Arabian Journal of Geosciences

, 12:580 | Cite as

Lithological identification with probabilistic distribution by the modified compositional Kriging

  • Feilong Han
  • Hongbing Zhang
  • Qiang Guo
  • Jianwen Rui
  • Qiuyan Ji
Original Paper


Lithological distribution, as a direct geological reflection, has been widely used in petroleum and geological projects. It is a challenging task to obtain a probabilistic map of lithology by compositional Kriging (CK) with limited logging core data. The predictions are usually affected by local high or low values, resulting in discontinuous boundaries and abnormal data fluctuation. To address the problem of local instability, we proposed the modified compositional Kriging (MCK) with a regulating factor, for adjusting the influence of core data to increase the accuracy and continuity of lithological probabilistic map. To obtain a reasonable result, we determined the hyper-parameter in the regulating factor’s function and a kind of seismic attribute highly correlated with logging data. The examples and the cross validation showed the effectiveness of Kriging method, and then proved the outstanding achievement of MCK through the comparison of field data’s predictions. After direct demonstration, we extracted the correlation coefficients and the regulating factor in our method to reveal more details inside the calculation. The correlation coefficients showed the variation of logging’s influence and illustrated the phenomenon that lacking of loggings can lead to deviations in the probability map. The regulating factor’s distribution showed the extra effect of MCK to increase the stability by its controlling effect. Hence, the proposed MCK method can provide a stable distribution of lithology when a suitable regulating factor has been chosen. This method is an effective tool for estimating lithological probabilistic map with limited wells.


Lithology Identification Compositional Kriging Geostatistical modelling Classification 



The authors gratefully appreciate the two anonymous revivers for offering valuable comments that led to great improvements in this paper

Funding information

The authors would like to acknowledge the considerable support by the Fundamental Research Funds for the Central Universities (2018B696X14), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0623), and the general projects of the National Natural Science Foundation of China (Nos. 41374116 and 41674113).


  1. Abedini A, Torabi F, Tontiwachwuthikul P (2012) Reservoir rock type analysis using statistical pore size distribution. Special Topics & Reviews in Porous Media 3:97–103CrossRefGoogle Scholar
  2. Adeoti L, Adesanya OY, Oyedele KF, Afinotan IP, Adekanle A (2018) Lithology and fluid prediction from simultaneous seismic inversion over Sandfish field, Niger Delta, Nigeria. Geosci J 22:155–169CrossRefGoogle Scholar
  3. Aitchison J, Barceló-Vidal C, Martin-Fernández JA, Pawlowsky-Glahn V (2000) Log-ratio analysis and compositional distance. Math Geosci 32:271–275Google Scholar
  4. Asfahani J, Abdul GB, Ahmad Z (2015) Basalt identification by interpreting nuclear and electrical well logging measurements using fuzzy technique (case study from southern syria). Appl Radiat Isot 105:92–97CrossRefGoogle Scholar
  5. Borkowski AS, Kwiatkowska-Malina J (2017) Geostatistical modelling as an assessment tool of soil pollution based on deposition from atmospheric air. Geosci J 21:645–653CrossRefGoogle Scholar
  6. Buland A, Kolbjørnsen O, Hauge R, Skjæveland Ø, Duffault K (2008) Bayesian lithology and fluid prediction from seismic prestack data. Geophysics 73:C13–C20CrossRefGoogle Scholar
  7. Chang H, Chen H, Fang J (1997) Lithology determination from well logs with fuzzy associative memory neural network. IEEE Trans Geosci Remote 35:773–780CrossRefGoogle Scholar
  8. Chang HC, Kopaska-Merkel DC, Chen HC, Durrans SR (2000) Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system. Comput Geosci 26:591–601CrossRefGoogle Scholar
  9. de Gruijter JJ, Walvoort DJJ, van Gaans PFM (1997) Continuous soil maps – a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 77:169–195CrossRefGoogle Scholar
  10. Deng CX, Pan HP, Fang SN, Konaté AA, Qin RD (2017) Support vector machine as an alternative method for lithology classification of crystalline rocks. J Geophys Eng 14:341–349CrossRefGoogle Scholar
  11. Farrell ME (2004) Estimating lithology and fluid parameters from seismic data. J Acoust Soc Am 115:2471–2471CrossRefGoogle Scholar
  12. Grana D, Paparozzi E, Mancini S, Tarchiani C (2013) Seismic driven probabilistic classification of reservoir facies for static reservoir modeling: a case history in the Barents Sea. Geophys Prospect 61:613–629CrossRefGoogle Scholar
  13. Luo Y, Zhao YC, Lu XH (2013) Characteristics and evaluation of the shale oil reservoir in upper es inter salt in Liutun Sag. J Earth Sci 24:962–975CrossRefGoogle Scholar
  14. Maiti S, Tiwari RK (2010) Neural network modeling and an uncertainty analysis in Bayesian framework: a case study from the KTB borehole site. J Geophys Res Solid Earth 115:1–28CrossRefGoogle Scholar
  15. Mueller TG, Pusuluri NB, Mathias KK, Cornelius PL, Barnhisel RI, Shearer SA (2004) Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Sci Soc Am J 68:2042–2047CrossRefGoogle Scholar
  16. Odeh IOA, Todd AJ, Triantafilis J (2003) Spatial prediction of soil particle-size fractions as compositional data. Soil Sci 168:501–515Google Scholar
  17. Oonk S, Slomp CP, Huisman DJ, Vriend SP (2009) Effects of site lithology on geochemical signatures of human occupation in archaeological house plans in the Netherlands. J Archaeol Sci 36:1215–1228CrossRefGoogle Scholar
  18. Rolon L, Mohaghegh SD, Ameri S, Gaskari R, McDaniel B (2009) Using artificial neural networks to generate synthetic well logs. J Nat Gas Sci Eng 1:118–133CrossRefGoogle Scholar
  19. Rosenbaum MS, Rosén L, Gustafson G (1997) Probabilistic models for estimating lithology. Eng Geol 47:43–55CrossRefGoogle Scholar
  20. Shi WJ, Liu JY, Du ZP, Song YJ, Chen CY, Yue TX (2009) Surface modelling of soil pH. Geoderma 150:113–119CrossRefGoogle Scholar
  21. Singh S, Kanli AI (2016) Estimating shear wave velocities in oil fields: a neural network approach. Geosci J 20:221–228CrossRefGoogle Scholar
  22. Sun XL, Wu YJ, Wang HL, Zhao YG, Zhang GL (2014) Mapping soil particle size fractions using compositional kriging, cokriging and additive log-ratio cokriging in two case studies. Math Geosci 46:429–443CrossRefGoogle Scholar
  23. Taboada J, Saavedra Á, Iglesias C, Giráldez E (2013) Estimating quartz reserves using compositional kriging. Abstr Appl Anal 2013:1–16CrossRefGoogle Scholar
  24. Tolosana-Delgado R, van den Boogaart KG (2013) Joint consistent mapping of high-dimensional geochemical surveys. Math Geosci 45:983–1004CrossRefGoogle Scholar
  25. Walvoort DJJ, de Gruijter JJ (2001) Compositional Kriging: a spatial interpolation method for compositional data. Math Geol 33:951–966CrossRefGoogle Scholar
  26. Wang Z, Shi WJ (2017) Mapping soil particle-size fractions: a comparison of compositional kriging and log-ratio kriging. J Hydrol 546:526–541CrossRefGoogle Scholar
  27. Wang PY, Li ZQ, Wang WB, Li HL, Wang FT (2014) Glacier volume calculation from ice-thickness data for mountain glaciers——a case study of glacier No.4 of Sigong River over Mt. Bogda, Eastern Tianshan, Central Asia. J Earth Sci 25:371–378CrossRefGoogle Scholar
  28. Watanabe H, Matsuo K (2003) Rock type classification by multi-band TIR of ASTER. Geosci J 7:347–358CrossRefGoogle Scholar
  29. Zhang SW, Shen CY, Chen XY, Ye HC, Huang YF, Lai S (2013) Spatial interpolation of soil texture using compositional kriging and regression kriging with consideration of the characteristics of compositional data and environment variables. J Integr Agric 12:1673–1683CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.College of Earth Science and EngineeringHohai UniversityNanjingPeople’s Republic of China
  2. 2.NanjingPeople’s Republic of China

Personalised recommendations