Skip to main content
SpringerLink
Log in

Lithological identification with probabilistic distribution by the modified compositional Kriging

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abedini A, Torabi F, Tontiwachwuthikul P (2012) Reservoir rock type analysis using statistical pore size distribution. Special Topics & Reviews in Porous Media 3:97–103

    Article  Google Scholar 

  • 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 22:155–169

    Article  Google Scholar 

  • Aitchison J, Barceló-Vidal C, Martin-Fernández JA, Pawlowsky-Glahn V (2000) Log-ratio analysis and compositional distance. Math Geosci 32:271–275

    Google Scholar 

  • 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 105:92–97

    Article  Google Scholar 

  • Borkowski AS, Kwiatkowska-Malina J (2017) Geostatistical modelling as an assessment tool of soil pollution based on deposition from atmospheric air. Geosci J 21:645–653

    Article  Google Scholar 

  • Buland A, Kolbjørnsen O, Hauge R, Skjæveland Ø, Duffault K (2008) Bayesian lithology and fluid prediction from seismic prestack data. Geophysics 73:C13–C20

    Article  Google Scholar 

  • Chang H, Chen H, Fang J (1997) Lithology determination from well logs with fuzzy associative memory neural network. IEEE Trans Geosci Remote 35:773–780

    Article  Google Scholar 

  • 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 26:591–601

    Article  Google Scholar 

  • 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 77:169–195

    Article  Google Scholar 

  • 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 14:341–349

    Article  Google Scholar 

  • Farrell ME (2004) Estimating lithology and fluid parameters from seismic data. J Acoust Soc Am 115:2471–2471

    Article  Google Scholar 

  • 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 61:613–629

    Article  Google Scholar 

  • 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 24:962–975

    Article  Google Scholar 

  • 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 115:1–28

    Article  Google Scholar 

  • 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 68:2042–2047

    Article  Google Scholar 

  • Odeh IOA, Todd AJ, Triantafilis J (2003) Spatial prediction of soil particle-size fractions as compositional data. Soil Sci 168:501–515

    Google Scholar 

  • 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 36:1215–1228

    Article  Google Scholar 

  • 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 1:118–133

    Article  Google Scholar 

  • Rosenbaum MS, Rosén L, Gustafson G (1997) Probabilistic models for estimating lithology. Eng Geol 47:43–55

    Article  Google Scholar 

  • Shi WJ, Liu JY, Du ZP, Song YJ, Chen CY, Yue TX (2009) Surface modelling of soil pH. Geoderma 150:113–119

    Article  Google Scholar 

  • Singh S, Kanli AI (2016) Estimating shear wave velocities in oil fields: a neural network approach. Geosci J 20:221–228

    Article  Google Scholar 

  • 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 46:429–443

    Article  Google Scholar 

  • Taboada J, Saavedra Á, Iglesias C, Giráldez E (2013) Estimating quartz reserves using compositional kriging. Abstr Appl Anal 2013:1–16

    Article  Google Scholar 

  • Tolosana-Delgado R, van den Boogaart KG (2013) Joint consistent mapping of high-dimensional geochemical surveys. Math Geosci 45:983–1004

    Article  Google Scholar 

  • Walvoort DJJ, de Gruijter JJ (2001) Compositional Kriging: a spatial interpolation method for compositional data. Math Geol 33:951–966

    Article  Google Scholar 

  • Wang Z, Shi WJ (2017) Mapping soil particle-size fractions: a comparison of compositional kriging and log-ratio kriging. J Hydrol 546:526–541

    Article  Google Scholar 

  • 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 25:371–378

    Article  Google Scholar 

  • Watanabe H, Matsuo K (2003) Rock type classification by multi-band TIR of ASTER. Geosci J 7:347–358

    Article  Google Scholar 

  • 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 12:1673–1683

    Article  Google Scholar 

Download references

Acknowledgements

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

Funding

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).

Author information

Authors and Affiliations

  1. College of Earth Science and Engineering, Hohai University, Nanjing, 210098, People’s Republic of China

    Feilong Han, Hongbing Zhang, Qiang Guo, Jianwen Rui & Qiuyan Ji

  2. Nanjing, People’s Republic of China

    Hongbing Zhang

Authors
  1. Feilong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongbing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianwen Rui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiuyan Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Responsible Editor: Abdullah M. Al-Amri

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, F., Zhang, H., Guo, Q. et al. Lithological identification with probabilistic distribution by the modified compositional Kriging. Arab J Geosci 12, 580 (2019). https://doi.org/10.1007/s12517-019-4775-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-019-4775-4

Keywords

Search

Navigation