Big data intelligent platform and application analysis for oil and gas resource development
-
摘要: 油氣資源大數據智能平臺的總體框架應以數據資源為基礎、大數據平臺算力為支撐、人工智能算法為核心，面向油氣行業生產需求，構建集勘探、開發、生產數據于一體的油氣數據資源池，通過數據清洗與融合提升數據質量，整合物理模擬與數據挖掘等手段，實現服務功能模塊化，并在PC端、管控大屏、手機移動APP等多維平臺實現智能監測、預警與展示。通過對深度學習等人工智能方法在油氣工業領域的應用案例分析，表明其具有較好的應用前景。未來石油公司應與科研院所通力合作，挖掘石油工業數據的巨大潛能，實現降本增效，建設全新的智能油氣工業生態圈，完成產業升級。Abstract: With the rapid improvement of exploration and monitoring technologies, the oil and gas industry has accumulated a large amount of data in the fields of seismic exploration, logging, production, and development. How to transform the huge “data resources” into “data assets” and fully utilize data and tap their real value to better serve society is a main concern in the oil and gas industry today. Therefore, the oil industry needs to complete the industrial upgrading of “Smart Oilfield” through digital and intelligent transformation. In recent years, the rise of big data technology and artificial intelligence have allowed international oil companies and oil service giants to accelerate the construction of digital and intelligent oil fields. The overall framework of the big data intelligent platform of oil and gas resources should be based on data resources with big data platform computing power as the support and artificial intelligence algorithms as the core. To meet the production needs of the oil and gas industry, it is of great urgency to build an oil and gas data resource pool that integrates exploration, development, and production data. The data quality can be improved via data cleaning and fusion. Physical simulations, data mining, and other approaches should be combined to achieve the modularization of service functions. Additionally, the goals of intelligent monitoring, early warning, and display on multi-dimensional platforms such as PC, control screen, and mobile apps can also be achieved. The analysis of artificial intelligence methods such as deep learning in the context of the oil and gas industry shows that these methods have good application prospects. In the future, oil companies should work together with scientific research institutes to tap the huge potential of oil industry data, achieve cost reduction and efficiency increase, and build a new smart oil and gas industrial ecosystem to complete industrial upgrading.
-
Key words:
- petroleum /
- natural gas /
- big data /
- artificial intelligence /
- deep learning /
- machine learning
-
圖 1 SPE-one petro（美國石油工程師協會）數據庫中機器學習相關文章增長圖
Figure 1. Graph depicting the increase in the number of machine learning-related articles in SPE-OnePetro
圖 2 國內外油氣大數據智能平臺構建實例圖
Figure 2. Construction and example of the intelligent platform for domestic and foreign oil and gas big data
圖 3 國內外油氣工業數字化轉型發展歷程
Figure 3. Development process of the digital transformation of the oil and gas industry at home and abroad
圖 5 油氣大數據智能平臺基本流程與總體框架
Figure 5. Basic process and overall framework of oil and gas big data intelligent platform
圖 6 油氣大數據智能平臺Hadoop、Spark及Storm混合存儲計算架構
Figure 6. Oil and gas big data intelligent platform with Hadoop, Spark, and Storm hybrid storage computing architecture
圖 7 油氣工業多源異構數據體的清洗融合
Figure 7. Cleaning and fusion of multi-source data in the oil and gas industry
圖 8 油氣行業常用人工智能算法
Figure 8. Artificial intelligence algorithms commonly used in the oil and gas industry
圖 9 基于卷積神經網絡的儲層物性預測流程
Figure 9. Reservoir property prediction process based on convolutional neural network
圖 11 基于LSTM神經網絡的產量與剩余油分布預測流程
Figure 11. Prediction process of production data and remaining oil distribution based on LSTM neural network
圖 12 基于LSTM神經網絡的剩余油飽和度分布預測效果
Figure 12. Prediction effect of remaining oil distribution based on LSTM neural network
259luxu-164<th id="5nh9l"></th> <strike id="5nh9l"></strike> <th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th> <strike id="5nh9l"></strike> <th id="5nh9l"><noframes id="5nh9l"> <th id="5nh9l"></th> <strike id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span> <span id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span> <strike id="5nh9l"><noframes id="5nh9l"><strike id="5nh9l"></strike> <span id="5nh9l"><noframes id="5nh9l"> <span id="5nh9l"><noframes id="5nh9l"> <span id="5nh9l"></span> <span id="5nh9l"><video id="5nh9l"></video></span> <th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th> -
參考文獻
[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] 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李大偉, 熊華平, 石廣仁, 等. 基于全球典型油氣田數據庫的數據挖掘預處理. 大慶石油地質與開發, 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] 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李大偉, 石廣仁. 油氣勘探開發常用數據挖掘算法優選. 石油學報, 2018, 39(2):240 doi: 10.7623/syxb201802013 [6] 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劉偉, 閆娜. 人工智能在石油工程領域應用及影響. 石油科技論壇, 2018, 37(4):32 doi: 10.3969/j.issn.1002-302x.2018.04.006 [7] Lin B T, Guo J C. Discussion on current application of artificial intelligence in petroleum industry. Pet Sci Bull, 2019, 4(4): 403林伯韜, 郭建成. 人工智能在石油工業中的應用現狀探討. 石油科學通報, 2019, 4(4):403 [8] 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曾濤, 張弼弛. 國際油服公司數字化轉型經驗與啟示. 國際石油經濟, 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] 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孟小峰, 慈祥. 大數據管理: 概念, 技術與挑戰. 計算機研究與發展, 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] 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蘇健, 劉合. 石油工程大數據應用的挑戰與發展. 中國石油大學學報(社會科學版), 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] 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廖方宇, 洪學海, 汪洋, 等. 數據與計算平臺是驅動當代科學研究發展的重要基礎設施. 數據與計算發展前沿, 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] 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李國欣, 王峰, 皮學軍, 等. 非常規油氣藏地質工程一體化數據優化應用的思考與建議. 中國石油勘探, 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] 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石廣仁. 支持向量機在裂縫預測及含氣性評價應用中的優越性. 石油勘探與開發, 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] 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卞曉冰, 張士誠, 張景臣, 等. 壓裂投產低—特低滲透油藏井排距設計. 石油勘探與開發, 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] 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冉啟全, 李士倫, 李元元. 用神經網絡模式識別沉積微相. 石油勘探與開發, 1995, 22(2):59 doi: 10.3321/j.issn:1000-0747.1995.02.009 [41] 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吳新根, 葛家理. 應用人工神經網絡預測油田產量. 石油勘探與開發, 1994, 21(3):75 [42] 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劉想平, 汪崎生, 湯軍. 用神經網絡建立自噴井井底流壓預測模型. 石油勘探與開發, 1997, 24(5):92 doi: 10.3321/j.issn:1000-0747.1997.05.023 [43] 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宋子齊, 譚成仟, 吳少波, 等. 灰色系統與神經網絡技術在水淹層測井評價中的應用. 石油勘探與開發, 1999, 26(3):110 [44] 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)60052-0Negash B M, Yaw A D. 基于人工神經網絡的注水開發油藏產量預測. 石油勘探與開發, 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] 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王安輝, 張英魁, 高景龍, 等. 應用人工神經網絡方法確定巖石壓縮系數. 石油勘探與開發, 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] 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李道倫, 劉旭亮, 查文舒, 等. 基于卷積神經網絡的徑向復合油藏自動試井解釋方法. 石油勘探與開發, 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] Zhang D X, Chen Y T, Meng J. Synthetic well logs generation via recurrent neural networks. Pet Explor Dev, 2018, 45(4): 598張東曉, 陳云天, 孟晉. 基于循環神經網絡的測井曲線生成方法. 石油勘探與開發, 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 -
下載:
點擊查看大圖
圖(12)
計量
- 文章訪問數: 3132
- HTML全文瀏覽量: 1364
- PDF下載量: 270
- 被引次數: 0
