Environmental Earth Sciences

, Volume 74, Issue 7, pp 5565–5579 | Cite as

The multiplicative inverse misfit correlation approach for depth correlation of porosity in reservoir modeling

  • Katrina BurchEmail author
  • Jejung Lee
  • Jae Hwa Jin
Original Article


Evaluating well-log and core-plug data to understand the heterogeneity of porosity in geologic formations is of utmost importance in reservoir studies. The well-log data and core-plug data are integrated in order to generate an accurate model describing the porosity distribution; however, these data exist at different scales and resolution, which necessitates scaling of one or both sets of the data. This study looked at the efficacy of using geostatistical techniques, in particular the likelihood method, to correlate data at different scales. The result was the development of a geostatistical scaling method combining variance, skewness, kurtosis and standard deviation by means of a misfit algorithm in conjunction with correlating the depth of the core-plug data within the well-log data through a scaling process in order to integrate porosity data. The geostatistical scaling method involves basic variogram models for scaling the computerized tomography (CT) plug data to well-log scale. Variance-based statistics were calculated within CT plug-size intervals, then a best fit for depth correlation determined. A new correlation algorithm, named the multiplicative inverse misfit correlation (MIMC) method, was formulated for accurate depth correlation. The application of the MIMC method identified the sampled depth enabling higher accuracy for correlations of core plugs or CT scans to the well-log depth and porosity. The MIMC method proved it has the capacity to correlate the depths of the CT data for each well, including depths within the determined uncertainty.


Geostatistics Porosity Data integration Reservoir Petroleum Hydrology 


  1. Akin S, Ross CM, Kovscek AR (2008) Combination of well log and pore-scale data to predict petrophysical properties of diatomite. J Pet Sci Eng 60(3):133–149CrossRefGoogle Scholar
  2. Alfaaouri S, Riahi MA, Alizadeh N, Resaei M (2009) Permeability prediction in an oil reservoir and construction of 3D geological model by stochastic approaches. J Appl Sci 9(11):2016–2030CrossRefGoogle Scholar
  3. Al-Khalifah M, Makkawi M (2002) The impact of data integration on geostatistical porosity modeling: a case study from the Berri Field, Saudi Arabia. J Pet Geol 25(4):485–498CrossRefGoogle Scholar
  4. Al-Qassab HM, Fitzmaurice J, Al-Ali ZA, Al-Khalifa MA, Aktas GA, Glover PW (2000) Cross-discipline integration in reservoir modeling: the impact on fluid flow simulation and reservoir management. Presented at Society of Petroleum Engineers Technical Conference. Dallas, Texas. SPE62902Google Scholar
  5. Bigelow E (1992) Introduction to wireline log analysis. Western Atlas, HoustonGoogle Scholar
  6. Burch K (2012) Geostatistical integration of core and well log data for high-resolution reservoir modeling. Master’s thesis, University of Missouri, Kansas CityGoogle Scholar
  7. Carr T, Sawin RS (1996) Hugoton natural gas area of Kansas. Kansas Geological Survey, Public Information Circular (PIC) 5. Accessed 29 Mar 2012
  8. Choquette PW, Pray LC (1970) Geological nomenclature and classification of porosity in sedimentary carbonates. AAPG Bull 54:207–250Google Scholar
  9. Chough SK, Sohn YK (2010) Tectonic and sedimentary evolution of a Cretaceous continental arc-barkarc system in the Korean peninsula: new view. Earth-Sci Rev 101:225–249CrossRefGoogle Scholar
  10. Deutsch CV (2002) Geostatistical reservoir modeling. Oxford University Press, New YorkGoogle Scholar
  11. Fournier F (1995) Integration of 3D seismic data in reservoir stochastic simulation: a case study. Presented at Society of Petroleum Engineers Technical Conference. Dallas, Texas. SPE30564Google Scholar
  12. Frykman P, Deutsch CV (1999) Geostatistical scaling laws applied to core and log data. Presented at Society of Petroleum Engineers Technical Conference. Houston, Texas. SPE56822Google Scholar
  13. Hirsche K, Boerner S, Kalkomey C, Gastaldi C (1998) Avoiding pitfalls in geostatistical reservoir characterization: a survival guide. The Leading Edge. 493–504Google Scholar
  14. Journel AG, Huijbregts CHJ (1978) Mining geostatistics. Academic Press Inc., New YorkGoogle Scholar
  15. Kansas Geological Survey. Oil and gas production data: LAS files.
  16. Kim Y, Suh BS, Kim K (2001) Natural gamma effect in density determination in the gamma-gamma log and its correction. Accessed 22 Sep 2011
  17. Leary PC, Al-Kindy F (2002) Power-law scaling of spatially correlated porosity and log (permeability) sequences from north-central North Sea Brae oilfield well core. Geophys J Int 148:426–442CrossRefGoogle Scholar
  18. Liu Y, Harding A, Abriel W, Strebelle S (2004) Multiple-point simulation integrating wells, three-dimensional seismic data and geology. AAPG Bull 88(7):905–921CrossRefGoogle Scholar
  19. Minasny B, Vrugt JA, McBratney AB (2011) Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation. Geoderma 163:150–162CrossRefGoogle Scholar
  20. Newell KD, Watney WL, Cheng SWL, Brownrigg R (1987) Stratigraphic and spatial distribution of oil and gas production in Kansas. Kansas Geological Society: Subsurface Geology. Series Number 9Google Scholar
  21. Oz B, Deutsch CV, Frykman P (2002) A visualbasic program for histogram and variogram scaling. Comput Geosci 28:21–31CrossRefGoogle Scholar
  22. Paik IS, Lee YI (1995) Short term climatic changes recorded in early Cretaceous floodplain deposits, Korea. Proceedings of 15th international symposium of Kyaungpook National University, Korea. pp 395–417Google Scholar
  23. Pawitan Y (2001) In all likelihood: statistical modelling and inference using likelihood. Oxford University Press Inc., New YorkGoogle Scholar
  24. Prasad M (2003) Velocity-permeability relations within hydraulic units. Geophysics 68(1):108–117CrossRefGoogle Scholar
  25. Price D, Curtis A, Wood R (2008) Statistical correlation between geophysical logs and extracted core. Geophysics 73(3):E97–E106CrossRefGoogle Scholar
  26. Remy N, Boucher A, Wu J (2009a) Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, New YorkCrossRefGoogle Scholar
  27. Remy N, Boucher A, Wu J, Li T (2009b) Applied geostatistics with SGeMS [Computer software]. Cambridge University Press, New YorkCrossRefGoogle Scholar
  28. Rider M (1986) The geological interpretation of well logs. Blackie Halsted Press, New YorkGoogle Scholar
  29. Tilke PG, Allen D, Gyllensten A (2006) Quantitative analysis of porosity heterogeneity: application of geostatistics to borehole images. Math Geol 38(2):155–174CrossRefGoogle Scholar
  30. Tran TT, Wen X, Behrens RA (1999) Efficient conditioning of 3d fine-scale reservoir model to multiphase production data using streamline-based coarse-scale inversion and geostatistical downscaling. Presented at Society of Petroleum Engineers Technical Conference. Houston, Texas. SPE56518Google Scholar
  31. Xu W, Tarn TT, Srivastava RM, Journel AG (1992) Integrating seismic data in reservoir modeling: the collocated cokriging alternative. Presented at Society of Petroleum Engineers Technical Conference. Washington, D.C. SPE24742Google Scholar
  32. Yao T, Mukerji T, Journel A, Mavko G (1999) Scale matching with factorial kriging for improved porosity estimation from seismic data. Math Geol 31(1):23–46CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of GeologyUniversity of MissouriColumbiaUSA
  2. 2.Department of GeosciencesUniversity of MissouriKansas CityUSA
  3. 3.Korea Institute of Geoscience and Mineral ResourcesDaejeonKorea

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