Skip to main content
SpringerLink
Log in

FMI image based rock structure classification using classifier combination

  • ICONIP2009
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Formation Micro Imager (FMI) can directly reflect changes of wall stratums and rock structures, and is an important factor to classify stratums and identify lithology for the oil and gas exploration. Conventionally, people analyze FMI images mainly with manual processing, which is, however, extremely inefficient and incurs a heavy workload for experts. In this paper, we propose an automatic rock structure classification system using image processing and pattern recognition technologies. We investigate the characteristics of rock structures in FMI images carefully. We also develop an effective classification framework with classifier combination that can integrate the domain knowledge from experienced geologists successfully. Our classification system includes three main steps. First, various effective features, specially designed for FMI images, are calculated and selected. Then, the corresponding single classifier associated with each feature is constructed. Finally, all these classifiers are combined as an effective cascade recognition system. We test our rock structure classification system with real FMI rock images. In experiments, with only one training sample per class, the average recognition accuracy of our proposed system is 81.11%. The accuracy is 15.55 percent higher than the traditional 1-nearest neighborhood method. Moreover, this automatic system can significantly reduce the complexity and difficulty in the rock structure analysis task for the oil and gas exploration.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Singh U, Baan DVD (1994) FMS/FMI borehole imaging of carbonate gas reservoirs, Central Luconia Province, offshore Sarawak, Malaysia. In: 1994 American association of petroleum geologists (AAPG) international conference and exhibition, Kuala Lumpur, pp 1162–1163

  2. Laubach SE, Gale JFW (2006) Obtaining fracture information for low-permeability (tight) gas sandstones from sidewall cores. Petroleum Geol 29(2):147–158

    Article  Google Scholar 

  3. Endres H, Lohr T, Trappe H, Samiee R, Thierer PO, Krawczyk CM, Tanner DC, Onchen O, Kukla PA (2008) Quantitative fracture prediction from seismic data. Petroleum Geosci 14(4):369–377

    Article  Google Scholar 

  4. Zhang Z, Tong HM, Bao ZD (2009) Development characteristics and quantitative prediction of reservoir fractures in the Chaoyanggou oil field. Min Sci Technol 19:373–379

    Google Scholar 

  5. Ferraretti D, Gamberoni G, Lamma E, Cuia RD, Turolla C (2009) An AI tool for the petroleum industry based on image analysis and hierarchical clustering. In: International conference on intelligent data engineering and automated learning, Burgos. pp 276–283

  6. Cuia RD, Ferraretti D, Gamberoni G, Pertier E, Escar L (2009) Validation of a semi-automatic interpretation of image logs using two wells from a north Africa sandstone reservoir. In: 6th North African/Mediterranean petroleum and geosciences conference and exhibition, Tunis

  7. Prensky SE (1999) Advances in borehole imaging technology and applications. Geol Soc 159:1–43

    Google Scholar 

  8. Ye S, Rabiller P (1998) Automated fracture detection on high resolution resistivity borehole imagery. In: SPE annual technical conference and exhibition, New Orleans. pp 777–784

  9. Ginkel MV, Vliet LV, Verbeek P, Kraaijveld M, Reding E, Lammers H (2003) Robust curve detection using a radon transform in orientation space. In: 13th Scandinavian Conference on Image Analysis. pp 125–132

  10. Yin X-C, Liu Q, Hao H-W, Wang Z-B, Huang KZ (2009) A rock structure recognition system using FMI images. In: International conference on neural information processing, Bangkok. pp 838–845

  11. Sonka M, Hlavac V, Boyle R (2007) Image processing: analysis and machine vision, 3rd edn. Cengage-Engineering, USA

  12. Tuceryan M, Jain AK (1998) Texture analysis. The handbook of pattern recognition and computer vision, 2nd edn. World Scientific Publishing Co, River Edge, pp 207–248

  13. Cheriet M, Kharma N, Liu CL, Suen C (2007) Character recognition systems: a guide for students and practitioners. Wiley, New York

    MATH  Google Scholar 

  14. Payenberg THD, Lang SC, Koch R (2000) A simple method for orienting conventional core using microresistivity (FMS) images and a mechanical goniometer to measure directional structures on cores. Sediment Res 70:419–422

    Article  Google Scholar 

  15. Russell SD, Akbar M, Vissapragada B, Walkden GM (2002) Rock types and permeability prediction from dipmeter and image logs: shuaiba reservoir (Aptian), Abu Dhabi. AAPG Bull 86(10):1709–1732

    Google Scholar 

  16. Loncaric S (1998) A survey of shape analysis techniques. Pattern Recogn 31(8):983–1001

    Article  Google Scholar 

  17. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  18. Chen L, Kamel MS (2009) A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems. Pattern Recogn 42(5):629–644

    Article  MATH  Google Scholar 

  19. Hao H-W, Liu C-L, Sako H (2003) Confidence evaluation for combining diverse classifier. In: 7th International conference on document analysis and recognition, Edinburgh. pp 760–764

  20. Ho TK, Hull J, Srihari SN (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66–75

    Article  Google Scholar 

  21. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239

    Article  Google Scholar 

  22. Yin X-C, Liu C-P, Han Z (2005) Feature combination using boosting. Pattern Recogn Lett 26(14):2195–2205

    Article  Google Scholar 

  23. Roli F, Giacinto G, Vernazza G (2001) Methods for designing multiple classifiers systems. Multiple Classifier Systems, Cambridge, pp 78–87

    Google Scholar 

  24. Xu L, Krzyzak A, Suen CY (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern 22(3):418–435

    Article  Google Scholar 

  25. Ruta D, Gabrys B (2005) Classifier selection for majority voting. Inf Fusion 6(1):63–81

    Article  Google Scholar 

  26. Huang YL, Wang KL, Chen DR (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2):164–169

    Article  Google Scholar 

  27. Zou W, Li Y, Tang A (2009) Effects of the number of hidden nodes used in a structure-based neural network on the reliability of image classification. Neural Comput Appl 18(3):249–260

    Google Scholar 

  28. Huang K, Yang H, King I, Lyu MR (2008) M4: learning large margin machines locally and globally. IEEE Trans Neural Netw 19(2):260–272

    Article  Google Scholar 

  29. Huang K, Yang H, King I, Lyu MR, Chan L (2004) The minimum error minimax probability machine. Mach Learn Res 5:1253–1286

    MathSciNet  Google Scholar 

  30. Mclennan JA, Allwardt PF, Hennings PH, Farrell HE (2009) Multivariate fracture intensity prediction: application to oil Mountain anticline, Wyoming. AAPG Bull 93(11):1585–1595

    Article  Google Scholar 

  31. Olson JE, Laubach SE, Lander RH (2009) Natural fracture characterization in tight gas sandstones: integrating mechanics and diagenesis. AAPG Bull 93(11):1535–1549

    Article  Google Scholar 

  32. Kang HJ, David D (2005) Selection of classifiers for the construction of multiple classifier systems. In: 8th International Conference on Document Analysis and Recognition, Seoul. pp 1194–1198

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 60675006 and thanks Fei Wu for his contributions to this paper. We also are thankful to T. Chow and anonymous reviewers for their valuable comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Computer Science, School of Information Engineering, University of Science and Technology Beijing, 100083, Beijing, China

    Xu-Cheng Yin, Qian Liu, Hong-Wei Hao & Zhi-Bin Wang

  2. Institute of Automation, Chinese Academy of Science, 100190, Beijing, China

    Kaizhu Huang

Authors
  1. Xu-Cheng Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong-Wei Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi-Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kaizhu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong-Wei Hao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yin, XC., Liu, Q., Hao, HW. et al. FMI image based rock structure classification using classifier combination. Neural Comput & Applic 20, 955–963 (2011). https://doi.org/10.1007/s00521-010-0395-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0395-3

Keywords

Search

Navigation