Wellbore Instability Prediction by Geomechanical Behavioral Modeling in Zilaie Oil Field

Document Type : Research Paper

Authors

1 Department of Geotechnics, Islamic Azad University, Central Tehran branch, Tehran, Iran

2 Department of Petroleum, Mining and Material Engineering, Islamic Azad University, Central Tehran branch, Tehran, Iran

3 Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran

Abstract

Wellbore instability is a critical problem during oil and gas reservoirs’ drilling and production phase, for which analytical, numerical, experimental, and field methods have been widely discussed. Because of the limitations of the mentioned techniques for predicting the different types of wellbore failures, the problem is still open. Although well logs provide a great source of big data for instability prediction, data-mining techniques have not matured in this domain. This paper explains how an AI-based method can be applied to instability detection/prediction. Unlike other data mining studies in this field, we proposed a systematic approach that can be traceable by the readers. We used several classification algorithms (e.g., Bayesian network, SVM) and found that the C5 decision tree algorithm has the best precision. We show the effectiveness of the method by applying the method to a dataset with about 30,000 records of wellbore logs, getting an accuracy of 91.5%.

Keywords

References

  1. Roshan, H., & Rahman, S. S. (2011). Analysis of pore pressure and stress distribution around a wellbore drilled in chemically active elastoplastic formations. Rock mechanics and rock engineering, 44, 541-552., doi: 10.1007/s00603-011-0141-x.##
  2. Zhang J. & Chenevert M. E. (2005). The impact of shale properties on wellbore stability, Faculty Graduate School, hdl.handle.net/2152/2204. ##
  3. Zeynali, M. E. (2012). Mechanical and physico-chemical aspects of wellbore stability during drilling operations. Journal of Petroleum Science and Engineering, 82, 120-124, doi: 10.1016/j.petrol.2012.01.006. ##
  4. Alkinani, H. H., Al-Hameedi, A. T. T., Dunn-Norman, S., Flori, R. E., Alsaba, M. T., Amer, A. S., & Hilgedick, S. A. (2019). Using data mining to stop or mitigate lost circulation. Journal of Petroleum Science and Engineering, 173, 1097-1108., 2019, doi: 10.1016/J.PETROL.2018.10.078. ##
  5. Lin, A., Alali, M., Almasmoom, S., & Samuel, R. (2018). Wellbore instability prediction using adaptive analytics and empirical mode decomposition, In IADC/SPE Drilling Conference and Exhibition, OnePetro, doi.org/10.2118/189598-MS. ##
  6. Okpo, E. E., Dosunmu, A., & Odagme, B. S. (2016, August). Artificial neural network model for predicting wellbore instability, In SPE Nigeria Annual International Conference and Exhibition, OnePetro, doi: 10.2118/184371-MS. ##
  7. Abbas, A. K., Al-Asadi, Y. M., Alsaba, M., Flori, R. E., & Alhussainy, S. (2018, January). Development of a geomechanical model for drilling deviated wells through the Zubair formation in Southern Iraq, In SPE/IADC Middle east Drilling Technology Conference and Exhibition, OnePetro, doi.org/10.2118/189306-MS. ##
  8. Zhou, B., Guo, Y., Liao, Y., Hao, Z., Wang, T., Zhao, W., & Deng, J. (2021). Research on numerical simulation of wellbore stability of natural gas hydrate reservoir considering dynamic drilling process, In International Technical Symposium on Deepwater Oil and Gas Engineering, 47-59, Singapore: Springer Singapore. ##
  9. Tohidi, A., Fahimifar, A., & Rasouli, V. (2018). Analytical solution to study depletion/injection rate on induced wellbore stresses in an anisotropic stress field, Geotechnical and Geological Engineering, 36, 1735-1744, doi: 10.1007/s10706-017-0429-z. ##
  10. Tohidi, A., Fahimifar, A., & Rasouli, V. (2019). Effect of non-Darcy flow on induced stresses around a wellbore in an anisotropic in-situ stress field, Scientia Iranica, 26(3), 1182-1193, doi: 10.24200/SCI.2017.4603. ##
  11. Yousefian, H., Soltanian, H., Marji, M. F., Abdollahipour, A., & Pourmazaheri, Y. (2018). Numerical simulation of a wellbore stability in an Iranian oilfield utilizing core data, Journal of Petroleum Science and Engineering, 168, 577-592, doi: 10.1016/j.petrol.2018.04.051. ##
  12. Ibrahim, A. (2021). A review of mathematical modelling approaches to tackling wellbore instability in shale formations, Journal of Natural Gas Science and Engineering, 89, 103870., doi: 10.1016/j.jngse.2021.103870. ##
  13. Cui, L., Cheng, A. H. D., Kaliakin, V. N., Abousleiman, Y., & Roegiers, J. C. (1996). Finite element analyses of anisotropic poroelasticity: A generalized Mandel's problem and an inclined borehole problem, International Journal for Numerical and Analytical Methods in Geomechanics, 20(6), 381-401, doi: 10.1002/(SICI)1096-9853(199606)20:6<381::AID-NAG826>3.0.CO;2-Y. ##
  14. Lenwoue, A. K., Deng, J., Feng, Y., Li, Z., Oloruntoba, A., Li, H., & Marembo, M. (2022). 3D numerical modeling of the effect of the drill string vibration cyclic loads on the wellbore natural fracture growth, Journal of Petroleum Science and Engineering, 208, 109481, doi: 10.1016/J.PETROL.2021.109481. ##
  15. Salehi, S., & Kiran, R. (2016). Integrated experimental and analytical wellbore strengthening solutions by mud plastering effects, Journal of Energy Resources Technology, 138(3), 032904, doi.org/10.1115/1.4032236. ##
  16. Mortazavi, A., & Atapour, H. (2018). An experimental study of stress changes induced by reservoir depletion under true triaxial stress loading conditions, Journal of Petroleum Science and Engineering, 171, 1366-1377, doi.org/10.1016/j.petrol.2018.08.047. ##
  17. Narayanasamy, R., Barr, D., & Milne, A. (2010). Wellbore-instability predictions within the Cretaceous mudstones, Clair Field, West of Shetlands, SPE Drilling & Completion, 25(04), 518-529, doi.org/10.2118/124464-PA. ##
  18. Hajiabadi, M. R., Afrough, A., & Nick, H. M. (2022). An evaluation of viscous deformation of chalk on wellbore stability, Journal of Natural Gas Science and Engineering, 105, 104694, doi: doi.org/10.1016/j.jngse.2022.104694. ##
  19. Skea, C., Rezagholilou, A., Far, P. B., Gholami, R., & Sarmadivleh, M. (2018). An approach for wellbore failure analysis using rock cavings and image processing, Journal of Rock Mechanics and Geotechnical Engineering, 10(5), 865-878., doi: 10.1016/j.jrmge.2018.04.011. ##
  20. Alkamil, E. H., Abbood, H. R., Flori, R. E., & Eckert, A. (2017, March). Wellbore stability evaluation for Mishrif formation, In SPE Middle East Oil and Gas Show and Conference, D031S031R004, doi: 10.2118/183668-MS. ##
  21. Allawi, R. H., & Al-Jawad, M. S. (2021). Wellbore instability management using geomechanical modeling and wellbore stability analysis for Zubair shale formation in Southern Iraq, Journal of Petroleum Exploration and Production Technology, 11, 4047-4062, doi: 10.1007/s13202-021-01279-y. ##
  22. Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer Science & Business Media. ##
  23. Rocha Vargas, L. A., & Izurieta, C. A. (2021, December). Integration of Neural Networks and Wellbore Stability, a Modern Approach to Recognize Drilling Problems Through Computer Vision, In SPE Middle East Oil and Gas Show and Conference, D041S048R005, doi: 10.2118/204760-MS. ##
  24. Noshi, C. I., Assem, A. I., & Schubert, J. J. (2018) The role of big data analytics in exploration and production: A review of benefits and applications, In SPE International Heavy Oil Conference and Exhibition, D012S021R001, doi.org/10.2118/193776-MS. ##
  25. Jahanbakhshi, R., Keshavarzi, R., & Jahanbakhshi, R. (2012). Intelligent prediction of wellbore stability in oil and gas wells: An artificial neural network approach, In ARMA US Rock Mechanics/Geomechanics Symposium, ARMA. ##
  26. Jahanbakhshi, R., Keshavarzi, R., & Jalili, S. (2014). Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs: a case study, European Journal of Environmental and Civil Engineering, 18(3), 320-335, doi: 10.1080/19648189.2013.860924. ##
  27. Okpo, E. E., Dosunmu, A., & Odagme, B. S. (2016). Artificial neural network model for predicting wellbore instability, In SPE Nigeria Annual International Conference and Exhibition, OnePetro, doi: 10.2118/184371-MS. ##
  28. Mohamadian, N., Ghorbani, H., Wood, D. A., Mehrad, M., Davoodi, S., Rashidi, S., & Shahvand, A. K. (2021). A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning, Journal of Petroleum Science and Engineering, 196, 107811, doi: 10.1016/j.petrol.2020.107811. ##
  29. Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.
  30. Jolliffe, I. T. (2002). Principal component analysis for special types of data, 338-372, Springer New York.
  31. Chawla N. V. (2009) Data Mining for Imbalanced Datasets: An Overview, Data Mining and Knowledge Discovery Handbook, 875–886, doi: 10.2493/jjspe.54.195. ##
  32. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321-357., doi: 10.1613/jair.953. ##
  33. Hssina, B., Merbouha, A., Ezzikouri, H., & Erritali, M. (2014). A comparative study of decision tree ID3 and C4. 5. International Journal of Advanced Computer Science and Applications, 4(2), 13-19. ##
  34. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874, doi: 10.1016/j.patrec.2005.10.010. ##

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