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Integration of Cluster Analysis and Rock Physics for the Identification of Potential Hydrocarbon Reservoir

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Abstract

Rock physics has proven its credibility for the quantitative seismic interpretation of reservoir and reservoir characterization. In this study, we implemented K-means clustering to group data points more objectively and without prior misconceptions into clusters. Furthermore, we used the grouping suggested by the algorithm for our study. We integrated rock physics and K-means cluster analysis to determine a possible hydrocarbon reservoir using three well logs data. Initially, K-means clustering was implemented on density logs and based on the arithmetic mean of each density log cluster, sandstone and shale-dominant parts were identified and separated. Then, rock physics parameters were computed for the sandstone-dominant part of the well logs. Based on the cross-plots of Lame’s constants with density product, and Lame’s constants ratio, additional lithology discrimination was done for the identification of a clean gas-sand zone. In the clean gas-sand zone, two different pore fluids zones were identified based on Vp/Vs ratio, Poisson ratio, P- and S-wave impedances. Finally, statistical analysis was carried out to observe the underlying frequency distribution of rock physics parameters. The integration of cluster analysis and rock physics gave us significant information about the presence of different fluids and the existence of a potential hydrocarbon reservoir.

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References

  • Al-Mudhafar, W. J., Al Lawe, E. M., & Noshi, C. I. (2019). Clustering analysis for improved characterization of carbonate reservoirs in a Southern Iraqi Oil Field. Paper presented at the offshore technology conference.

  • Al-Mudhafar, W., & Bondarenko, M. (2015). Integrating K-means clustering analysis and generalized additive model for efficient reservoir characterization. Paper presented at the 77th EAGE conference and exhibition 2015.

  • Bação, F., Lobo, V., & Painho, M. (2005). The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Computers & Geosciences, 31(2), 155–163.

    Article  Google Scholar 

  • Bateman, R. M. (1977). The fluid identification plot. Paper presented at the SPWLA 18th annual logging symposium, Houston, Texas.

  • Brown, G., Rees, H., Toro, M., & Arceneaux, C. L., Jr. (1995). Using production-log data from horizontal wells. Paper presented at the SPWLA 36th annual logging symposium, Paris, France.

  • Castagna, J. P., Batzle, M. L., & Eastwood, R. L. (1985). Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks. Geophysics, 50(4), 571–581.

    Article  Google Scholar 

  • Chen, S., Di Rosa, D. E., Gyllensten, A., Georgi, D., & Tauk, R. S. (2006). Use of the NMR diffusivity log to identify and quantify oil and water in carbonate formations. Paper presented at the Abu Dhabi international petroleum exhibition and conference, Abu Dhabi, UAE. https://doi.org/10.2118/101396-MS.

  • Chombart, L. G. (1960). Well logs in carbonate reservoirs. Geophysics, 25(4), 779–853.

    Article  Google Scholar 

  • Dco, P. P., Rao, R. V., & Singh, B. (1998). Identification of gas-oil contacts by overlay techniques using wireline log data. The Log Analyst, 39(03), 25.

    Google Scholar 

  • Deng, M., Liu, Q., Cheng, T., & Shi, Y. (2011). An adaptive spatial clustering algorithm based on Delaunay triangulation. Computers, Environment and Urban Systems, 35(4), 320–332.

    Article  Google Scholar 

  • Duda, R. O., Hart, P. E., & Stork, D. G. (1973). Pattern classification and scene analysis (Vol. 3). New York: Wiley.

    Google Scholar 

  • Euzen, T., Delamaide, E., Feuchtwanger, T., & Kingsmith, K. D. (2010). Well log cluster analysis: An innovative tool for unconventional exploration. Paper presented at the Canadian unconventional resources and international petroleum conference.

  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining (Vol. 21). Menlo Park: AAAI Press.

    Google Scholar 

  • Fukunaga, K. (2013). Introduction to statistical pattern recognition. Amsterdam: Elsevier.

    Google Scholar 

  • Gersho, A., & Gray, R. M. (2012). Vector quantization and signal compression (Vol. 159). New York: Springer.

    Google Scholar 

  • Goodway, B., Chen, T., & Downton, J. (1997). Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters;“λρ”, “μρ”, & “λ/μ fluid stack”, from P and S inversions. SEG technical program expanded abstracts 1997 (pp. 183–186). Society of Exploration Geophysicists.

  • Guha, S., Rastogi, R., & Shim, K. (1998). CURE: An efficient clustering algorithm for large databases. ACM Sigmoid Record, 27(2), 73–84.

    Article  Google Scholar 

  • Hartigan, J. A. (1975). Clustering algorithms. New York, NY: Wiley.

    Google Scholar 

  • Heslop, A., & Pickel, J. S. (1978). Fluid contact monitoring in some western Canadian Reefs. The Log Analyst, 19(02), 8.

    Google Scholar 

  • Karypis, G., Han, E.-H., & Kumar, V. (1999). Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8), 68–75.

    Article  Google Scholar 

  • McCreery, E., & Al-Mudhafar, W. (2017). Geostatistical classification of lithology using partitioning algorithms on well log data—A case study in forest hill oil field, East Texas Basin. Paper presented at the 79th EAGE conference and exhibition 2017.

  • McCreery, E. B., & Al-Mudhafar, W. J. (2017). Geostatistical classification of lithology using partitioning algorithms on well log data—A case study in forest hill oil field, East Texas Basin. 2017(1), 1–5.

  • Miller, H. J., & Han, J. (2009). Geographic data mining and knowledge discovery. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Nazeer, K. A., & Sebastian, M. (2009). Improving the accuracy and efficiency of the k-means clustering algorithm. Paper presented at the proceedings of the world congress on engineering.

  • Omudu, L. M., Ebeniro, J. O., Xynogalas, M., Olotu, S., & Osayande, N. (2008). Fluid discrimination and reservoir characterization from onshore Niger Delta. Paper presented at the 2008 SEG annual meeting, Las Vegas, Nevada.

  • Pickett, G. R. (1963). Acoustic character logs and their applications in formation evaluation. Journal of Petroleum Technology, 15(06), 659–667.

    Article  Google Scholar 

  • Puskarczyk, E. (2020). Application of multivariate statistical methods and artificial neural network for Facies analysis from well logs data: An example of Miocene deposits. Energies, 13(7), 1548.

    Article  Google Scholar 

  • Queen, M. (1966). Some methods for classification and analysis of multivariate observations (Methods for classification and analysis of multivariate observations).

  • Tzanis, G., Katakis, I., Partalas, I., & Vlahavas, I. (2006). Modern applications of machine learning. Paper presented at the proceedings of the 1st annual SEERC doctoral student conference–DSC.

  • Veeken, P., & Van Moerkerken, B. (2007). Seismic stratigraphic techniques. Handbook of geophysical exploration: Seismic exploration (pp. 111–234).

  • Wagstaff, K. (2012). Machine learning that matters. arXiv preprint arXiv:1206.4656.

  • Wang, Q., Wang, C., Feng, Z.-Y., & Ye, J.-F. (2012). Review of K-means clustering algorithm. Electronic Design Engineering, 20(7), 21–24.

    Google Scholar 

  • Wu, X., & Nyland, E. (1986). Well log data interpretation using artificial intelligence technique. Paper presented at the SPWLA 27th Annual Logging Symposium, Houston, Texas.

  • Xu, R., & Wunsch, D. (2008). Clustering (Vol. 10). New York: Wiley.

    Book  Google Scholar 

  • Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. ACM Sigmoid Record, 25(2), 103–114.

    Article  Google Scholar 

Download references

Acknowledgment

The authors of this study are grateful to the School of earth sciences, Zhejiang University, Hangzhou, China, for providing an excellent working environment and research facilities.

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Authors and Affiliations

  1. School of Earth Sciences, Zhejiang University, Hangzhou City, Zhejiang Province, China

    Amjad Ali

  2. School of Earth Sciences, Zhejiang University, Hangzhou City, China

    Chen Sheng-Chang

  3. Institute of Space Technology, Islamabad, 44000, Pakistan

    Munawar Shah

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  1. Amjad Ali
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  2. Chen Sheng-Chang
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  3. Munawar Shah
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Correspondence to Amjad Ali.

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Ali, A., Sheng-Chang, C. & Shah, M. Integration of Cluster Analysis and Rock Physics for the Identification of Potential Hydrocarbon Reservoir. Nat Resour Res 30, 1395–1409 (2021). https://doi.org/10.1007/s11053-020-09800-6

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  • DOI: https://doi.org/10.1007/s11053-020-09800-6

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