Integrated Drilling Geomechanics Analysis Using Poro-Elasto-Plastic Finite Elements and Machine Learning

This study will deal with the essential weaknesses of traditional elastic models to predict wellbore instability that in most cases results in expensive drilling procedures. The aim of this study is to develop an integrated drilling geomechanics analysis using Poro-Elasto-Plastic finite elements and machine learning. The objectives are to, integrate drilling operations using a poro-elasto-plastic Finite Element Model; integrate drilling operations using machine learning algorithms. The analysis evolves a synthesized framework that combines a poro-elastoplastic Finite Element Model (FEM) and a Machine Learning (ML) to allow a dynamic and precise geomechanical analysis. A Drucker-Prager yield criterion has been used in the FEM to model the effects of plastic deformation and time-dependent effects of pore pressure around the wellbore in a realistic manner. Afterward, the ML surrogate models are trained using the outputs of the FEM in order to provide fast real-time predictions. Findings indicate that the hybrid model is able to properly measure the plastic yield zone and can give a dynamically updated safe mud weight window, which is much better than the traditional methods of doing so. The conclusion confirms that such integration forms a powerful digital twin, which allows making decisions in advance to increase the safety of drilling, streamline operations, and minimize non-productive time, thereby creating a new paradigm of wellbore stability management.

Integrated Drilling Geomechanics, Pror-Elasto-Plastic

1. AlBahrani, H., Papamichos, E., & Morita, N. (2021). Building an integrated drilling geomechanics model using a machine-learning-assisted poro-elasto-plastic finite element method. SPE Journal, 26(04), 18931913. https://doi.org/10.2118/205497-PA [Google Scholar] [Crossref]

2. Al-Haddad, S. A., Abbas, H. A., Al-Haddad, L. A., & Al-Karkhi, M. I. (2025). Application of artificial neural networks for predicting soil settlement in geotechnical applications with plastic waste reinforcement above buried pipes. Discover Geoscience, 3(1), 1-17. [Google Scholar] [Crossref]

3. Amadike, M. P., Nwanesi, F. O., Ogbonna, C. G., Ohale, U. U., Okoli, A. E., Jacob, O. J., & Okeke, O. C. (2024). Principles and Applications of Seismic Reflection Geophysical Technique In Petroleum Exploration And Production: A Review. DOI: 10.5281/zenodo.14265732 [Google Scholar] [Crossref]

4. Beckman J. (2024). Agbami field offshore Nigeria set for renewed drilling. https://www.offshoremag.com/regional-reports/africa/news/55243211/agbami-field-offshore-nigeria-set-for-renewed-drilling [Google Scholar] [Crossref]

5. Chamanzad, M., Nikkhah, M., Ramezanzadeh, A., Shi, X., Pezeshki, M., & Mostafavi, I. (2025). Proposing an approach for geomechanical model construction based on laboratory and wellbore test results and wellbore instability assessment in the Kangan and Dalan formations. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 11(1), 1-23. https://doi.org/10.1007/s40948-025-01006-5 [Google Scholar] [Crossref]

6. Emudianughe, J. E., Eze, P. M., & Utah, S. (2021). Porosity and Permeability Trend In Agbami-Field Using Well Log, Offshore, Niger Delta. Communication In Physical Sciences, 7(4), 531-541. https://orcid.org/0000-0002-1217-4599 [Google Scholar] [Crossref]

7. Gladious, J., Paul, P. S., & Mukhopadhyay, M. (2025). Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone. Scientific Reports, 15(1), 21868. https://doi.org/10.1038/s41598-025-99026-4 [Google Scholar] [Crossref]

8. Harle, S. M., & Wankhade, R. L. (2025). Machine learning techniques for predictive modelling in geotechnical engineering: a succinct review. Discover Civil Engineering, 2(1), 1-21. [Google Scholar] [Crossref]

9. Jamshidi, E., Kianoush, P., Hosseini, N., & Adib, A. (2024). Scaling-up dynamic elastic logs to pseudostatic elastic moduli of rocks using a wellbore stability analysis approach in the Marun oilfield, SW Iran. Scientific Reports, 14(1), 19094. https://doi.org/10.1038/s41598-024-69758-w\ [Google Scholar] [Crossref]

10. Khan, A., Li, Y., Shoaib, M., Sajjad, U., & Rui, F. (2025). Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data. Journal of Intelligent Construction, 3(2), 123. [Google Scholar] [Crossref]

11. Komijani, M. (2024). A mixed nonlocal finite element model for thermo‐poro‐elasto‐plastic simulation of porous media with multiphase fluid flow. International Journal for Numerical Methods in Engineering, 125(17), e7466. https://doi.org/10.1002/nme.7466 [Google Scholar] [Crossref]

12. Li, X., El Mohtar, C. S., & Gray, K. E. (2019). 3D poro-elasto-plastic modeling of breakouts in deviated wells. Journal of Petroleum Science and Engineering, 174, 913-920. https://doi.org/10.1016/j.petrol.2018.11.086 [Google Scholar] [Crossref]

13. Lindqwister, W., Peloquin, J., Dalton, L. E., Gall, K., & Veveakis, M. (2025). Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks. Communications Engineering, 4(1), 73. https://doi.org/10.1038/s44172-025-00410-9 [Google Scholar] [Crossref]

14. Liu, H., Su, H., Sun, L., & Dias-da-Costa, D. (2024). State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering. Artificial Intelligence Review, 57(8), 196. [Google Scholar] [Crossref]

15. Liu, M., & Huang, H. (2021). Finite element modeling of spherical indentation in a poro‐elasto‐plastic medium via step displacement loading. International Journal for Numerical and Analytical Methods in Geomechanics, 45(10), 1347-1380. [Google Scholar] [Crossref]

16. Nautiyal, A., & Mishra, A. K. (2023). Machine learning application in enhancing drilling performance. Procedia Computer Science, 218, 877-886. [Google Scholar] [Crossref]

17. Nguyen, H. C., He, X., Nazem, M., Chen, X., Xu, H., Sousa, R., & Kowalski, J. (2025). Gaussian process emulators for the undrained bearing capacity of spatially random soils using cell-based smoothed finite element method. Journal of Rock MechanicsandGeotechnicalEngineering. https://doi.org/10.1016/j.jrmge.2025.04.042 [Google Scholar] [Crossref]

18. Osaki, L. J. & Oghonyon, R. (2025a) Synthesizing Geophysical Well Logging and Spatial Modelling for [Google Scholar] [Crossref]

19. Reservoir Analysis Optimization: A Dynamic Approach. African Journal of Engineering and Environment Research Vol.7(2) 2025 pp 22- 44 ©2025 CEFPACS Consulting Limited. www.cefpacsconsultingltd.org.ng ISSN: 2714-2264. [Google Scholar] [Crossref]

20. Osaki, L. J. & Oghonyon, R. (2025b), An Integrated Deep Learning Solution for Petrophysics, Pore Pressure, And Geomechanics Property Prediction. The International Journal of Engineering and Science (IJES) Volume 14 Issue 8 August Pages PP 20-29 2025 ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805. [Google Scholar] [Crossref]

21. Osaki, L.J..Onwubuariri, C.N., Agoha, C.C. (2025). Optimizing Unconventional Reservoir Performance in Niger Delta: A 3D Geostatistical driven Geomechanics Evaluation-Integrated Approach to Characterization. Journal of scientific and engineering Research, 12(2):138-154 [Google Scholar] [Crossref]

22. Pirhadi, A., Kianoush, P., Varkouhi, S., Shirinabadi, R., Shirazy, A., Shirazi, A., & Ebrahimabadi, A. (2025). Thermo-poroelastic analysis of drilling fluid pressure and temperature on wellbore stresses in the [Google Scholar] [Crossref]

23. Mansouri oilfield, SW Iran. Results in Earth Sciences, 3, 100061. https://doi.org/10.1016/j.rines.2025.100061 [Google Scholar] [Crossref]

24. Sanei, M., Ramezanzadeh, A., & Delavar, M. R. (2023). Applied machine learning-based models for predicting the geomechanical parameters using logging data. Journal of Petroleum Exploration and Production Technology, 13(12), 2363-2385. [Google Scholar] [Crossref]

25. Yan, B., Zhang, X., Tang, C., Wang, X., Yang, Y., & Xu, W. (2023). A random forest-based method for predicting borehole trajectories. Mathematics, 11(6), 1297. [Google Scholar] [Crossref]

26. Yoon, H., Kucala, A., Chang, K. W., Martinez, M. J., Bean, J. E., Kadeethum, T., & Fuhg, J. N. (2022). Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media (No. SAND2022-12824). Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). https://doi.org/10.2172/1890064 [Google Scholar] [Crossref]

27. Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469-477. [Google Scholar] [Crossref]

28. Zhang, L., Zhang, H., Hu, K., Chen, Z., & Yin, S. (2022). Thermoporoelastoplastic wellbore breakout modeling by finite element method. Mining, 2(1), 52-64. https://doi.org/10.3390/mining2010004 [Google Scholar] [Crossref]

• A Comparative Study on the Thermal and Electrical Conductivity of Common Materials
• Thickness Dependent Thermoelectric Properties of Pb0.4In0.6Se Thin Films Deposited by Physical Evaporation Technique
• Optimization of a Patch Antenna Using Genetic Algorithm
• Kinematic Constraints On Brown Dwarf Atmospheric Variability And Evidence For Bimodal Formation From Multi-Survey Analysis
• Reservoir Characterization through the Application of Petrophysical Evaluation of Well Logs of Animaux Field, Niger Delta Basin, Nigeria