All Issue

2021 Vol.58, Issue 2 Preview Page

Technical Report

A Review on Deep Learning Applications in Logging Data to Model Gas-Hydrate-Bearing Sediments

가스하이드레이트 저류층 모델링을 위한 심층학습 기반 물리검층 해석의 최신 기술동향 분석

Gayoung Park1
Seoyoon Kwon2
Minsoo Ji1
Baehyun Min3*
Xuan Huy Nguyen 4
Kwang Hyun Kim5
Sungil Kim6
Kyungbook Lee7
박 가영1
권 서윤2
지 민수1
민 배현3*
Nguyen Xuan Huy4
김 광현5
김 성일6
이 경북7
1M.S Course, Department of Climate and Energy Systems Engineering, Ewha Womans University
2All but Dissertation, Department of Climate and Energy Systems Engineering, Ewha Womans University
3Associate Professor, Department of Climate and Energy Systems Engineering, Ewha Womans University
4Assistant Professor, Faculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology
5Assistant Researcher, Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources
6Senior Researcher, Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources
7Assistant Professor, Department of Geoenvironmental Sciences, Kongju National University
1이화여자대학교 기후‧에너지시스템공학과 석사과정
2이화여자대학교 기후‧에너지시스템공학과 박사과정수료
3이화여자대학교 기후‧에너지시스템공학과 부교수
4호치민시기술대학교 지질석유공학과 조교수
5한국지질자원연구원 석유해저연구본부 연구원
6한국지질자원연구원 석유해저연구본부 선임연구원
7공주대학교 지질환경과학과 조교수
*Corresponding Author
April 2021. pp. 161-178
PDF XML Citation
Abstract

본 연구는 국내 가스하이드레이트 저류층에 대한 심층학습 모델링 연구의 기반을 제공하고자 한다. 이를 위해 유·가스 저류층 및 가스하이드레이트 저류층에서 물리검층 자료에 신경망 기법을 적용한 최신 기술동향을 분석한다. 물리검층 분야에서 신경망 기법을 적용한 국내외 연구사례들을 신경망이 예측하고자 하는 출력자료 유형에 따라 저류층 암상 추정, 유체유동인자 추정, 지질역학인자 추정, 합성로그 생성 등 네 가지 적용 유형으로 분류하였다. 각 사례들의 주요 특징을 기술하는 한편 특징들을 요약한 종합표를 제공한다.

키워드
가스하이드레이트
물리검층
심층학습
적용 유형

This study aims to provide the fundamentals of deep learning-based modeling for gas hydrate-bearing sediments. For this purpose, we analyze state-of-the-art neural network applications in geophysical logs for conventional oil and gas reservoirs and unconventional gas-hydrate-bearing sediments. According to the type of neural network outputs, the previous studies are categorized into four application types: estimation of reservoir lithofacies, estimation of fluid flow parameters, estimation of geomechanical parameters, and generation of synthetic logs. For each type, we describe the key points of previous studies and provide summary tables.

Keywords
Gas hydrate
Geophysical logs
Deep learning
Application type
References
1
Adams, S.J., 2005. Core-to-Log Comparison-What's a Good Match?. Proceedings of SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, October 9-12. 10.2118/97013-MS
2
Akinnikawe, O., Lyne, S., and Roberts, J., 2018. Synthetic Well Log Generation Using Machine Learning Techniques. Unconventional Resources Technology Conference, Houston, Texas, USA, July 23-25. 10.15530/urtec-2018-2877021
3
Al Khalifah, H., Glover, P.W.J., and Lorinczi, P., 2020. Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques. Marine and Petroleum Geology, 112, 104096. 10.1016/j.marpetgeo.2019.104096
4
Alizadeh, B., Najjari, S., and Kadkhodaie-Ilkhchi, A., 2012. Artificial Neural Network Modeling and Cluster Analysis for Organic Facies and Burial History Estimation Using Well Log Data: A Case Study of the South Pars Gas Field, Persian Gulf, Iran. Computers & Geosciences, 45, p.261-269. 10.1016/j.cageo.2011.11.024
5
Ashraf, U., Zhu, P., Yasin, Q., Anees, A., Imraz, M., Mangi, H. N., and Shakeel, S., 2019. Classification of Reservoir Facies Using Well Log and 3D Seismic Attributes for Prospect Evaluation and Field Development: A Case Study of Sawan Gas Field, Pakistan. Journal of Petroleum Science and Engineering, 175, p.338-351. 10.1016/j.petrol.2018.12.060
6
Avansi, G.D. and Schiozer, D.J., 2015. A New Approach to History Matching Using Reservoir Characterization and Reservoir Simulation Integrated Studies. Offshore Technology Conference, Houston, Texas, USA, May 4-7. 10.4043/26038-MS
7
Chaki, S., Routray, A., and Mohanty, W.K., 2018. Well-Log and Seismic Data Integration for Reservoir Characterization: A Signal Processing and Machine-Learning Perspective. IEEE Signal Processing Magazine, 35(2), p.72-81. 10.1109/MSP.2017.2776602
8
Chen, Y. and Zhang, D., 2020. Physics-Constrained Deep Learning of Geomechanical Logs. IEEE Transactions on Geoscience and Remote Sensing, 58(8), p.5932-5943. 10.1109/TGRS.2020.2973171
9
Cheng, C.-L., Shalabh, and Garg, G., 2014. Coefficient of Determination for Multiple Measurement Error Models. Journal of Multivariate Analysis, 126, p.137-152. 10.1016/j.jmva.2014.01.006
10
Collett, T.S., 1992. Well Log Evaluation of Natural Gas Hydrates, Topical Report, United States Geological Survey: Denver, Colorado, USA, 52p. 10.2172/10142315
11
Guerin, G. and Goldberg, D., 2002. Sonic Waveform Attenuation in Gas Hydrate-Bearing Sediments from the Mallik 2L-38 Research Well, Mackenzie Delta, Canada. Journal of Geophysical Research, 107(B5), p.1-11. 10.1029/2001JB000556
12
Huh, D.G. and Lee, J.Y., 2017. Overview of Gas Hydrates R&D. Journal of the Korean Society of Mineral and Energy Resources Engineers, 54(2), p.201-214. 10.12972/ksmer.2017.54.2.201
13
Jafari Kenari, S.A. and Mashohor, S., 2013. Robust Committee Machine for Water Saturation Prediction. Journal of Petroleum Science and Engineering, 104, p.1-10. 10.1016/j.petrol.2013.03.009
14
Jeong, T., Byun, J., Choi, H., and Yoo, D., 2014. Estimation of Gas Hydrate Saturation in the Ulleung Basin Using Seismic Attributes and a Neural Network. Journal of Applied Geophysics, 106, p.37-49. 10.1016/j.jappgeo.2014.04.006
15
Karimpouli, S., Fathianpour, N., and Roohi, J., 2010. A New Approach to Improve Neural Networks' Algorithm in Permeability Prediction of Petroleum Reservoirs Using Supervised Committee Machine Neural Network (SCMNN). Journal of Petroleum Science and Engineering, 73(3-4), p.227-232. 10.1016/j.petrol.2010.07.003
16
Kil, S.C., Nah, D.B., and Oh, M.S., 2009. The Global Trends of the Patents for the Gas Hydrates. Economic and Environmental Geology, 42(6), p.655-668.
17
Kim, S., Kim, K.H., Min, B., Lim, J., and Lee, K., 2020. Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada. Geofluids, 2020, p.1-26. 10.1155/2020/5387183
18
Lee, D.G., Shin, H.J., and Lim, J.S., 2013a. 3D Spatial Distribution Modeling for Petrophysical Property of Gas Hydrate-Bearing Sediment Using Well Data in Ulleung Basin. Journal of Energy Engineering, 22(2), p.156-168. 10.5855/ENERGY.2013.22.2.156
19
Lee, G.H., Yi, B.Y., Yoo, D.G., Ryu, B.J., and Kim, H.J., 2013b. Estimation of the Gas-Hydrate Resource Volume in a Small Area of the Ulleung Basin, East Sea Using Seismic Inversion and Multi-Attribute Transform Techniques. Marine and Petroleum Geology, 47, p.291-302. 10.1016/j.marpetgeo.2013.04.001
20
Lee, J., Byun, J., Kim, B., and Yoo, D.G., 2017. Delineation of Gas Hydrate Reservoirs in the Ulleung Basin Using Unsupervised Multi-Attribute Clustering without Well Log Data. Journal of Natural Gas Science and Engineering, 46, p.326-337. 10.1016/j.jngse.2017.08.007
21
Lee, M.W., 2008. Models for Gas Hydrate-Bearing Sediments Inferred from Hydraulic Permeability and Elastic Velocities. Scientific Investigations Report 2008-5219, US Geological Survey, Reston, Virginia, USA. 10.3133/sir20085219
22
Lim, J.S. and Park, H.J., 2005. Polynominal Neural Network Approach to Reservoir Permeability Estimation from Well Logs. Journal of the Korean Society for Geosystem Engineering, 42(3), p.173-179.
23
Lim, J.S., 2003. Reservoir Permeability Determination Using Artificial Neural Network. Journal of the Korean Society for Geosystem Engineering, 40(4), p.232-238.
24
Mahmoudi, S. and Mahmoudi, A., 2014. Water Saturation and Porosity Prediction Using Back-Propagation Artificial Neural Network (BPANN) from Well Log Data. Journal of Engineering and Technology , 5(2), p.1-8.
25
Majumder, M., 2009. Identification of Gas Hydrates Using Well Log Data-A Review. Geohorizons, 56(7), p.40-48.
26
Miah, M.I., Zendehboudi, S., and Ahmed, S., 2020. Log Data- Driven Model and Feature Ranking for Water Saturation Prediction Using Machine Learning Approach. Journal of Petroleum Science and Engineering, 107291. 10.1016/j.petrol.2020.107291
27
Min, B., Kwon, S., Park, G., Jeong, D., and Lee, H., 2020. Current Status and Prospects of Artificial Intelligence in the Oil and Gas Exploration and Production Business. Journal of the Korean Society of Mineral and Energy Resources Engineers, 57(3), p.295-308. 10.32390/ksmer.2020.57.3.295
28
Mohamed, I.A., Hemdan, M., Hosny, A., and Rashidy, M., 2019. High-Resolution Water-Saturation Prediction Using Geostatistical Inversion and Neural Network Methods. Interpretation, 7(2), p.455-465. 10.1190/INT-2018-0153.1
29
Mukherjee, B. and Sain, K., 2019. Prediction of Reservoir Parameters in Gas Hydrate Sediments Using Artificial Intelligence (AI): A Case Study in Krishna-Godavari Basin (NGHP Exp-02). Journal of Earth System Science, 128, 199. 10.1007/s12040-019-1210-x
30
Ochie, K.I. and Rotimi, O.J., 2018. Geostatistics - Kriging and Co-Kriging Methods in Reservoir Characterization of Hydrocarbon Rock Deposits. SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, August 6-8. 10.2118/193483-MS
31
Onalo, D., Adedigba, S., Khan, F., James, L.A., and Butt, S., 2018. Data Driven Model for Sonic Well Log Prediction. Journal of Petroleum Science and Engineering, 170, p.1022- 1037. 10.1016/j.petrol.2018.06.072
32
Pandey, A.K., Chatterjee, R., and Choudhury, B. 2020. Application of Neural Network Modelling for Classifying Hydrocarbon Bearing Zone, Water Bearing Zone and Shale with Estimation of Petrophysical Parameters in Cauvery Basin, India. Journal of Earth System Science, 129, 33. 10.1007/s12040-019-1285-4
33
Pham, N. and Naeini, E.Z., 2019. Missing Well Log Prediction Using Deep Recurrent Neural Networks. 81st EAGE Conference and Exhibition 2019, London, UK, June 3-6. 10.3997/2214-4609.201901612
34
Pham, N., Wu, X., and Naeini, E., 2020. Missing Well Log Prediction Using Convolutional Long Short-Term Memory Network. Geophysics, 85(4), p.1-55. 10.1190/geo2019-0282.1
35
Rolon, L., Mohaghegh, S.D., Ameri, S., Gaskari, R., and McDaniel, B., 2009. Using Artificial Neural Networks to Generate Synthetic Well Logs. Journal of Natural Gas Science and Engineering, 1(4-5), p.118-133. 10.1016/j.jngse.2009.08.003
36
Ryu, B.J., 2018. Chimney Structures in the Deep-water Ulleung Basin, East Sea. New & Renewable Energy , 12(14), p.14-26. 10.7849/ksnre.2018.12.14.4.014
37
Salehi, M.M., Rahmati, M., Karimnezhad, M., and Omidvar, P., 2017. Estimation of the Non Records Logs from Existing Logs Using Artificial Neural Networks. Egyptian Journal of Petroleum, 26(4), p.957-968. 10.1016/j.ejpe.2016.11.002
38
Saputro, O.D., Maulana, Z.L., and Latief, F.D.E., 2016. Porosity Log Prediction Using Artificial Neural Network. Journal of Physics: Conference Series, 739, 012092. 10.1088/1742-6596/739/1/012092
39
Seo, K., Lim, J.S., and Lee, J., 2010. Estimation of Petrophysical Properties in Gas Hydrate Bearing Sediments using the 1st Well Data in Ulleung Basin, Offshore Korea. Journal of the Korean Society of Mineral and Energy Resources Engineers, 47(1), p.70-80.
40
Singh, A., Ojha, M., and Sain, K., 2020. Predicting Lithology using Neural Networks from Downhole Data of a Gas Hydrate Reservoir in the Krishna-Godavari Basin, Eastern Indian Offshore. Geophysical Journal International, 220(3), p.1813-1837. 10.1093/gji/ggz522
41
Singh, H., Seol, Y., and Myshakin, E.M., 2021. Prediction of Gas Hydrate Saturation Using Machine Learning and Optimal Set of Well-Logs. Computational Geosciences, 25, 267-283. 10.1007/s10596-020-10004-3
42
Singha, D.K., Chatterjee, R., Sen, M.K., and Sain, K., 2014. Pore Pressure Prediction in Gas-Hydrate Bearing Sediments of Krishna-Godavari Basin, India. Marine Geology, 357, p.1-11. 10.1016/j.margeo.2014.07.003
43
Tang, H., Meddaugh, W.S., and Toomey, N., 2011. Using an Artificial-Neural-Network Method to Predict Carbonate Well Log Facies Successfully. SPE Reservoir Evaluation & Engineering, 14(1), p.35-44. 10.2118/123988-PA
44
Varhaug, M., 2016. Basic Well Log Interpretation, Oilfield Review, Schlumberger, Houston, Texas, USA.
45
Wang, H., Wu, W., Chen, T., Dong, X., and Wang, G., 2019. An Improved Neural Network for TOC, S1 and S2 Estimation Based on Conventional Well Logs. Journal of Petroleum Science and Engineering, 176, p.664-678. 10.1016/j.petrol.2019.01.096
46
Wang, X., Lee, M., Collett, T., Yang, S., Guo, Y., and Wu, S., 2014. Gas Hydrate Identified in Sand-Rich Inferred Sedimentary Section Using Downhole Logging and Seismic Data in Shenhu Area, South China Sea. Marine and Petroleum Geology, 51, p.298-306. 10.1016/j.marpetgeo.2014.01.002
47
Xu, Z., Chen, S., Yang, S., and Ma, Z., 2007. Identification Signs and Prospects of Hydrate Gas. Bulletin of Marine Science, 9(1), p.84-96.
48
Yi, H., 2019. Case Analysis of Applications for Deep Learning Technology in the Mining Industry. Journal of the Korean Society of Mineral and Energy Resources Engineers, 56(5), p.435-446. 10.32390/ksmer.2019.56.5.435
49
Zhang, D., Chen, Y., and Meng, J., 2018. Synthetic Well Logs Generation via Recurrent Neural Networks. Petroleum Exploration and Development , 45(4), p.629-639. 10.1016/S1876-3804(18)30068-5
50
Zhu, K., Wang, L., Du, Y., Jiang, C., and Sun, Z. 2019. DeepLog: Identify Tight Gas Reservoir Using Multi-Log Signals by a Fully Convolutional Network. IEEE Geoscience and Remote Sensing Letters, 17(4), p.568-571. 10.1109/LGRS.2019.2930587
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 : 58
  • No :2
  • Pages :161-178
  • Received Date :2021. 03. 04
  • Revised Date :2021. 04. 07
  • Accepted Date : 2021. 04. 27
  • DOI :https://doi.org/10.32390/ksmer.2021.58.2.161
Journal Informaiton Agriculture and Life Sciences Research Institute Journal of the Korean Society of Mineral and Energy Resources Engineers
Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • open access
  • orcid
  • KCI 문헌 유사도 검사
  • ccl
Journal Informaiton Journal Informaiton - close