Advertisement

Acta Geophysica

, Volume 67, Issue 2, pp 589–596 | Cite as

Study on logging interpretation of coal-bed methane content based on deep learning

  • Parhat Zunu
  • Xiang Min Email author
  • Zhang Fengwei 
Research Article - Applied Geophysics
  • 29 Downloads

Abstract

To solve quantitative interpretation problems in coal-bed methane logging, deep learning is introduced in this study. Coal-bed methane logging data and laboratory results are used to establish a deep belief network (DBN) to compute coal-bed methane content. Network parameter effects on calculations are examined. The calculations of DBN, statistical probabilistic method and Langmuir equation are compared. Results show that, first, the precision and speed of DBN calculation should determine the restricted Boltzmann machine’s quantity. Second, the hidden layer neuron quantity must align with calculation accuracy and stability. Third, the ReLU function is the best for logging data; the Sigmoid function and Linear function are second; and the Softmax function has no effect. Fourth, the cross-entropy function is superior to MSE function. Fifth, RBMs make DBN more accuracy than BPNN. Furthermore, DBN calculation accuracy and stability are better than those of statistical probabilistic method and Langmuir equation.

Keywords

Coal-bed methane Geophysics logging Deep learning Restricted Boltzmann machine Coal-bed methane content 

Notes

Acknowledgements

This research was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2017D01B08), the Scientific Research Planning Project of Xinjiang Uygur Autonomous Region (XJEDU2017S056 and XJEDU2017S057), the Ph.D. Research Startup Foundation of the Xinjiang Institute of Engineering (2016xgy341812), Tianchi Doctor Research Project of Xinjiang Uygur Autonomous Region (BS2017001) and Xinjiang Uygur Autonomous Region key specialty of Geological Engineering.

References

  1. Alipanahi B, Delong A (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 33(8):831–838.  https://doi.org/10.1038/nature14539 CrossRefGoogle Scholar
  2. Bhanja AK, Srivastava OP (2008) A new approach to estimate CBM gas content from well logs. SPE115563:1–5.  https://doi.org/10.2118/115563-ms
  3. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3):197–387.  https://doi.org/10.1561/2000000039 CrossRefGoogle Scholar
  4. Guo Y, Liu Y (2016) Deep learning for visual understanding: a review. Neurocomputing 187(C):27–48.  https://doi.org/10.1016/j.neucom.2015.09.116 CrossRefGoogle Scholar
  5. Hawkins JM, Schraufnagel RA, Olszewski AJ (1992) Estimating coalbed gas content and sorption isotherm using well log data. Phys Rev Lett 97(7):1143–1238.  https://doi.org/10.2523/24905-MS Google Scholar
  6. Hinton GE (2012) A practical guide to training restricted boltzmann machines, vol 7700. Springer, Berlin, pp 599–619Google Scholar
  7. Juanjuan L, Hong C (2006) Researching development on BP neural networks. Control Eng China 13(5):449–451Google Scholar
  8. Junsheng H, Ying W (1999) Interpretation of well logging data for coalbed methane using BP neural network. Geol Prospect 35(3):41–45Google Scholar
  9. Krizhevsky A, Sutskever I (2012) ImageNet classification with deep convolutional neural networks. Int Conf Neural Inf Process Syst 25(2):1097–1105.  https://doi.org/10.1145/3065386 Google Scholar
  10. Langmuir I (1918) The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc 40:1361–1370.  https://doi.org/10.1021/ja02242a004 CrossRefGoogle Scholar
  11. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  12. Liu Z, Luo P (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730–3738.  https://doi.org/10.1109/iccv.2015.425
  13. Noda K, Yamaguchi Y (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737.  https://doi.org/10.1007/s10489-014-0629-7 CrossRefGoogle Scholar
  14. Pan H, Liu G (1997) Applying back- propagation artificial neural networks to predict coal quality parameters and coal bed gas content. Earth Sci J China Univ Geosci 22(2):210–214Google Scholar
  15. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning Represent Back Propag Errors. Nature 323:533–536.  https://doi.org/10.1038/323533a0 CrossRefGoogle Scholar
  16. Silver D, Huang A (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–492.  https://doi.org/10.1038/nature16961 CrossRefGoogle Scholar
  17. Tomczak JM, Gonczarek A (2017) Learning invariant features using subspace restricted boltzmann machine. Neural Process Lett 45(1):173–182.  https://doi.org/10.1007/s11063-016-9519-9 CrossRefGoogle Scholar
  18. Wang Z (2009) Logging methods evaluation of the gas content in Coal-bed methane reservoir. Jilin University, Changchun, pp 55–60Google Scholar
  19. Xiaofan Y, Tingkui C (1994) Inherent advantages and disadvantages of artificial neural networks. Comput Sci 2:23–26Google Scholar
  20. Yang Y, Cloud T, Kirk CV (2005) New application of well log parameters in coalbed methane (CBM) reservoir evaluation at the Drunkards Wash Unit, Uinta Basin, Utah. In: SPE Eastern regional meeting, 1–9.  https://doi.org/10.2523/97988-ms
  21. Zeliang J, Haifei X, Haibin G (2013) Technology for evaluation of CBM reservoir logging and its application. Coal Geol Explor 41(2):42–45Google Scholar
  22. Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931–934.  https://doi.org/10.1038/nmeth.3547 CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.Xinjiang Institute of EngineeringUrumqiChina

Personalised recommendations