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Abstract

Summary

Geostatistical classification and prediction of lithology is an important step in reservoir characterization. By predicting rock facies with geostatistical analysis of well logs, especially in intervals that have not been cored, overall data acquisition costs, project cycle times, project costs, and exploration costs could be reduced. Additionally, lithology identification adds a control factor for the relationship between permeability and porosity. In this case study, partitioning algorithms Partitioning Around Mediods (PAM) and K-means Cluster Analysis were utilized to analyze well log data in R-statistical computing language, for one well in Forest Hill oil field in the East Texas Basin. The output from K-means Cluster Analysis indicated that three clusters were the ideal groupings of the input data, which aligned with the known stratigraphy of the area of interest for this case study. Each cluster was then assigned a lithological classification based on geologic literature of the East Texas Basin. These classifications were compared to the original well log data and well log interpretations from Interactive Petrophysics software to verify the accuracy of the clustering analysis and the vertical sequence of lithology in the well.

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/content/papers/10.3997/2214-4609.201700905
2017-06-12
2024-04-25

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References

  1. Foote, R. Q., Massingill, L. M., and Wells, R. H.
    (1988). Petroleum geology and the distribution of conventional crude oil, natural gas, and natural gas liquids, East Texas Basin (No. 88-450-K). US Geological Survey.
     [Google Scholar]
  2. Hothorn, T. and Everitt, B.S.
    (2009). A handbook of statistical analyses using R. CRC Press.
     [Google Scholar]
  3. Hovorka, S. D. and Nance, H. S.
    (1994). Dynamic depositional and early diagenetic processes in a deep-water shelf setting, Upper Cretaceous Austin Chalk, North Texas. Gulf Coast Association of Geological Societies Transactions. 44(269–276).
     [Google Scholar]
  4. Jain, A.K., Murty, M.N. and Flynn, P.J.
    (1999). Data clustering: a review. ACM computing surveys(CSUR), 31(3), 264–323.
     [Google Scholar]
  5. Jain, A.K.
    (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
     [Google Scholar]
  6. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R. and Wu, A.Y.
    (2002). An efficient k-means clustering algorithm: Analysis and implementation, Pattern Analysis and Machine Intelligence. IEEE Transactions, 24(7), 881–892.
     [Google Scholar]
  7. Nichols, P. H.
    (1964). Gulf Coast Association of Geological Societies Transactions. 14.
     [Google Scholar]
  8. Oliver, W. B.
    (1970). Depositional Systems in Woodbine Formation (Upper Cretaceous), Northeast Texas: ABSTRACT. AAPG Bulletin, 54(9), 1788–1788.
     [Google Scholar]
  9. Siemers, C. T., Tillman, R. W., and Williamson, C. R.
    (Eds.). (1981). Deep-water Clastic Sediments: A Core Workshop. Society of Economic Paleontologists and Mineralogists.
     [Google Scholar]
  10. Turner, J. R. and Conger, S. J.
    (1981). Environment of deposition and reservoir properties of the Woodbine sandstone at Kurten field, Brazos County, Texas. The Society of Economic Paleontologists and Mineralogists (SEPM).
     [Google Scholar]
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Geostatistical Classification of Lithology Using Partitioning Algorithms on Well Log Data - A Case Study in Forest Hill Oil Field, East Texas Basin
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201700905
/content/papers/10.3997/2214-4609.201700905
/content/papers/10.3997/2214-4609.201700905
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