grateful to Equinor and the Volve license partners for their permission to use the Volve field data in developing the ML models for this study. https://www.equinor.com/content/dam/statoil/documents/what-we-do/Equinor-HRS-Terms-and-conditions-for-licence-to-data-Volve.pdf. The Volve field dataset is publicly available through Equinor’s open-source initiative. Researchers can access the dataset via Equinor’s official website or directly through the link Volve Field. https://www.equinor.com/energy/volve-data-sharing.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-12-18
Accepted: 2025-04-09
Published Online: 2025-04-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijcre-2024-0243
Keywords for this article
well logs; geo-mechanics; Poisson ratio; machine learning; regression

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Preface: Special Issue dedicated to “The International Conference on Navigating Global Energy Transition for a Sustainable Future (ICNETS 2024)” at the Energy Summit-2024 UPES, Dehradun, India
  4. Reviews
  5. A review of conventional and non-conventional disinfection methods for treating potable water and wastewater
  6. Comprehensive assessment of carbon market potential in India: a case study of prominent energy and power majors
  7. R & D needs in the field of clay modified polymeric coatings
  8. Articles
  9. Drying and thermal studies of antimicrobial – poly(styrene)-poly(methyl methacrylate)-toluene coatings doped with CuO nanoparticles
  10. Critical study of reservoir and hydraulic fracture parameters on cumulative shale gas production – a sensitivity analysis
  11. Machine learning based modeling of the dynamic Poisson’s ratio for petroleum oil field
  12. Copper oxide-based nanofluids for the shell and tube heat exchanger: performance analysis
  13. Analysis of breakthrough curve and application of response surface methodology for the optimization of VOC adsorption
  14. A comprehensive analysis of household air pollution due to traditional cooking in the himalayan belt
  15. Aloe vera oil biodiesel with silicon dioxide additive for diesel engine: performance and emission study
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