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2021 Vol.58, Issue 3 Preview Page

Research Paper

June 2021. pp. 215-226
Abstract
References
1
Agatonovic-Kustrin, S. and Beresford, R., 2000. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, Journal of Pharmaceutical and Biomedical Analysis, 22(5), p.717-727. 10.1016/S0731-7085(99)00272-1
2
Agbasi, O.E., 2013. Estimation of water saturation using a modeled equation and Archie's equation from wire-line logs, Niger Delta Nigeria, IOSR Journal of Applied Physics, 3, p.66-71. 10.9790/4861-0346671
3
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
4
Al-Bulushi, N., King, P.R., Blunt, M.J., and Kraaijveld, M., 2009. Development of artificial neural network models for predicting water saturation and fluid distribution, Journal of Petroleum Science and Engineering, 68(3-4), p.197-208. 10.1016/j.petrol.2009.06.017
5
Archie, G.E., 1952. Classification of carbonate reservoir rocks and petrophysical considerations, AAPG Bulletin, 36(2), p.278-298. 10.1306/3D9343F7-16B1-11D7-8645000102C1865D
6
Chitsazan, N., Nadiri, A.A., and Tsai, F.T.C., 2015. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging, Journal of Hydrology, 528, p.52-62. 10.1016/j.jhydrol.2015.06.007
7
Dalvand, M. and Falahat, R., 2020. A new rock physics model to estimate shear velocity log, Journal of Petroleum Science and Engineering, 196, 107697. 10.1016/j.petrol.2020.107697
8
Equinor, 2019.02.19, https://data-equinor-com.azurewebsites.net/dataset/volve
9
Gardner, G.H.F., Gardner, L.W., and Gregory, A.R., 1974. Formation velocity and density-The diagnostic basics for stratigraphic traps, Geophysics, 39(6), p.770-780. 10.1190/1.1440465
10
Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory, Neural Computation, 9(8), p.1735-1780. 10.1162/neco.1997.9.8.17359377276
11
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
12
Kwon, S., Park, G., Min, B., Kim, K., Lee, T., and Han, J., 2020. Evaluation of CO2-EOR efficiency in carbonate reservoirs using multiple nonlinear regression analysis, Journal of The Korean Society of Mineral and Energy Resources Engineers, 57(2), p.185-194. 10.32390/ksmer.2020.57.2.185
13
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
14
Lin, G. and Shen, W., 2018. Research on convolutional neural network based on improved Relu piecewise activation function, Procedia Computer Science, 131, p.977-984. 10.1016/j.procs.2018.04.239
15
McCulloch, W.S. and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biology, 5(4), p.115-133. 10.1007/BF02478259
16
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, 194, 107291. 10.1016/j.petrol.2020.107291
17
Moradi, S., Moeini, M., Al-Askari, M.K.G., and Mahvelati, E.H., 2016. Determination of shale volume and distribution patterns and effective porosity from well log data based on cross-plot approach for a shaly carbonate gas reservoir. IOP Conf. Series: Earth and Environmental Science, 44(042002), p.1755-1315. 10.1088/1755-1315/44/4/042002
18
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
19
Osarogiagbon, A., Oloruntobi, O., Khan, F., Venkatesan, R., and Butt, S., 2020. Gamma ray log generation from drilling parameters using deep learning, Journal of Petroleum Science and Engineering, 195, 107906. 10.1016/j.petrol.2020.107906
20
Park, G., Kwon, S., Ji, M., Min, B., Huy, N.X., Kim, K., Kim, S., Lee, K.B., 2021. A review on deep learning applications in logging data to model gas-hydrate-bearing sediments, Journal of The Korean Society of Mineral and Energy Resources Engineers, 58(2), p.161-178. 10.32390/ksmer.2021.58.2.161
21
Pham, N., Wu, X., and Zabihi Naeini, E., 2020. Missing well log prediction using convolutional long short-term memory network, Geophysics, 85(4), p.WA159-WA171. 10.1190/geo2019-0282.1
22
Ravasi, M., Vasconcelos, I., Curtis, A., and Kristi, A., 2015. Vector-acoustic reverse time migration of Volve ocean-bottom cable data set without up/down decomposed wavefields, Geophysics, 80(4), p.S137-S150. 10.1190/geo2014-0554.1
23
Samo, A.O., 2020. Reservoir Characterization of the Volve Field North Sea, using rock-physics modeling, MS Thesis, Texas A&M University-Kingsville, USA.
24
Sen, S. and Ganguli, S. S., 2019. Estimation of Pore Pressure and Fracture Gradient in Volve Field, Norwegian North Sea. SPE Oil and Gas India Conference and Exhibition, Mumbai, India, April 9-11. 10.2118/194578-MS
25
Sherstinsky, A., 2020. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D: Nonlinear Phenomena, 404, 132306. 10.1016/j.physd.2019.132306
26
Simandoux, P., 1963. Dielectric measurements on porous media, application to the measurements of water saturation: study of behavior of argillaceous formations, Revue de l'Institut Francais du Petrol, 18(suppl), p.93-215.
27
Sun, J., Ma, X., and Kazi, M., 2018. Comparison of decline curve analysis DCA with recursive neural networks RNN for production forecast of multiple wells. SPE Western Regional Meeting, California, USA, April 22-26. 10.2118/190104-MS
28
Szabó, N.P., 2011. Shale volume estimation based on the factor analysis of well-logging data, Acta Geophysica, 59(5), 935. 10.2478/s11600-011-0034-0
29
Temizel, C., Canbaz, C.H., Saracoglu, O., Putra, D., Baser, A., Erfando, T., Krishna S., and Saputelli, L., 2020. Production forecasting in shale reservoirs through conventional DCA and machine/deep learning methods. Unconventional Resources Technology Conference, Virtual, July 20-22. 10.15530/urtec-2020-2878PMC7869042
30
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
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 :3
  • Pages :215-226
  • Received Date :2021. 04. 14
  • Revised Date :2021. 06. 22
  • Accepted Date : 2021. 06. 25