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油气资源开发的大数据智能平台及应用分析

宋洪庆 都书一 周园春 王宇赫 王九龙

宋洪庆, 都书一, 周园春, 王宇赫, 王九龙. 油气资源开发的大数据智能平台及应用分析[J]. 工程科学学报, 2021, 43(2): 179-192. doi: 10.13374/j.issn2095-9389.2020.07.21.001
引用本文: 宋洪庆, 都书一, 周园春, 王宇赫, 王九龙. 油气资源开发的大数据智能平台及应用分析[J]. 工程科学学报, 2021, 43(2): 179-192. doi: 10.13374/j.issn2095-9389.2020.07.21.001
SONG Hong-qing, DU Shu-yi, ZHOU Yuan-chun, WANG Yu-he, WANG Jiu-long. Big data intelligent platform and application analysis for oil and gas resource development[J]. Chinese Journal of Engineering, 2021, 43(2): 179-192. doi: 10.13374/j.issn2095-9389.2020.07.21.001
Citation: SONG Hong-qing, DU Shu-yi, ZHOU Yuan-chun, WANG Yu-he, WANG Jiu-long. Big data intelligent platform and application analysis for oil and gas resource development[J]. Chinese Journal of Engineering, 2021, 43(2): 179-192. doi: 10.13374/j.issn2095-9389.2020.07.21.001

油气资源开发的大数据智能平台及应用分析

doi: 10.13374/j.issn2095-9389.2020.07.21.001
基金项目: 国家自然科学基金资助项目(11972073);中央高校基本科研业务费资助项目(FRF-TP-19-005B1)
详细信息
    通讯作者:

    E-mail:songhongqing@ustb.edu.cn

  • 中图分类号: TE3

Big data intelligent platform and application analysis for oil and gas resource development

More Information
  • 摘要: 油气资源大数据智能平台的总体框架应以数据资源为基础、大数据平台算力为支撑、人工智能算法为核心,面向油气行业生产需求,构建集勘探、开发、生产数据于一体的油气数据资源池,通过数据清洗与融合提升数据质量,整合物理模拟与数据挖掘等手段,实现服务功能模块化,并在PC端、管控大屏、手机移动APP等多维平台实现智能监测、预警与展示。通过对深度学习等人工智能方法在油气工业领域的应用案例分析,表明其具有较好的应用前景。未来石油公司应与科研院所通力合作,挖掘石油工业数据的巨大潜能,实现降本增效,建设全新的智能油气工业生态圈,完成产业升级。
  • 图  1  SPE-one petro(美国石油工程师协会)数据库中机器学习相关文章增长图

    Figure  1.  Graph depicting the increase in the number of machine learning-related articles in SPE-OnePetro

    图  2  国内外油气大数据智能平台构建实例图

    Figure  2.  Construction and example of the intelligent platform for domestic and foreign oil and gas big data

    图  3  国内外油气工业数字化转型发展历程

    Figure  3.  Development process of the digital transformation of the oil and gas industry at home and abroad

    图  4  油田工业大数据“6V”特性[13]

    Figure  4.  Oilfield industry big data “6V” features[13]

    图  5  油气大数据智能平台基本流程与总体框架

    Figure  5.  Basic process and overall framework of oil and gas big data intelligent platform

    图  6  油气大数据智能平台Hadoop、Spark及Storm混合存储计算架构

    Figure  6.  Oil and gas big data intelligent platform with Hadoop, Spark, and Storm hybrid storage computing architecture

    图  7  油气工业多源异构数据体的清洗融合

    Figure  7.  Cleaning and fusion of multi-source data in the oil and gas industry

    图  8  油气行业常用人工智能算法

    Figure  8.  Artificial intelligence algorithms commonly used in the oil and gas industry

    图  9  基于卷积神经网络的储层物性预测流程

    Figure  9.  Reservoir property prediction process based on convolutional neural network

    图  10  基于深度BP神经网络的储层连通性预测[72]

    Figure  10.  Reservoir connectivity prediction based on deep BP neural network[72]

    图  11  基于LSTM神经网络的产量与剩余油分布预测流程

    Figure  11.  Prediction process of production data and remaining oil distribution based on LSTM neural network

    图  12  基于LSTM神经网络的剩余油饱和度分布预测效果

    Figure  12.  Prediction effect of remaining oil distribution based on LSTM neural network

  • [1] Hassani H, Silva E S. Big Data: a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev, 2018, 42(1): 74 doi: 10.1111/opec.12118
    [2] 李大伟, 熊华平, 石广仁, 等. 基于全球典型油气田数据库的数据挖掘预处理. 大庆石油地质与开发, 2016, 35(1):66 doi: 10.3969/J.ISSN.1000-3754.2016.01.013

    Li D W, Xiong H P, Shi G R, et al. Preprocessing of the data tapping based on global typical oil and gas field database. Pet Geol Oilfield Dev Daqing, 2016, 35(1): 66 doi: 10.3969/J.ISSN.1000-3754.2016.01.013
    [3] Lynch C. How do your data grow? Nature, 2008, 455(7209): 28 doi: 10.1038/455028a
    [4] Los W, Wood J. Dealing with data: Upgrading infrastructure. Science, 2011, 331(6024): 1515
    [5] 李大伟, 石广仁. 油气勘探开发常用数据挖掘算法优选. 石油学报, 2018, 39(2):240 doi: 10.7623/syxb201802013

    Li D W, Shi G R. Optimization of common data mining algorithms for petroleum exploration and development. Acta Pet Sinica, 2018, 39(2): 240 doi: 10.7623/syxb201802013
    [6] 刘伟, 闫娜. 人工智能在石油工程领域应用及影响. 石油科技论坛, 2018, 37(4):32 doi: 10.3969/j.issn.1002-302x.2018.04.006

    Liu W, Yan N. Application and influence of artificial intelligence in petroleum engineering area. Oil Forum, 2018, 37(4): 32 doi: 10.3969/j.issn.1002-302x.2018.04.006
    [7] 林伯韬, 郭建成. 人工智能在石油工业中的应用现状探讨. 石油科学通报, 2019, 4(4):403

    Lin B T, Guo J C. Discussion on current application of artificial intelligence in petroleum industry. Pet Sci Bull, 2019, 4(4): 403
    [8] 曾涛, 张弼弛. 国际油服公司数字化转型经验与启示. 国际石油经济, 2019, 27(7):39 doi: 10.3969/j.issn.1004-7298.2019.07.006

    Zeng T, Zhang B C. Experiences and enlightenments of digital transformation of international oil service companies. Int Petrol Econom, 2019, 27(7): 39 doi: 10.3969/j.issn.1004-7298.2019.07.006
    [9] Bravo C E. Digital transformation for oil & gas production operations: Voice of the Oilfield™ technology[J/OL]. Halliburton Landmark (2018)[2020-07-07]. https://innovationisrael.org.il/sites/default/files/04%20Sebastian%20Kroczka%20Halliburton.pdf
    [10] Bryant R E, Katz R H, Lazowska E D. Big-data computing: creating revolutionary breakthroughs in commerce, science and society[J/OL]. Computing Community Consortium (2008-12-22)[2020-07-07]. http://acrhive2.cra.org/ccc/files/docs/init/Big_Data.pdf
    [11] 孟小峰, 慈祥. 大数据管理: 概念, 技术与挑战. 计算机研究与发展, 2013, 50(1):146 doi: 10.7544/issn1000-1239.2013.20121130

    Meng X F, Ci X. Big data management: concepts, technology and challenges. J Comput Res Dev, 2013, 50(1): 146 doi: 10.7544/issn1000-1239.2013.20121130
    [12] Baaziz A, Quoniam L. How to use big data technologies to optimize operations in upstream petroleum industry. Int J Innov, 2015, 1(1): 19
    [13] Hamzeh H. Application of Big Data in Petroleum Industry[J/OL]. ResearchGate (2016-01-12)[2020-07-07]. https://www.academia.edu/27175616/Application_of_Big_Data_in_Petroleum_Industry_Application_of_Big_Data_in_Petroleum_Industry
    [14] 苏健, 刘合. 石油工程大数据应用的挑战与发展. 中国石油大学学报(社会科学版), 2020, 36(3):1

    Su J, Liu H. Challenges and development of big data application in petroleum engineering. J China Univ Petrol Ed Social Sci, 2020, 36(3): 1
    [15] Jiang J M, Younis R M. Hybrid coupled discrete-fracture/matrix and multicontinuum models for unconventional-reservoir simulation. SPE J, 2016, 21(3): 1009 doi: 10.2118/178430-PA
    [16] Ghassemi A, Pak A. Numerical study of factors influencing relative permeabilities of two immiscible fluids flowing through porous media using lattice Boltzmann method. J Pet Sci Eng, 2011, 77(1): 135 doi: 10.1016/j.petrol.2011.02.007
    [17] Wang S, Javadpour F, Feng Q H. Molecular dynamics simulations of oil transport through inorganic nanopores in shale. Fuel, 2016, 171: 74 doi: 10.1016/j.fuel.2015.12.071
    [18] 廖方宇, 洪学海, 汪洋, 等. 数据与计算平台是驱动当代科学研究发展的重要基础设施. 数据与计算发展前沿, 2019, 1(1):2

    Liao F Y, Hong X H, Wang Y, et al. The data and computing platform is an important infrastructure which drives modern scientific research development. Front Data Comput, 2019, 1(1): 2
    [19] Shvachko K, Kuang H R, Radia S, et al. The Hadoop distributed file system // 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST). Incline Village, 2010: 1
    [20] Vavilapalli V K, Murthy A C, Douglas C, et al. Apache Hadoop YARN: yet another resource negotiator // Proceedings of the 4th Annual Symposium on Cloud Computing. California, 2013: 5
    [21] Moon S, Lee J, Kee Y S. Introducing SSDs to the Hadoop MapReduce framework // 2014 IEEE 7th International Conference on Cloud Computing. Anchorage, 2014: 272
    [22] Zaharia M, Chowdhury M, Franklin M J, et al. Spark: Cluster computing with working sets. HotCloud. 2010: 95
    [23] Toshniwal A, Taneja S, Shukla A, et al. Storm@ twitter // Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. Snowbird, 2014: 147
    [24] da Silva Morais T. Survey on frameworks for distributed computing: Hadoop, spark and storm // Proceedings of the 10th Doctoral Symposium in Informatics Engineering-DSIE'15. Porto, 2015: 95
    [25] 李国欣, 王峰, 皮学军, 等. 非常规油气藏地质工程一体化数据优化应用的思考与建议. 中国石油勘探, 2019, 24(2):147

    Li G X, Wang F, Pi X J, et al. Optimized application of geology-engineering integration data of unconventional oil and gas reservoirs. China Pet Explor, 2019, 24(2): 147
    [26] Hao S, Tang N, Li G L, et al. A novel cost-based model for data repairing. IEEE Trans Knowl Data Eng, 2017, 29(4): 727 doi: 10.1109/TKDE.2016.2637928
    [27] Wang J N, Tang N. Towards dependable data repairing with fixing rules // Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. Snowbird, 2014: 457
    [28] Rekatsinas T, Chu X, Ilyas I F, et al. HoloClean: Holistic data repairs with probabilistic inference. arXiv preprint (2017-02-02)[2020-07-07]. https://arxiv.org/abs/1702.00820
    [29] He J, Veltri E, Santoro D, et al. Interactive and deterministic data cleaning // Proceedings of the 2016 International Conference on Management of Data. San Francisco, 2016: 893
    [30] Aliyuda K, Howell J, Humphrey E. Impact of geological variables in controlling oil-reservoir performance: An insight from a machine-learning technique. SPE Reservoir Eval Eng, 2020, 23(4): 1314 doi: 10.2118/201196-PA
    [31] Ahmadi M A, Bahadori A. A LSSVM approach for determining well placement and conning phenomena in horizontal wells. Fuel, 2015, 153: 276 doi: 10.1016/j.fuel.2015.02.094
    [32] Al-anazi A F, Gates I D. Support-vector regression for permeability prediction in a heterogeneous reservoir: A comparative study. SPE Reservoir Eval Eng, 2010, 13(3): 485 doi: 10.2118/126339-PA
    [33] 石广仁. 支持向量机在裂缝预测及含气性评价应用中的优越性. 石油勘探与开发, 2008, 35(5):588 doi: 10.3321/j.issn:1000-0747.2008.05.010

    Shi G R. Superiorities of support vector machine in fracture prediction and gassiness evaluation. Pet Explor Dev, 2008, 35(5): 588 doi: 10.3321/j.issn:1000-0747.2008.05.010
    [34] El-Sebakhy E A. Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems. Comput Geosci, 2009, 35(9): 1817 doi: 10.1016/j.cageo.2007.10.016
    [35] Patel A N, Davis D, Guthrie C F, et al. Optimizing cyclic steam oil production with genetic algorithms // SPE Western Regional Meeting: Society of Petroleum Engineers. Irvine, 2005: SPE-93906-MS
    [36] 卞晓冰, 张士诚, 张景臣, 等. 压裂投产低—特低渗透油藏井排距设计. 石油勘探与开发, 2015, 42(5):646 doi: 10.1016/S1876-3804(15)30059-8

    Bian X B, Zhang S C, Zhang J C, et al. Well spacing design for low and ultra-low permeability reservoirs developed by hydraulic fracturing. Pet Explor Dev, 2015, 42(5): 646 doi: 10.1016/S1876-3804(15)30059-8
    [37] Siavashi M, Tehrani M R, Nakhaee A. Efficient particle swarm optimization of well placement to enhance oil recovery using a novel streamline-based objective function. J Energy Resour Technol, 2016, 138(5): 052903 doi: 10.1115/1.4032547
    [38] Ding S W, Jiang H Q, Li J J, et al. Optimization of well placement by combination of a modified particle swarm optimization algorithm and quality map method. Comput Geosci, 2014, 18(5): 747 doi: 10.1007/s10596-014-9422-2
    [39] Ahmadi M A, Zendehboudi S, Lohi A, et al. Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization. Geophys Prospect, 2013, 61(3): 582 doi: 10.1111/j.1365-2478.2012.01080.x
    [40] 冉启全, 李士伦, 李元元. 用神经网络模式识别沉积微相. 石油勘探与开发, 1995, 22(2):59 doi: 10.3321/j.issn:1000-0747.1995.02.009

    Ran Q Q, Li S L, Li Y Y. Identification of sedimentary microfacies with an artificial neural network model. Pet Explor Dev, 1995, 22(2): 59 doi: 10.3321/j.issn:1000-0747.1995.02.009
    [41] 吴新根, 葛家理. 应用人工神经网络预测油田产量. 石油勘探与开发, 1994, 21(3):75

    Wu X G, Ge J L. The application of artificial neural network in predicting output of oil fields. Pet Explor Dev, 1994, 21(3): 75
    [42] 刘想平, 汪崎生, 汤军. 用神经网络建立自喷井井底流压预测模型. 石油勘探与开发, 1997, 24(5):92 doi: 10.3321/j.issn:1000-0747.1997.05.023

    Liu X P, Wang Q S, Tang J. An application of neural network in developing a model for predicting flowing bottomhole pressure of flowing wells. Pet Explor Dev, 1997, 24(5): 92 doi: 10.3321/j.issn:1000-0747.1997.05.023
    [43] 宋子齐, 谭成仟, 吴少波, 等. 灰色系统与神经网络技术在水淹层测井评价中的应用. 石油勘探与开发, 1999, 26(3):110

    Song Z Q, Tan C Q, Wu S B, et al. Application of grey system theory and neural network technology to wateredout formation logging evaluation. Pet Explor Dev, 1999, 26(3): 110
    [44] Negash B M, Yaw A D. 基于人工神经网络的注水开发油藏产量预测. 石油勘探与开发, 2020, 47(2):357 doi: 10.1016/S1876-3804(20)60052-0

    Negash B M, Yaw A D. Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection. Pet Explor Dev, 2020, 47(2): 357 doi: 10.1016/S1876-3804(20)60052-0
    [45] Carpenter C. Geology-driven estimated-ultimate-recovery prediction with deep learning. J Pet Technol, 2016, 68(5): 74 doi: 10.2118/0516-0074-JPT
    [46] Korjani M M, Popa A S, Grijalva E, et al. Reservoir characterization using fuzzy kriging and deep learning neural networks // SPE Annual Technical Conference and Exhibition: Society of Petroleum Engineers. Dubai, 2016: 15
    [47] You L J, Tan Q G, Kang Y L, et al. Reconstruction and prediction of capillary pressure curve based on particle swarm optimization-back propagation neural network method. Petroleum, 2018, 4(3): 268 doi: 10.1016/j.petlm.2018.03.004
    [48] 王安辉, 张英魁, 高景龙, 等. 应用人工神经网络方法确定岩石压缩系数. 石油勘探与开发, 2003, 30(4):105 doi: 10.3321/j.issn:1000-0747.2003.04.034

    Wang A H, Zhang Y K, Gao J L, et al. Predicting rock compressibility by artificial neural network. Pet Explor Dev, 2003, 30(4): 105 doi: 10.3321/j.issn:1000-0747.2003.04.034
    [49] Wang S H, Chen Z, Chen S N. Applicability of deep neural networks on production forecasting in Bakken shale reservoirs. J Pet Sci Eng, 2019, 179: 112 doi: 10.1016/j.petrol.2019.04.016
    [50] 李道伦, 刘旭亮, 查文舒, 等. 基于卷积神经网络的径向复合油藏自动试井解释方法. 石油勘探与开发, 2020, 47(3):583

    Li D L, Liu X L, Zha W S, et al. Automatic well test interpretation based on convolutional neural network for a radial composite reservoir. Pet Explor Dev, 2020, 47(3): 583
    [51] Zhu L P, Li H Q, Yang Z G, et al. Intelligent logging lithological interpretation with convolution neural networks. Petrophysics, 2018, 59(6): 799
    [52] Huang L, Dong X S, Clee T E. A scalable deep learning platform for identifying geologic features from seismic attributes. Lead Edge, 2017, 36(3): 249 doi: 10.1190/tle36030249.1
    [53] He Y F, Liu Y L, Shao S, et al. Application of CNN-LSTM in gradual changing fault diagnosis of rod pumping system. Math Problems Eng, 2019, 2019: 4203821
    [54] Wang X, He Y F, Li F J, et al. A working condition diagnosis model of sucker rod pumping wells based on big data deep learning // International Petroleum Technology Conference. Beijing, 2019: 10
    [55] 张东晓, 陈云天, 孟晋. 基于循环神经网络的测井曲线生成方法. 石油勘探与开发, 2018, 45(4):598

    Zhang D X, Chen Y T, Meng J. Synthetic well logs generation via recurrent neural networks. Pet Explor Dev, 2018, 45(4): 598
    [56] Tian C, Horne R N. Recurrent neural networks for permanent downhole gauge data analysis // SPE Annual Technical Conference and Exhibition: Society of Petroleum Engineers. San Antonio, 2017: 12
    [57] Lu L, Zhang G Z, Zhao C. Reservoir thickness forecasting based on deep belief networks // International Geophysical Conference. Qingdao, 2017: 733
    [58] Cao J X, Wu S K. Deep learning: Chance and challenge for deep gas reservoir identification // International Geophysical Conference. Qingdao, 2017: 711
    [59] Carpenter C. Artificial intelligence improves seismic-image reconstruction. J Pet Technol, 2019, 71(10): 65 doi: 10.2118/1019-0065-JPT
    [60] Li Q X, Luo Y N. Using GAN priors for ultrahigh resolution seismic inversion // SEG International Exposition and Annual Meeting. San Antonio, 2019: SEG-2019-3215520
    [61] Zhang H J, Wang W, Wang X K, et al. An implementation of the seismic resolution enhancing network based on GAN // SEG International Exposition and Annual Meeting. San Antonio, 2019: SEG-2019-3216229
    [62] Shi X J, Chen Z R, Wang H, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting // 29th Annual Conference on Neural Information Processing Systems (NIPS 2015). Montreal, 2015: 802
    [63] Xu P, Du R, Zhang Z B. Predicting pipeline leakage in petrochemical system through GAN and LSTM. Knowl-Based Syst, 2019, 175: 50 doi: 10.1016/j.knosys.2019.03.013
    [64] Alakeely A, Horne R N. Simulating the behavior of reservoirs with convolutional and recurrent neural networks. SPE Reservoir Eval Eng, 2020, 23(3): 992 doi: 10.2118/201193-PA
    [65] Lei L, Yu L, Xiong Z, et al. Convolutional recurrent neural networks based waveform classification in seismic facies analysis // SEG International Exposition and Annual Meeting. San Antonio, 2019: SEG-2019-3215237
    [66] Anifowose F, Abdulraheem A. Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. J Nat Gas Sci Eng, 2011, 3(3): 505 doi: 10.1016/j.jngse.2011.05.002
    [67] Amiri M, Ghiasi-Freez J, Golkar B, et al. Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm–A case study. J Pet Sci Eng, 2015, 127: 347 doi: 10.1016/j.petrol.2015.01.013
    [68] Saemi M, Ahmadi M, Varjani A Y. Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng, 2007, 59(1-2): 97 doi: 10.1016/j.petrol.2007.03.007
    [69] LeCun Y, Bengio Y, Hinton G E. Deep learning. Nature, 2015, 521(7553): 436 doi: 10.1038/nature14539
    [70] Wu J L, Yin X L, Xiao H. Seeing permeability from images: fast prediction with convolutional neural networks. Sci Bull, 2018, 63(18): 1215 doi: 10.1016/j.scib.2018.08.006
    [71] Alqahtani N, Armstrong R T, Mostaghimi P. Deep learning convolutional neural networks to predict porous media properties // SPE Asia Pacific Oil and Gas Conference and Exhibition: Society of Petroleum Engineers. Brisbane, 2018: SPE-191906-MS
    [72] Du S Y, Wang R F, Wei C J, et al. The connectivity evaluation among wells in reservoir utilizing machine learning methods. IEEE Access, 2020, 8: 47209 doi: 10.1109/ACCESS.2020.2976910
    [73] Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. arXiv preprint (2015-02-19)[2020-07-07]. https://arxiv.org/abs/1409.2329
    [74] Zhang Q T, Wei C J, Wang Y H, et al. Potential for prediction of water saturation distribution in reservoirs utilizing machine learning methods. Energies, 2019, 12(19): 3597 doi: 10.3390/en12193597
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  • 收稿日期:  2020-07-21
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