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

• Autorzy: Zunu Parhat , Min Xiang , Fengwei Zhang

Identyfikatory

DOI

10.1007/s11600-019-00245-x

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

coal-bed methane; geophysical logging; deep learning; restricted Boltzmann machine; content

PL

metan ze złóż węglowych; profilowania geofizyczne; uczenie głębokie; ograniczona maszyna Boltzmanna; zawartość

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 2
• Strony: 589--596
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Zunu Parhat

• Xinjiang Institute of Engineering, Urumqi 830001, Xinjiang, China

autor

Min Xiang

• Xinjiang Institute of Engineering, Urumqi 830001, Xinjiang, China

autor

Fengwei Zhang

• Xinjiang Institute of Engineering, Urumqi 830001, Xinjiang, China

Bibliografia

• 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
• 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
• 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
• 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
• 6. Hinton GE (2012) A practical guide to training restricted boltzmann machines, vol 7700. Springer, Berlin, pp 599–619
• 7. Juanjuan L, Hong C (2006) Researching development on BP neural networks. Control Eng China 13(5):449–451
• 8. Junsheng H, Ying W (1999) Interpretation of well logging data for coalbed methane using BP neural network. Geol Prospect 35(3):41–45
• 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
• 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
• 11. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
• 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
• 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):210–214
• 15. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning Represent Back Propag Errors. Nature 323:533–536. https://doi.org/10.1038/323533a0
• 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
• 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
• 18. Wang Z (2009) Logging methods evaluation of the gas content in Coal-bed methane reservoir. Jilin University, Changchun, pp 55–60
• 19. Xiaofan Y, Tingkui C (1994) Inherent advantages and disadvantages of artificial neural networks. Comput Sci 2:23–26
• 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):42–45
• 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

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).