All Issue

2024 Vol.61, Issue 6 Preview Page

Research Paper

Development of Deep Learning-Based Detection Model for Borehole Breakout Depths in Image Logs

딥러닝기반 이미지로그의 압축파쇄대 심도 검출모델 개발

Hayoung Kim1
,
Juyeol Yeom1
,
Chandong Chang2
,
Yeonguk Jo3
,
Kyungbook Lee14*
김 하영1
,
염 주열1
,
장 찬동2
,
조 영욱3
,
이 경북14*
1Department of Geoenvironmental Sciences, Kongju National University, Gongju-si, Chungnam, Korea
2Department of Geological Sciences, Chungnam National University, Daejeon, Korea
3Deep Subsurface Storage and Disposal Research Center, Geology & Space Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea
4Yellow Sea Institute of Geoenvironmental Sciences, Gongju-si, Chungnam, Korea
1국립공주대학교 지질환경과학과
2충남대학교 지질환경과학과
3한국지질자원연구원 국토우주지질연구본부 심층처분환경연구센터
4황해지질환경연구소
*Corresponding Author
31 December 2024. pp. 468-479
PDF XML Citation
Abstract

This study applied the long short-term memory (LSTM) algorithm to detect the depths of borehole breakouts in image logs to overcome the limitations of the extreme gradient boosting (XGBoost) model. Using 99,084 data obtained from the preprocessed image log of a 1 km deep borehole in Gyeongju-si, we developed both XGBoost and LSTM models. While the XGBoost model considers only single-depth data, the LSTM model analyzes the occurrence of borehole breakouts at the central depth using data from seven consecutive depths. The LSTM model showed an 88% reduction in false negatives and 29% improvement in recall on the test data compared to the XGBoost model. It also outperformed the XGBoost model that using the seven-depth data. These results suggest that the LSTM model can systematically analyze sequential depth data to enhance the efficiency of experts in stress analysis.

Keywords
borehole breakout
image log
long short-term memory (LSTM)
extreme gradient boosting (XGBoost)
confusion matrix

본 연구는 이미지로그의 압축파쇄대 심도를 검출하기 위해 장단기기억(long short-term memory, LSTM)을 적용하여 XGBoost(extreme gradient boosting) 모델의 한계를 보완하고자 한다. 경주시 심도 1 km 시추공의 이미지로그를 전처리하여 얻어진 99,084개 자료로 XGBoost와 LSTM 모델을 개발하였다. 단일 심도만을 고려하는 XGBoost 모델과 달리, LSTM 모델은 연속된 7개 심도자료로 중앙심도의 압축파쇄대 여부를 분석한다. XGBoost 대비 LSTM 모델은 테스트자료에서 false negative가 88% 감소하였고 재현율이 29% 향상되었다. 또한 7개 심도자료를 활용한 XGBoost 모델과 비교 시에도 우수한 성능을 보였다. 이는 LSTM이 연속된 심도자료를 체계적으로 분석하여 전문가의 응력분석 효율성을 향상시킬 수 있음을 시사한다.

키워드
압축파쇄대
이미지로그
장단기기억
익스트림 그래디언트 부스팅
혼동행렬
References
1

Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K., 2019. A state-of-the-art survey on deep learning theory and architectures, Electronics, 8(3), 292p.

10.3390/electronics8030292
2

Alzubaidi, F., Makuluni, P., Clark, S.R., Lie, J.E., Mostaghimi, P., and Armstrong, R.T., 2022. Automatic fracture detection and characterization from unwrapped drill-core images using mask R-CNN, Journal of Petroleum Science and Engineering, 208(Part C), 109471.

10.1016/j.petrol.2021.109471
3

Barton, C.A., Moos, D., Peska, P., and Zoback, M.D., 1997. Utilizing wellbore image data to determine the complete stress tensor: application to permeability anisotropy and wellbore stability, The Log Analyst, 38(6), SPWLA-1997-v38n6a1.

10.1071/AJ97023
4

Barton, C.A., Zoback, M.D., and Burns, K.L., 1988. In-situ stress orientation and magnitude at the Fenton Geothermal Site, New Mexico, determined from wellbore breakouts, Geophysical Research Letters, 15(5), p.467-470.

10.1029/GL015i005p00467
5

Chang, C., 2017. Critical pore pressure assessment for CO2 geological storage in Gyeongsang Basin, Korea, from a geomechanics perspective, Geosciences Journal, 21, p.617-630.

10.1007/s12303-016-0067-0
6

Chen, M.Y., Dahan, C.A., Ekstorm, M.P., Lloyd, P.M., and Rossi, D.J., 1987. Formation imaging with microelectrical scanning arrays, The Log Analyst, 28(3), SPWLA-1987-v28n3a4.

7

Chen, T. and Guestrin, C., 2016. XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SICKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, California, USA.

10.1145/2939672.2939785
8

Dias, L.O., Bom, C.R., Faria, E.L., Valentín, M.B., Correia, M.D., de Albuquerque, M.P., de Albuquerque, M.P., and Coelho, J.M., 2020. Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks, Journal of Petroleum Science and Engineering, 191, 107099.

10.1016/j.petrol.2020.107099
9

Du, L., Lu, X., and Li, H., 2023. Automatic fracture detection from the images of electrical image logs using Mask R-CNN, Fuel, 351, 128992.

10.1016/j.fuel.2023.128992
10

Elman, J.L., 1990. Finding structure in time, Cognitive Science, 14(2), p.179-211.

10.1016/0364-0213(90)90002-E
11

Friedman, J.H., 2001. Greedy function approximation: A gradient boosting machine, The Annals of Statistics, 29(5), p.1189-1232.

10.1214/aos/1013203451
12

Han, S., Xiao, X., Song, B., Guan, T., Zhang, Y., and Lyu, M., 2023. Automatic borehole fracture detection and characterization with tailored Faster R-CNN and simplified Hough transform, Engineering Applications of Artificial Intelligence, 126(Part C), 107024.

10.1016/j.engappai.2023.107024
13

Heidbach, O., Rajabi, M., Cui, X., Fuchs, K., Müller, B., Reinecker, J., Reiter, K., Tingay, M., Wenzel, F., Xie, F., Ziegler, M.O., Zoback, M.L., and Zoback, M., 2018. The World Stress Map database release 2016: Crustal stress pattern across scales, Tectonophysics, 744, p.484-498.

10.1016/j.tecto.2018.07.007
14

Heidbach, O., Rajabi, M., Reiter, K., and Ziegler, M., 2016. World stress map 2016, GFZ Data Services.

15

Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory, Neural Computation, 9(8), p.1735-1780.

10.1162/neco.1997.9.8.17359377276
16

Hochreiter, S., 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 6(2), p.107-116.

10.1142/S0218488598000094
17

Hwang, H., Kim, S., and Song, G., 2018. XGBoost model to identify potential factors improving and deteriorating elderly cognition, The Journal of Korean Institute of Next Generation Computing, 14(3), p.16-24.

18

Jo, Y., Chang, C., Ji, S.H., and Park, K.W., 2019. In situ stress states at KURT, an underground research laboratory in South Korea for the study of high-level radioactive waste disposal, Engineering Geology, 259, 105198.

10.1016/j.enggeo.2019.105198
19

Ki, S., Seo, J., Kwon, O., and Jang, I., 2019. Prediction of missing tubing head pressure using recurrent neural network, Journal of the Korean Society of Mineral and Energy Resources Engineers, 56(5), p.416-426.

10.32390/ksmer.2019.56.5.416
20

Kim, H., Baek, M., Ki, S., Chen, Z., and Lee, K., 2024. Improvement of RNN-based prediction for shale-gas production with data preprocessing in Montney shale play, Gas Science and Engineering (under review).

21

Lagraba, J.O.P., Hansen, S.M., Spalburg, M., and Helmy, M., 2010. Borehole image tool design, value of information, and tool selection, Dipmeter and Borehole Image Log Technology, 92, p.15-38.

22

Lee, D.Y., Shin, H.J., and Lim, J.S., 2024. Analysis of the shale gas production prediction model based on machine learning using productivity factors, Journal of the Korean Society of Mineral and Energy Resources Engineers, 61(2), p.111-123.

10.32390/ksmer.2024.61.2.111
23

Lee, K., Lim, J., Yoon, D., and Jung, H., 2019. Prediction of shale-gas production at duvernay formation using deep-learning algorithm, SPE Journal, 24(6), p.2423-2437.

10.2118/195698-PA
24

Li, X., Li, Q., Bai, B., Wei, N., and Yuan, W., 2016. The geomechanics of Shenhua carbon dioxide capture and storage (CCS) demonstration project in Ordos Basin, China, Journal of Rock Mechanics and Geotechnical Engineering, 8(6), p.948-966.

10.1016/j.jrmge.2016.07.002
25

Liu, G., Chen, X., Luan, Y., and Li, D., 2024. VirusPredictor: XGBoost-based software to predict virus-related sequences in human data, Bioinformatics, 40(4), btae192.

10.1093/bioinformatics/btae19238597887PMC11052659
26

Ma, J., Ding, Y., Cheng, J.C.P., Tan, Y., Gan, V.J.L., and Zhang, J., 2019. Analyzing the leading causes of traffic fatalities using XGBoost and grid-based analysis: a city management perspective, IEEE Access, 7, p.148059-148072.

10.1109/ACCESS.2019.2946401
27

Olya, B.A.M., Mohebian, R., Bagheri, H., Hezaveh, A.M., and Mohammadi, A.K., 2024. Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5, Interpretation, 12(2), p.SB9-SB18.

10.1190/INT-2022-0104.1
28

Qiao, B., Wu, J., Wang, R., Hao, Y., Wang, P., Han, D., and Wu, G., 2024. A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction, The Journal of Supercomputing, 80, p.11293-11321.

10.1007/s11227-023-05863-3
29

Schuster, M. and Paliwal, K.K., 1997. Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, 45(11), p.2673-2681.

10.1109/78.650093
30

Shamir, G. and Zoback, M.D., 1992. Stress orientation profile to 3.5 km depth near the San Andreas Fault at Cajon Pass, California, Journal of Geophysical Research: Solid Earth, 97(B4), p.5059-5080.

10.1029/91JB02959
31

XGBoost, 2024.06.27., https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBRFClassifier

32

Yang, J., Goodfellow, S.D., and Harrison, J.P., 2022. Automated extraction of borehole breakout properties from acoustic televiewer (ATV) data, Proceedings of the 56th U.S. Rock Mechanics/Geomechanics Symposium, ARMA, Santa Fe, New Mexico, USA.

10.56952/ARMA-2022-0408
33

Yeom, J., Kim, H., Lee, K., Chang, C., and Jo, Y., 2023. Detection of borehole breakout depth in image logs using machine learning algorithms, The Korean Society of Mineral and Energy Resources Engineers, 60(4), p.223-230.

10.32390/ksmer.2023.60.4.223
34

Zhang, W., Wu, T., Li, Z., Li, Y., Qiu, A., and Shi, Y., 2021. Automatic detection of fractures based on optimal path search in well logging images, Journal of Sensors, 2021(1), 5577084.

10.1155/2021/5577084
35

Zhu, J., Hu, K., Lu, X., Huang, X., Liu, K., and Wu, X., 2015. A review of geothermal energy resources, development, and applications in China: Current status and prospects, Energy, 93(1), p.466-483.

10.1016/j.energy.2015.08.098
36

Zoback, M.D., 2010. Reservoir geomechanics, Cambridge University Press, 449p.

Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 61
  • No :6
  • Pages :468-479
  • Received Date : 2024-11-01
  • Revised Date : 2024-12-04
  • Accepted Date : 2024-12-09
  • DOI :https://doi.org/10.32390/ksmer.2024.61.6.468
Journal Informaiton Journal of the Korean Society of Mineral and Energy Resources Engineers Journal of the Korean Society of Mineral and Energy Resources Engineers
Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close