Skip to main content
SpringerLink
Log in

Lithology Classification Based on Set-Valued Identification Method

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

Lithology classification using well logs plays a key role in reservoir exploration. This paper studies the problem of lithology identification based on the set-valued method (SV), which uses the SV model to establish the relation between logging data and lithologic types at a certain depth point. In particular, the system model is built on the assumption that the noise between logging data and lithologic types is normally distributed, and then the system parameters are estimated by SV method based on the existing identification criteria. The logging data of Shengli Oilfield in Jiyang Depression are used to verify the effectiveness of SV method. The results indicate that the SV model classifies lithology more accurately than the Logistic Regression model (LR) and more stably than uninterpretable models on imbalanced dataset. Specifically, the Macro-F1 of the SV models (i.e., SV(3), SV(5), and SV(7)) are higher than 85%, where the sandstone samples account for only 22%. In addition, the SV(7) lithology identification system achieves the best stability, which is of great practical significance to reservoir 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

Similar content being viewed by others

References

  1. Delfiner P, Peyret O, and Serra O, Automatic determination of lithology from well logs, SPE Formation Evaluation, 1987, 2(3): 303–310.

    Article  Google Scholar 

  2. Honarkhah M and Caers J, Direct pattern-based simulation of non-stationary geostatistical models, Mathematical Geosciences, 2012, 44(6): 651–672.

    Article  Google Scholar 

  3. Zych M, Stachura G, Hanus R, et al., Application of artificial neural networks in identification of geological formations on the basis of well logging data — A comparison of computational environments’ efficiency, International Workshop on Modeling Social Media, Lyon France, 2018.

  4. Chang J, Li J, Kang Y, et al., Unsupervised domain adaptation using maximum mean discrepancy optimization for lithology identification, Geophysics, 2020, 86(2): 1–84.

    Article  Google Scholar 

  5. Li Z, Kang Y, Lü W, et al., Interpretable semisupervised classification method under multiple smoothness assumptions with application to lithology identification, IEEE Geoscience and Remote Sensing Letters, 2021, 18(3): 386–390.

    Article  Google Scholar 

  6. Chang J, Kang Y, Li Z, et al., Cross-domain lithology identification using active learning and source reweighting, IEEE Geoscience and Remote Sensing Letters, 2020, DOI: https://doi.org/10.1109/LGRS.2020.3041960.

  7. Zhang G, Wang Z, and Chen Y, Deep learning for seismic lithology prediction, Geophysical Journal International, 2018, 215(2): 1368–1387.

    Google Scholar 

  8. Zhu L, Zhang C, Zhang C, et al., Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning, Applied Soft Computing, 2019, 83(105): 596–619.

    Google Scholar 

  9. Ahmadi M A and Chen Z, Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs, Petroleum, 2019, 5(3): 271–284.

    Article  Google Scholar 

  10. Zhu L, Zhang C, Zhang C, et al., A new and reliable dual model- and data-driven toc prediction concept: A toc logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning, Journal of Petroleum Science and Engineering, 2020, 188(106): 944–960.

    Google Scholar 

  11. Sebtosheikh M A, Motafakkerfard R, Riahi M A, et al., Support vector machine method, a new technique for lithology prediction in an iranian heterogeneous carbonate reservoir using petrophysical well logs, Carbonates and Evaporites, 2015, 30(1): 59–68.

    Article  Google Scholar 

  12. Deng C, Pan H, Fang S, et al., Support vector machine as an alternative method for lithology classification of crystalline rocks, Journal of Geophysics and Engineering, 2017, 14(2): 341–349.

    Article  Google Scholar 

  13. Li Z, Kang Y, Feng D, et al., Semi-supervised learning for lithology identification using laplacian support vector machine, Journal of Petroleum Science and Engineering, 2020, 195(107): 510–523.

    Google Scholar 

  14. Ma Y Z, Gomez E, and Luneau B, Integration of seismic and well-log data using statistical and neural network methods, The Leading Edge, 2017, 36(4): 324–329.

    Article  Google Scholar 

  15. Liu H, Wu Y, Cao Y, et al., Well logging based lithology identification model establishment under data drift: A transfer learning method, Sensors, 2020, 20(13): 3643–3660.

    Article  Google Scholar 

  16. Bestagini P, Lipari V, and Tubaro S, A machine learning approach to facies classification using well logs, SEG Technical Program Expanded Abstracts, 2017, 2137–2142, DOI: https://doi.org/10.1190/segam2017-17729805.1.

    Google Scholar 

  17. Dev V A and Eden M R, Evaluating the boosting approach to machine learning for formation lithology classification, Computer Aided Chemical Engineering, 2018, 44: 1465–1470.

    Article  Google Scholar 

  18. Ao Y, Li H, Zhu L, et al., Logging lithology discrimination in the prototype similarity space with random forest, IEEE Geoscience and Remote Sensing Letters, 2018, 16(5): 687–691.

    Article  Google Scholar 

  19. Sun J, Li Q, Chen M, et al., Optimization of models for a rapid identification of lithology while drilling: A win-win strategy based on machine learning, Journal of Petroleum Science and Engineering, 2019, 176: 321–341.

    Article  Google Scholar 

  20. Zhu L, Li H, Yang Z, et al., Intelligent logging lithological interpretation with convolution neural networks, Petrophysics, 2018, 59(6): 799–810.

    Google Scholar 

  21. Imamverdiyev Y and Sukhostat L, Lithological facies classification using deep convolutional neural network, Journal of Petroleum Science and Engineering, 2019, 174: 216–228.

    Article  Google Scholar 

  22. Zhu L, Zhang C, Zhang Z, et al., High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model, Advances in Geo-Energy Research, 2020, 4(2): 135–151.

    Article  Google Scholar 

  23. Xie Y, Zhu C, Zhou W, et al., Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances, Journal of Petroleum Science and Engineering, 2018, 160: 182–193.

    Article  Google Scholar 

  24. Le Y W, Zhang J F, and Yin G G, System identification using binary sensors, IEEE Transactions on Automatic Control, 2003, 48(11): 1892–1907.

    Article  MathSciNet  Google Scholar 

  25. Guo J, Zhang J F, and Zhao Y, Adaptive tracking of a class of first-order systems with binary-valued observations and fixed thresholds, Journal of Systems Science & Complexity, 2012, 25(6): 1041–1051.

    Article  MathSciNet  Google Scholar 

  26. Kang G, Bi W, Zhao Y, et al., A new system identification approach to identify genetic variants in sequencing studies for a binary phenotype, Human Heredity, 2014, 78(2): 104–116.

    Article  Google Scholar 

  27. Wang L Y, Yin G G, Zhang J F, et al., System Identification with Quantized Observations, Basel, Switzerland: Birkhäuser Press, 2010.

    Book  Google Scholar 

  28. Zhao Y, Zhang J F, and Guo J, System identification and adaptive control of set-valued systems, Journal of Systems Science and Mathematical Sciences, 2012, 32(10): 1257–1265.

    MathSciNet  MATH  Google Scholar 

  29. Chen T, Zhao Y, and Jung L L, Impulse response estimation with binary measurements: A regularized fir model approach, IFAC Proceedings Volumes, 2012, 45(16): 113–118.

    Article  Google Scholar 

  30. Bi W, Kang G, Zhao Y, et al., SVSI: Fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits, Annals of Human Genetics, 2015, 79(4): 294–309.

    Article  Google Scholar 

  31. Zhao Y, Bi W, and Wang T, Iterative parameter estimate with batched binary-valued observations, Science China Information Sciences, 2016, 59(5): 1–18.

    Article  Google Scholar 

  32. Bi W, Zhao Y, Liu C, et al., Set-valued analysis for genome-wide association studies of complex diseases, Proceedings of the 32nd Chinese Control Conference (CCC), Xi’an, China, 2013.

  33. Wang T, Bi W, Zhao Y, et al., Radar target recognition algorithm based on rcs observation sequence-set-valued identification method, Journal of Systems Science & Complexity, 2016, 29(3): 573–588.

    Article  MathSciNet  Google Scholar 

  34. Wang X, Hu M, Zhao Y, et al., Credit scoring based on the set-valued identification method, Journal of Systems Science & Complexity, 2020, 33(5): 1297–1309.

    Article  Google Scholar 

  35. Dev V A and Eden M R, Formation lithology classification using scalable gradient boosted decision trees, Computers and Chemical Engineering, 2019, 128(2): 392–404.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, China

    Jing Li, Wenjun Lü & Yu Kang

  2. Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Lifang Wu

  3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

    Lifang Wu

  4. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China

    Ting Wang

  5. Shengli Geophysical Research Institute, SINOPEC Group, Dongying, 257022, China

    Deyong Feng

  6. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

    Hansheng Zhou

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lifang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenjun Lü
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Deyong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hansheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjun Lü.

Additional information

This research was supported in part by the National Key Research and Development Project of China under Grant Nos. 2018AAA0100800 and 2018YFE0106800, in part by the SINOPEC Programmes for Science and Technology Development (PE19008-8), in part by the National Natural Science Foundation of China under Grant Nos. 61725304, 61803370, and 61903353, in part by the Major Science and Technology Project of Anhui Province (201903a07020012), in part by the University Synergy Innovation Program of Anhui Province (GXXT-2021-010), and in part by the Fundamental Research Funds for the Central Universities (WK2100000013).

This paper was recommended for publication by Editor LI Hongyi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Wu, L., Lü, W. et al. Lithology Classification Based on Set-Valued Identification Method. J Syst Sci Complex 35, 1637–1652 (2022). https://doi.org/10.1007/s11424-022-1059-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-022-1059-y

Keywords

Search

Navigation