Research Progress on the Application of Machine Learning in Oil and Gas Exploration and Development
DOI:
https://doi.org/10.54097/dq40e089Keywords:
Machine Learning, Oil and Gas Exploration, Oil and Gas Field Development, Future Prospects.Abstract
Machine learning becomes a powerful tool for large-scale data processing and analysis in the oil and gas industry with its automation, intelligence, efficiency and high accuracy. This paper summarized the current status of the application of machine learning in oil and gas exploration and oil and gas field development in recent years. This paper discussed the current problems faced by the application of machine learning in oil and gas exploration and development. Besides it looked forward to the future. It was found that machine learning helped to improve the efficiency and accuracy of exploration and production and reduce the risk of exploration. Apart from that, machine learning also changed the operation mode of E&P to a certain extent.
Downloads
References
KUANG Lichun, LIU He, REN Yili, et al. Application and development trend of artificial intelligence in petroleum exploration and development [J]. Petroleum Exploration and Development, 2021, 48 (1): 1 - 11.
H.B. Li, P.Y. Luo, Y.Y. Bai, et al. Overview of machine learning algorithms and their application in drilling engineering [J]. Xinjiang Oil and Gas, 2022, 18 (1): 1 - 13.
HANG Dongxiao, CHEN Yuntian, MENG Jin. Well logging curve generation method based on recurrent neural network [J]. Petroleum Exploration and Development,2018, 45 (04): 598 - 607.
Pham N, Wu X, Naeini E Z. Missing well log prediction using convolutional long short-term memory network [J]. Geophysics, 2020, 85 (4): 1 - 55.
YANG Jing, CHEN Yuntian, JIANG Chunbi. A machine learning modeling paradigm for the logging curve generation problem - an example of shale wells in Changning Weiyuan are a[J]. China Offshore Oil and Gas, 2020, 33 (01): 76 - 84.
BITTAR M, WANGS, WUX, et al. Multiple well-log depth matching using deepQ-Learning [J]. Petrophysics-The SPWLA Journal of Formation Evaluation and Petrophysics-The SPWLA Journal of Formation Evaluation and Reservoir Description, 2021, 62 (4): 353 - 361.
ZHOU Xin, CAO Junxing, WANG Xingjian et al. Reconstruction technique of acoustic logging curve based on two-way gated recurrent unit neural network [J]. Advances in Geophysics, 2022, 37 (01): 357 - 366.
CACERES VAT, DUFFAUTK, YAZIDIA, et al. Automated well-log depth matching:1Dconvolutional neural networks vs. classic cross correlation [J]. Petrophysics, 2022, 63 (1): 12 - 34.
TENG Jianqiang, QIU Meng, YANG Mingren, et al. Well log curve prediction method based on gated recurrent unit neural network [J]. Oil and Gas Geology and Recovery, 2023, 30 (01): 93 - 100.
WU X, LIAN, L, SHI Y, et al. Deep learning for local seismic image processing: fault detection, structure-oriented smoothing with edge preserving, and slope estimation [R]. and slope estimation by using a single convolutional neural network [R]. San Antonio: 2019 SEG Annual Meeting, 2019.
PHAN S, SEN M. Deep learning with cross-shape deep Boltzmann machine for pre-stack inversion problem [R]. San Antonio: 2019 SEG Annual Meeting, 2019.
MA Y, CAO S, RECTOR J W, et al. Automatic first arrival picking for borehole seismic data using a pixel-level network[R]. San Antonio: 2019 SEG Annual Meeting, 2019.
Qin Min, Hu Xiangyang, Liang Yunan, et al. Identification of high-temperature and high-pressure reservoir fluids using Stacking model fusion method [J]. Petroleum Geophysical Exploration, 2021, 56 (02): 364 - 371+214 - 215.
CHEN Yan, JIAO Shixiang, CHENG Chao, et al. A semi-supervised compartmentalized interlayer identification method based on self-coder [J]. Specialty Oil and Gas Reservoirs, 2021, 28 (01): 86 - 91.
Y. Zhang, R.W. Ding, S. Zhao et al. A denoising method for shallow stratigraphic profiles based on improved loop generation adversarial network [J]. CTheoretical and Applied Research, 2023, 32 (01): 15 - 25.
CHEN Juan, ZHANG Yanduo, LU Tao. A rock-like crack detection method based on pixel difference convolutional neural network[J]. Journal of Wuhan Engineering University, 2023, 45 (01): 81 - 86+100.
CHENG Guojian, GUO Wenhui, FAN Pengzhao. Rock image classification based on convolutional neural network [J]. Journal of Xi'an Petroleum University (Natural Science Edition), 2017, 32 (04): 116 - 122.
CHENG Guojian, LI Bi, WAN Xiaolong et al. Research on rock flake image classification based on Squeeze Net convolutional neural network [J]. Mineral Rock, 2021, 41 (04): 94 - 101.
LI Liang, XUE Yuan, GAO Yuan et al. Research on the quality control method of exploration and development data based on machine learning [J]. Information System Engineering, 2021 (04): 146 - 147+150.
WU Fan, FANG Zhendong, XIAO Lizhi. Application of offshore multi-source logging data in reservoir capacity prediction [J]. Petrochemical Technology,2022, 29 (01): 145 - 148.
Liu GQ, Gong RB, Shi YJ, et al. Construction of oil and gas formation logging knowledge map and its intelligent recognition method [J]. Petroleum Exploration and Development, 2022, 49 (03): 502 - 512.
Mudan, Wang Zhuwen, Huang Yulong et al. Volcanic lithology identification based on SVM logging data-an example from the eastern depression of the Liao he Basin [J]. Journal of Geophysics, 2015, 58 (05): 1785 - 1793.
Li JG, Zhang WD, Liu GN. Application of deep learning in logging lithology identification [J]. Science and Technology Innovation and Application, 2015 (14): 21 - 22.
An Peng, Cao Danping. Research and application of deep learning-based lithology recognition method for logging [J]. Advances in Geophysics, 2018, 33 (03): 1029 - 1034.
JIANG Kai, WANG Shoudong, HU Yongjing et al. Well logging lithology identification model based on Boosting Tree algorithm [J]. Logging Technology, 2018, 42 (04): 395 - 400.
Feng Yaxing, Gong Xi, Xu Yongyang et al. Research on lithology recognition method based on rock fresh surface image and twinned convolutional neural network [J]. Geography and Geographic Information Science, 2019, 35 (05): 89 - 94.
C. Y. Wu, X. Zhang, C. L. Zhang et al. A lithology identification method based on LSTM recurrent neural network [J]. Lithologic reservoirs,2021, 33 (03): 120 - 128.
Zhang Chi, Pan Mao, Hu Shui-Qing et al. A machine-learning lithology identification method for fusing reservoir longitudinal information[J]. Geoscience and Technology Bulletin, 2023, 42 (03): 289 - 299.
HOU Mingyu, YANG Jianqin, LI Weichong. Research on lithology identification based on graph embedding technology [J]. China Petroleum and Chemical Standards and Quality, 2023, 43 (13): 169 - 171.
Jicai Ding. Quantitative prediction method of reservoir physical properties based on support vector machine [C]//Chinese Geophysical Society, Organizing Committee of National Symposium on Petrology and Geodynamics, Tectonic Geology and Geodynamics Specialized Committee of Chinese Geological Society, Regional Geology and Mineralization Specialized Committee of Chinese Geological Society.2015 Proceedings of the Joint Academic Conference of the Geosciences of China (XX) - -Topic 51 Reservoir Geophysics. China Peace Audio and Video Electronic Press, 2015: 3.
Ni Weijun, Li Qi, Guo Wenhui et al. Transverse wave velocity prediction of shale reservoir based on support vector machine [J]. Journal of Xi'an Petroleum University (Natural Science Edition), 2017, 32 (04): 46 - 49+54.
ZHU Linqi, ZHANG Chong, ZHOU Xueqing et al. A prediction method of reservoir permeability in NMR logging by fusing deep confidence network and with kernel limit learning machine algorithm [J]. Computer Applications, 2017, 37 (10): 3034 - 3038.
LIN Nian-Tian, ZHANG Dong, ZHANG Kai et al. Small-sample convolutional neural network learning and prediction of seismic oil and gas reservoirs [J]. Journal of Geophysics, 2018, 61 (10): 4110 - 4125.
YU H Y, REZAEE R, WANG Z L, et al. A new method for TOC estimation in tight shale gas reservoirs [J]. International Journal of Coal Geology, 2017, 179: 269 - 277.
TANG Cheng, WANG Chongjing, LIANG Bo et al. Research on the calculation model of shale gas evaluation parameters based on machine learning algorithm [J]. Recorded Well Engineering, 2021, 32 (04): 18 - 22.
ZHANG Yuhang, SHI Baohong, ZHANG Yujing et al. Application of machine learning method in porosity prediction of shallow beach-dam phase thin reservoirs--an example of Cretaceous system in Chepaizi area, Junggar Basin[J]. Journal of Sedimentology, 2023, 41 (05): 1559 - 1567.
Xiao Ai, Hongyu Wang, Baitao Sun. Automatic Identification of Sedimentary Facies Based on a Support Vector Machine in the Aryskum Graben. Kazakhstan [J]. Applied Sciences, 2019, 9 (21).
XIAO He, ZHANG Chaomu, SU Xiangqun. Application of logging data to quantitatively identify carbonate sedimentary microphases--A case study of Changxing Formation in Yuanba area, northeast Sichuan [J]. Science, Technology and Engineering, 2020, 20 (07): 2573 - 2582.
Liu X Y, Zhou L, Chen X H, etal. Lithofaciesidentificationusing support vector machine based on local deep multi-kernel learning [J]. Petroleum Science, 2020 (4).
Chen Li, Wang Caizhi, Ning Qianqian et al. Recognition method based on machine learning for petrographic logging of Long7 section in Longdong area of Ordos Basin [J]. Reservoir Evaluation and Development, 2023, 13 (04): 525 - 536.
Liu Zongbao, Cui Yumeng, Li Junhui et al. Convolutional neural network-based lithologic type logging curve recognition in dense sandstone reservoirs [J]. Journal of Heilongjiang University of Science and Technology, 2023, 33 (03): 376 - 383.
Wang Yanping, Jiang Jing, Dang Jingwen et al. An introduction to the dynamic analysis of oil and gas well production [J]. China Petroleum and Chemical Standards and Quality, 2021, 41 (15): 29 - 30.
Tian ZX. Application of machine learning in reservoir production dynamic analysis [D]. China University of Geosciences (Beijing), 2022.
GU Jianwei, ZHOU Mei, LI Zhitao et al. Data mining-based well production prediction method for long and short-term memory network model [J]. Specialty Oil and Gas Reservoir, 2019, 26 (02): 77 - 81+131.
LIU Wei, LIU Wei, GU Jianwei. Daily oil production prediction of oil wells based on machine learning method [J]. Oil Drilling Process, 2020, 42 (01): 70 - 75.
Xue L, Gu Shaohua, Wang Jiabao et al. Dynamic prediction of gas well production based on particle swarm optimization and long- and short-term memory neural networks [J]. Oil Drilling Process, 2021, 43 (04): 525 - 531.
Xue YC, Yuan ZQ, Jin QS et al. Well production prediction based on deep forest algorithm [J]. Science, Technology and Engineering, 2022, 22 (11): 4327 - 4334.
Han Yidong. Capacity prediction of volumetrically fractured horizontal wells in tight reservoirs based on machine learning [D]. Northeast Petroleum University, 2023.
Yao J, Zhang K, Liu JR. Theory and application of intelligent oilfield development [M]. Qingdao: Science Press, 2018: 48 - 51.
Du, Yuqi, Weiss, et al. "Obtain an Optimum Artificial Neural Network Model for Reservoir Studies." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003.
L. Zhang, X. S. Chen, G. X. Li, et al. An automatic history fitting method based on ensemble and neural network architecture search [J]. Journal of China University of Petroleum: Natural Science Edition, 2022, 46 (2): 127 - 136.
LU Yi, HU Hao, CHENG Yabin, et al. Research on automatic history fitting method for reservoir simulation with multiple models [J]. Journal of Southwest Petroleum University: Natural Science Edition, 2022, 44 (6): 97 - 104.
Lee, Sanghyun, and Karl, Dunbar Stephen. "Field Application Study on Automatic History Matching Using Particle Swarm Optimization." paper presented at the SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, UAE, September 2019.
Santoso, Ryan, He, et al. "Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations." Paper presented at the SPE Reservoir Simulation Conference, On-Demand, October 2021.
[55] Vo Thanh, Hung, Sugai, et al. "Integrated Artificial Neural Network and Object-based Modelling for Enhancement History Matching in a Fluvial Channel Sandstone Reservoir." Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Bali, Indonesia, October 2019.
RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations [J]. Journal of Computational Physics, 2019, 378: 686 - 707.
PARK J, DATTA-GUPTA A, SINGH A, et al. Hybrid physics and data -driven modeling for unconventional field development and its application to US onshore basin [J]. Journal of Petroleum Science and Engineering, 2021, 206: 109008.
HUANG Chaoqin, NIAN Kai, WANG Bin, et al. A new model for deep learning of hydrocarbon seepage considering physical process information [J]. Journal of China University of Petroleum: Natural Science Edition, 2020, 44 (4): 47 - 56.
Xue L, Dai C, Han Jiangxia et al. Deep neural network modeling driven by combined reservoir seepage physics and data [J]. Oil and Gas Geology and Recovery, 2022, 29 (01): 145 - 151.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







