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Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models

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Abstract

Rock properties in geomechanical models depend on electrofacies. Electrofacies classification is a crucial task for generating accurate rock property models. Insufficient information about core samples and image logs at all locations is the major drawback in electrofacies classification. This study classified electrofacies by the multi-resolution graph-based clustering (MRGC) approach using the well-log data from the Scotian shelf, Offshore Canada. The unsupervised method such as MRGC, ascendant hierarchical clustering, and self-organizing map uses the K-nearest neighbors and kernel index for defining the cluster dots which characterize the electrofacies. These classified facies are incorporated with core information to establish a relationship. This relation can develop electrofacies in the non-core zone/wells. The electrofacies were predicted using the MRGC approach to generate rock mechanical properties such as Young's modulus, Poisson’s ratio, unconfirmed compressive strength, and internal friction coefficient.

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References

  1. Ambati, V.; Nagendra Babu, M.; Nair, R.R.: Reservoir wellbore stability analysis and weak zones identification using the 1d MEM, swelling tests and UCS: a case study from Mumbai offshore. India. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-05530-w

    Article  Google Scholar 

  2. Chang, C.; Zoback, M.D.; Khaksar, A.: Empirical relations between rock strength and physical properties in sedimentary rocks. J. Pet. Sci. Eng. 51, 223–237 (2006). https://doi.org/10.1016/j.petrol.2006.01.003

    Article  Google Scholar 

  3. Inichinbia, S.; Sule, P.O.; Ahmed, A.L.; Hamaza, H.; Lawal, K.M.: Petro physical analysis of among hydrocarbon field fluid and lithofaciesusing well log data. IOSR J. Appl. Geol. Geophys. 2, 86–96 (2014). https://doi.org/10.9790/0990-02218696

    Article  Google Scholar 

  4. Primmer TJ, Cade CA, Evans J, Gluyas JG, Hopkins MS, Oxtoby NH, Smalley PC, Warren EA, Worden RH.: Global patterns in sandstone diagenesis: their application to reservoir quality prediction for petroleum exploration, pp. 61–77 (1997)

  5. Nagendra Babu, M.; Ambati, V.; Nair, R.R.: Characterization of complex fluvial-deltaic deposits in Northeast India using Poisson impedance inversion and non-parametric statistical technique. Sci. Rep. 12, 16917 (2022). https://doi.org/10.1038/s41598-022-21444-5

    Article  Google Scholar 

  6. Riazi, Z.: Application of integrated rock typing and flow units identification methods for an Iranian carbonate reservoir. J. Pet. Sci. Eng. 160, 483–497 (2018). https://doi.org/10.1016/j.petrol.2017.10.025

    Article  Google Scholar 

  7. Ma, Y.Z.: Lithofacies clustering using principal component analysis and neural network: applications to wireline logs. Math. Geosci. 43, 401–419 (2011). https://doi.org/10.1007/s11004-011-9335-8

    Article  Google Scholar 

  8. Doveton, J.H.: Principles of Mathematical Petrophysics. Oxford University Press (2014)

    Book  MATH  Google Scholar 

  9. Belozerov, B., Bukhanov, N., Egorov, D., Zakirov, A., Osmonalieva, O., Golitsyna, M., Reshytko, A., Semenikhin, A., Shindin, E., Lipets, V: Automatic well log analysis across priobskoe field using machine learning methods (Russian). In: SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers (2018)

  10. Puskarczyk, E.: Application of multivariate statistical methods and artificial neural network for facies analysis from well logs data: an example of miocene deposits. Energies 13, 1548 (2020). https://doi.org/10.3390/en13071548

    Article  Google Scholar 

  11. Nazeer, A.; Abbasi, S.A.; Solangi, S.H.: Sedimentary facies interpretation of Gamma Ray (GR) log as basic well logs in Central and Lower Indus Basin of Pakistan. Geod. Geodyn. 7, 432–443 (2016). https://doi.org/10.1016/j.geog.2016.06.006

    Article  Google Scholar 

  12. Moradi, M.; Tokhmechi, B.; Kordi, M.; Masoudi, P.: Gamma-clustering sequence stratigraphy, case study of the carbonate Sarvak Formation Southwest Iran. SN Appl. Sci. 1, 1369 (2019). https://doi.org/10.1007/s42452-019-1407-2

    Article  Google Scholar 

  13. Adams, A.; Diamond, L.W.: Facies and depositional environments of the upper Muschelkalk (Schinznach Formation, Middle Triassic) in northern Switzerland. Swiss J. Geosci. 112, 357–381 (2019). https://doi.org/10.1007/s00015-019-00340-7

    Article  Google Scholar 

  14. Inoue, K.; Fukushi, M.; Van Le, T.; Tsuruoka, H.; Kasahara, S.; Nimelan, V.: Distribution of gamma radiation dose rate related with natural radionuclides in all of Vietnam and radiological risk assessment of the built-up environment. Sci. Rep. 10, 12428 (2020). https://doi.org/10.1038/s41598-020-69003-0

    Article  Google Scholar 

  15. Nagendra Babu, M.; Ambati, V.; Nair, R.R.: Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: a case study. J. Earth Syst. Sci. 131, 55 (2022). https://doi.org/10.1007/s12040-021-01792-y

    Article  Google Scholar 

  16. Bawazer, W.; Lashin, A.; Kinawy, M.M.: Characterization of a fractured basement reservoir using high-resolution 3D seismic and logging datasets: a case study of the Sab’atayn Basin, Yemen. PLoS ONE 13, e0206079 (2018). https://doi.org/10.1371/journal.pone.0206079

    Article  Google Scholar 

  17. Torres, K.M., Al Hashmi, N.F., Al Hosani, I.A., Al Rawahi, A.S., Parra, H.: Reducing the uncertainty of static reservoir model in a carbonate platform, through the implementation of an integrated workflow: case a-field, Abu Dhabi, UAE. In: Day 1 Mon, November 07, 2016. SPE (2016)

  18. Hossain, T.M.; Watada, J.; Aziz, I.A.; Hermana, M.: Machine learning in electrofacies classification and subsurface lithology interpretation: a rough set theory approach. Appl. Sci. 10, 5940 (2020). https://doi.org/10.3390/app10175940

    Article  Google Scholar 

  19. Wang, G.; Carr, T.R.; Ju, Y.; Li, C.: Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin. Comput. Geosci. 64, 52–60 (2014). https://doi.org/10.1016/j.cageo.2013.12.002

    Article  Google Scholar 

  20. Qi, L.; Carr, T.R.: Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields. Southwest Kansas. Comput. Geosci. 32, 947–964 (2006). https://doi.org/10.1016/j.cageo.2005.10.020

    Article  Google Scholar 

  21. Bhattacharya, S.; Carr, T.R.: Integrated petrofacies characterization and interpretation of depositional environment of the bakken shale in the Williston Basin North America. Petrophysics SPWLA J. Form. Eval. Reserv. Descr. 57, 95–110 (2016)

    Google Scholar 

  22. Maurya, S.P.: Estimating elastic impedance from seismic inversion method: a study from nova scotia field Canada. Curr Sci 116, 628 (2019). https://doi.org/10.18520/cs/v116/i4/628-635

    Article  Google Scholar 

  23. Qayyum, F.; Catuneanu, O.; Bouanga, C.E.: Sequence stratigraphy of a mixed siliciclastic carbonate setting Scotian Shelf Canada. Interpretation 3(2), SN21–SN37 (2015). https://doi.org/10.1190/INT-2014-0129.1

    Article  Google Scholar 

  24. Xiao, M.: Reservoir estimation in the Penobscot 3D seismic volume using Constrained Sparse Spike Inversion, Offshore Nova Scotia, Canada (2016)

  25. Salvaris, M., Kaznady, M., Paunic, V., Karmanov, I., Bhatia, A., Tok, W.H., Chikkerur, S.: DeepSeismic: a deep learning library for seismic interpretation. In: First EAGE Digitalization Conference and Exhibition, pp. 1–5. European Association of Geoscientists & Engineers (2020)

  26. Campbell, T.J.; Richards, F.W.B.; Silva, R.L.; Wach, G.; Eliuk, L.: Interpretation of the Penobscot 3D seismic volume using constrained sparse spike inversion, Sable sub-Basin, offshore Nova Scotia. Mar. Pet. Geol. 68, 73–93 (2015). https://doi.org/10.1016/j.marpetgeo.2015.08.009

    Article  Google Scholar 

  27. Kiaei, H.; Sharghi, Y.; Ilkhchi, A.K.; Naderi, M.: 3D modeling of reservoir electrofacies using integration clustering and geostatistic method in central field of Persian Gulf. J. Pet. Sci. Eng. 135, 152–160 (2015). https://doi.org/10.1016/j.petrol.2015.08.019

    Article  Google Scholar 

  28. Cover, T.; Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory. 13, 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964

    Article  MATH  Google Scholar 

  29. Rahimpour-Bonab, H.; Aliakbardoust, E.: Pore facies analysis: incorporation of rock properties into pore geometry based classes in a Permo-Triassic carbonate reservoir in the Persian Gulf. J. Geophys. Eng. 11, 035008 (2014). https://doi.org/10.1088/1742-2132/11/3/035008

    Article  Google Scholar 

  30. Ye, S.-J., Rabiller, P.: A new tool for electro-facies analysis: multi-resolution graph-based clustering (2000)

  31. Zhang, J.: Kohonen self-organizing map–an artificial neural network. In: Zhang, J. (eds.) Visualization for Information Retrieval. The Information Retrieval Series, vol. 23. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-75148-9_5

  32. Kohonen, T.: The self-organizing map. Neurocomputing 21, 1–6 (1998). https://doi.org/10.1016/S0925-2312(98)00030-7

    Article  MATH  Google Scholar 

  33. Lotfi Shahreza, M.; Moazzami, D.; Moshiri, B.; Delavar, M.R.: Anomaly detection using a self-organizing map and particle swarm optimization. Sci. Iran. 18, 1460–1468 (2011). https://doi.org/10.1016/j.scient.2011.08.025

    Article  Google Scholar 

  34. Yorek, N.; Ugulu, I.; Aydin, H.: Using self-organizing neural network map combined with ward’s clustering algorithm for visualization of students’ cognitive structural models about aliveness concept. Comput. Intell. Neurosci. 2016, 1–14 (2016). https://doi.org/10.1155/2016/2476256

    Article  Google Scholar 

  35. Szymański, J.: Self–organizing map representation for clustering wikipedia search results. In: Intelligent Information and Database Systems—Third International Conference, pp. 140–149 (2011)

  36. Campoy, P.: Dimensionality reduction by self organizing maps that preserve distances in output space. In: 2009 International Joint Conference on Neural Networks, pp. 432–438. IEEE (2009)

  37. Natita, W.; Wiboonsak, W.; Dusadee, S.: Appropriate learning rate and neighborhood function of self-organizing map (SOM) for specific humidity pattern classification over Southern Thailand. Int. J. Model. Optim. 6, 61–65 (2016). https://doi.org/10.7763/ijmo.2016.v6.504

    Article  Google Scholar 

  38. Arnault, S.; Thiria, S.; Crépon, M.; Kaly, F.: A tropical Atlantic dynamics analysis by combining machine learning and satellite data. Adv. Sp. Res. 68, 467–486 (2021). https://doi.org/10.1016/j.asr.2020.09.044

    Article  Google Scholar 

  39. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963). https://doi.org/10.1080/01621459.1963.10500845

    Article  MathSciNet  Google Scholar 

  40. Lukasová, A.: Hierarchical agglomerative clustering procedure. Pattern Recognit. 11, 365–381 (1979). https://doi.org/10.1016/0031-3203(79)90049-9

    Article  MathSciNet  MATH  Google Scholar 

  41. On the geology of Nova Scotia. Nature 3, 214–215 (1871). https://doi.org/10.1038/003214a0

  42. Gould, K.M.; Piper, D.J.; Pe-Piper, G.: Lateral variation in sandstone lithofacies from conventional core Scotian Basin: implications for reservoir quality and connectivity. Can. J. Earth Sci. 49(12), 1478–1503 (2012). https://doi.org/10.1139/e2012-064

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank OpendTect for providing the data in the Penobscot 3D field. We would also like to thank Emerson-Paradigm for providing an academic license to carry out research work.

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  1. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, India

    Pradeep Mahadasu & Kumar Hemant Singh

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  1. Pradeep Mahadasu
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PM: Conceptualization, methodology, investigation, interpretation of results, writing and editing manuscript. KHS: Supervision and Validation of results. Both authors comprehended and designed the workflows and wrote the paper.

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Correspondence to Pradeep Mahadasu.

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Mahadasu, P., Singh, K.H. Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models. Arab J Sci Eng 48, 7855–7866 (2023). https://doi.org/10.1007/s13369-022-07521-x

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