非常规油气井压裂参数智能优化研究进展与发展展望

郭建春, 任文希, 曾凡辉, 罗扬, 李宇麟, 杜肖泱

郭建春,任文希,曾凡辉,等. 非常规油气井压裂参数智能优化研究进展与发展展望[J]. 石油钻探技术,2023, 51(5):1-7. DOI: 10.11911/syztjs.2023097
引用本文: 郭建春,任文希,曾凡辉,等. 非常规油气井压裂参数智能优化研究进展与发展展望[J]. 石油钻探技术,2023, 51(5):1-7. DOI: 10.11911/syztjs.2023097
GUO Jianchun, REN Wenxi, ZENG Fanhui, et al. Unconventional oil and gas well fracturing parameter intelligent optimization: research progress and future development prospects [J]. Petroleum Drilling Techniques,2023, 51(5):1-7. DOI: 10.11911/syztjs.2023097
Citation: GUO Jianchun, REN Wenxi, ZENG Fanhui, et al. Unconventional oil and gas well fracturing parameter intelligent optimization: research progress and future development prospects [J]. Petroleum Drilling Techniques,2023, 51(5):1-7. DOI: 10.11911/syztjs.2023097

非常规油气井压裂参数智能优化研究进展与发展展望

基金项目: 国家自然科学基金面上项目“大数据驱动的深层页岩压裂参数协同优化与实时调控研究”(编号:52374045)、四川省自然科学基金项目“深层页岩储层多簇射孔压裂竞争扩展多目标协同智能优化与调控" (编号:23NSFSC2103)和国家自然科学基金青年基金项目“陆相页岩多重孔隙空间中复杂烃类混合物的赋存机制和相态行为研究”(编号:52004239)联合资助
详细信息
    作者简介:

    郭建春(1970—),男,四川营山人,1992年毕业于西南石油学院采油工程专业,1998年获西南石油学院油气田开发工程专业博士学位,教授,博士生导师,主要从事储层增产改造理论与技术研究工作。系本刊编委。E-mail:guojianchun@vip.163.com。

  • 中图分类号: TE357.1

Unconventional Oil and Gas Well Fracturing Parameter Intelligent Optimization: Research Progress and Future Development Prospects

  • 摘要:

    非常规油气储层具有非均质性强、低孔低渗的特征,非常规油气井需要进行压裂才能投产,与常规油气储层相比,其工程地质条件更为复杂,对传统压裂参数优化方法提出了挑战。人工智能可以为传统方法难以解决的问题提供解决方法,因此,被引入了非常规油气井压裂参数优化。为推动智能压裂理论和技术的快速发展,系统介绍了非常规油气井压裂参数智能优化研究进展情况,主要包括压裂参数优化目标的确定、压裂参数与压裂效果映射关系的建立、最优压裂参数组合的求解,提出非常规油气井压裂参数智能优化主要向基于光纤的井下压裂数据实时采集和传输、物理–数据协同的裂缝扩展–生产动态模拟、压裂参数智能优化及实时调控集成系统等3个方向发展。

    Abstract:

    Unconventional oil and gas reservoirs are characterized by strong heterogeneity, low porosity, and low permeability, and unconventional oil and gas wells need to be fractured to produce. Compared with conventional oil and gas reservoirs, unconventional oil and gas reservoirs have more complex engineering geology condition, which poses a challenge to the traditional fracturing parameter optimization methods. Artificial intelligence can provide solutions to problems that are difficult to solve with traditional methods, so artificial intelligence have been introduced into the optimization of fracturing parameters of unconventional oil and gas wells. In order to promote the rapid development of intelligent fracturing theory and technology, the research progress of intelligent optimization of fracturing parameters for unconventional oil and gas wells was systematically introduced, which mainly including the determination of the optimization objective of fracturing parameters, the establishment of the mapping relationship between fracturing parameters and fracturing effect, and the solution of the optimal fracturing parameter combination, etc. It was also proposed that the intelligent optimization of fracturing parameters for unconventional oil and gas wells should be mainly developed in three directions: real-time acquisition and transmission of downhole fracturing data based on optical fiber, physics-data synergy fracture propagation-production dynamic simulation, as well as intelligent optimization of fracturing parameters and real-time control integrated system.

  • 图  1   Arps模型及其对应的3种递减模式

    Figure  1.   Arps model and its three corresponding decline models

    图  2   某井生产280 d的实际产量与LSTM预测产量[40]

    Figure  2.   Real production and LSTM prediction of a well after producing 280 days[40]

    图  3   物理–数据协同驱动示意[53]

    Figure  3.   Physics-data synergy driven[53]

    图  4   HESS公司的一键式压裂系统[54]

    Figure  4.   “Push-Button” fracturing operation system HESS Company[54]

  • [1] 窦宏恩,张蕾,米兰,等. 人工智能在全球油气工业领域的应用现状与前景展望[J]. 石油钻采工艺,2021,43(4):405–419.

    DOU Hongen, ZHANG Lei, MI Lan, et al. The application status and prospect of artificial intelligence in the global oil and gas in dustry[J]. Oil Drilling & Production Technology, 2021, 43(4): 405–419.

    [2] 盛茂,张家麟,张彦军,等. 基于数据驱动的水平井暂堵压裂有效性评价新模型[J]. 天然气工业,2023,43(9):132–140.

    SHENG Mao, ZHANG Jialin, ZHANG Yanjun, et al. A new data-driven effectiveness evaluation model of temporary plugging fracturing for horizontal wells[J]. Natural Gas Industry, 2023, 43(9): 132–140.

    [3] 李根生,宋先知,祝兆鹏,等. 智能钻完井技术研究进展与前景展望[J]. 石油钻探技术,2023,51(4):35–47.

    LI Gensheng, SONG Xianzhi, ZHU Zhaopeng, et al. Research progress and the prospect of intelligent drilling and completion technologies [J]. Petroleum Drilling Techniques, 2023, 51(4): 35–47.

    [4] 崔奕,汪海阁,丁燕,等. 碳中和愿景下油气钻井的数字化、智能化转型之路[J]. 石油钻采工艺,2022,44(6):769–776.

    CUI Yi, WANG Haige, DING Yan, et al. Routes of digital and intelligent transformation for petroleum drilling with a vision of carbon neutrality[J]. Oil Drilling & Production Technology, 2022, 44(6): 769–776.

    [5] 张世昆,陈作. 人工智能在压裂技术中的应用现状及前景展望[J]. 石油钻探技术,2023,51(1):69–77.

    ZHANG Shikun, CHEN Zuo. Status and prospect of artificial intelligence application in fracturing technology[J]. Petroleum Drilling Techniques, 2023, 51(1): 69–77.

    [6] 葛亮, 滕怡, 肖国清,等. 基于井下环空参数的溢流智能预警技术研究[J]. 西南石油大学学报(自然科学版),2023,45(2):126–134.

    GE Liang, TENG Yi, XIAO Guoqing, et al. Research on over flow intelligent warning technology based on downhole annulus parameters[J]. Journal of Southwest Petroleum University( Science & Technology Edition), 2023, 45(2): 126–134.

    [7] 蒋廷学,周珺,廖璐璐. 国内外智能压裂技术现状及发展趋势[J]. 石油钻探技术,2022,50(3):1–9.

    JIANG Tingxue, ZHOU Jun, LIAO Lulu. Development status and future trends of intelligent fracturing technologies[J]. Petroleum Drilling Techniques, 2022, 50(3): 1–9.

    [8] 王敏生,光新军,耿黎东. 人工智能在钻井工程中的应用现状与发展建议[J]. 石油钻采工艺,2021,43(4):420–427.

    WANG Minsheng, GUANG Xinjun, GENG Lidong. Application status and development suggestions of artificial intelligence in drilling engineering[J]. Oil Drilling & Production Technology, 2021, 43(4): 420–427.

    [9] 杨传书,李昌盛,孙旭东,等. 人工智能钻井技术研究方法及其实践[J]. 石油钻探技术,2021,49(5):7–13.

    YANG Chuanshu, LI Changsheng, SUN Xudong, et al. Research method and practice of artificial intelligence drilling technology[J]. Petroleum Drilling Techniques, 2021, 49(5): 7–13.

    [10] 吴泽兵,谷亚冰,姜雯,等. 基于遗传优化算法的井底钻压智能预测模型[J]. 石油钻采工艺,2023,45(2):151–159.

    WU Zebing, GU Yabing, JIANG Wen, et al. Intelligent prediction models of downhole weight on bit based on genetic optimization algorithm[J]. Oil Drilling & Production Technology, 2023, 45(2): 151–159.

    [11] 孙翰文,费繁旭,高阳,等. 吉木萨尔陆相页岩水平井压裂后产量影响因素分析[J]. 特种油气藏,2020,27(2):108–114.

    SUN Hanwen, FEI Fanxu, GAO Yang, et al. Production sensitivity analysis of fractured horizontal wells in Jimusar continental shale[J]. Special Oil & Gas Reservoirs, 2020, 27(2): 108–114.

    [12] 常青,李青一,赵鹏,等. 镧系金属示踪剂的研制及其在苏里格地区的应用 [J]. 钻井液与完井液,2018,35(3):114–118.

    CHANG Qing, LI Qingyi, ZHAO Peng, et al. Lanthanide series metal tracers: development and application in Sulige Area[J]. Drilling Fluid & Completion Fluid, 2018, 35(3): 114–118.

    [13] 张矿生,唐梅荣,陶亮,等. 庆城油田页岩油水平井压增渗一体化体积压裂技术[J]. 石油钻探技术,2022,50(2):9–15.

    ZHANG Kuangsheng, TANG Meirong, TAO Liang, et al. Horizontal well volumetric fracturing technology integrating fracturing, energy enhancement, and imbibition for shale oil in Qingcheng Oilfield[J]. Petroleum Drilling Techniques, 2022, 50(2): 9–15.

    [14] 习传学,高东伟,陈新安,等. 涪陵页岩气田西南区块压裂改造工艺现场试验[J]. 特种油气藏,2018,25(1):108–114.

    XI Chuanxue, GAO Dongwei, CHEN Xin'an, et al. Field test of fracturing technology in the southwest section of Fuling Shale Gas Field[J]. Special Oil & Gas Reservoirs, 2018, 25(1): 108–114.

    [15] 李宗田,肖勇,李宁,等. 低油价下的页岩油气开发工程技术新进展[J]. 断块油气田,2021,28(5):577–585.

    LI Zongtian,XIAO Yong,LI Ning,et al. New progress in shale oil and gas development engineering technology under low oil prices[J]. Fault-Block Oil & Gas Field, 2021, 28(5): 577–585.

    [16] 刘红磊,周林波,陈作,等. 中国石化页岩气电动压裂技术现状及发展建议[J]. 石油钻探技术,2023,51(1):62–68.

    LIU Honglei, ZHOU Linbo, CHEN Zuo, et al. The up-to-date electric shale gas fracturing technologies of Sinopec and suggestions for further improvements[J]. Petroleum Drilling Technique, 2023, 51(1): 62–68.

    [17]

    REN Wenxi, LI Gensheng, TIAN Shouceng, et al. Adsorption and surface diffusion of supercritical methane in shale[J]. Industrial and Engineering Chemistry Research, 2017, 56(12): 3446–3455. doi: 10.1021/acs.iecr.6b04432

    [18]

    WU Keliu, CHEN Zhangxin, LI Xiangfang. Real gas transport through nanopores of varying cross-section type and shape in shale gas reservoirs[J]. Chemical Engineering Journal, 2015, 281: 813–825. doi: 10.1016/j.cej.2015.07.012

    [19]

    REN Wenxi, LI Gensheng, TIAN Shouceng, et al. An analytical model for real gas flow in shale nanopores with non-circular cross-section[J]. AIChE Journal, 2016, 62(8): 2893–2901. doi: 10.1002/aic.15254

    [20]

    SUN Zheng, LI Xiangfang, SHI Juntai, et al. Apparent permeability model for real gas transport through shale gas reservoirs considering water distribution characteristic [J]. International Journal of Heat and Mass Transfer, 2017, 115(part A): 1008−1019.

    [21]

    REN Wenxi, GUO Jianchun, ZENG Fanghui, et al. Modeling of high-pressure methane adsorption on wet shales[J]. Energy & Fuels, 2019, 33(8): 7043–7051.

    [22]

    LI Jing, WU Keliu, CHEN Zhangxin, et al. Effects of energetic heterogeneity on gas adsorption and gas storage in geologic shale systems[J]. Applied Energy, 2019, 251: 113368. doi: 10.1016/j.apenergy.2019.113368

    [23]

    YANG Feng, NING Zhengfu, ZHANG Rui, et al. Investigations on the methane sorption capacity of marine shales from Sichuan Basin, China[J]. International Journal of Coal Geology, 2015, 146: 104–117. doi: 10.1016/j.coal.2015.05.009

    [24]

    REN Wenxi, TIAN Shouceng, LI Gensheng, et al. Modeling of mixed-gas adsorption on shale using hPC-SAFT-MPTA[J]. Fuel, 2017, 210: 535–544. doi: 10.1016/j.fuel.2017.09.012

    [25]

    YANG Feng, XIE Congjiao, NING Zhengfu, et al. High-pressure methane sorption on dry and moisture-equilibrated shales[J]. Energy & Fuels, 2017, 31(1): 482–492.

    [26] 任文希,周玉,郭建春,等. 适用于中深层—深层页岩气的高压吸附模型[J]. 地球科学,2022,47(5):1865–1875. doi: 10.3321/j.issn.1000-2383.2022.5.dqkx202205023

    REN Wenxi, ZHOU Yu, GUO Jianchun, et al. High-pressure adsorption model for middle–deep and deep shale gas[J]. Earth Science, 2022, 47(5): 1865–1875. doi: 10.3321/j.issn.1000-2383.2022.5.dqkx202205023

    [27]

    ARPS J J. Analysis of decline curves[J]. Transactions of the AIME, 1945, 160(1): 228–247. doi: 10.2118/945228-G

    [28]

    ILK D, RUSHING J A, PEREGO A D, et al. Exponential vs. hyperbolic decline in tight gas sands: understanding the origin and implications for reserve estimates using Arps’ decline curves[R]. SPE 116731, 2008.

    [29]

    DUONG A N. An unconventional rate decline approach for tight and fracture-dominated gas wells[R]. SPE 137748, 2010.

    [30]

    CLARK A J, LAKE L W, PATZEK T W. Production forecasting with logistic growth models[R]. SPE 144790, 2011.

    [31]

    TAN Lei, ZUO Lihua, WANG Binbin. Methods of decline curve analysis for shale gas reservoirs[J]. Energies, 2018, 11(3): 552. doi: 10.3390/en11030552

    [32]

    ZUO Lihua, YU Wei, WU Kan. A fractional decline curve analysis model for shale gas reservoirs[J]. International Journal of Coal Geology, 2016, 163: 140–148. doi: 10.1016/j.coal.2016.07.006

    [33]

    YU Shaoyong, LEE W J, MIOCEVIC D J, et al. Estimating proved reserves in tight/shale wells using the modified SEPD method[R]. SPE 166198, 2013.

    [34]

    REN Wenxi, LAU H C. Analytical modeling and probabilistic evaluation of gas production from a hydraulically fractured shale reservoir using a quad-linear flow model[J]. Journal of Petroleum Science and Engineering, 2020, 184: 106516. doi: 10.1016/j.petrol.2019.106516

    [35]

    REN Wenxi, LAU H C. New rate-transient analysis for fractured shale gas wells using a tri-linear flow model[J]. Journal of Natural Gas Science and Engineering, 2020, 80: 103368. doi: 10.1016/j.jngse.2020.103368

    [36]

    SONG Xuanyi, LIU Yuetian, XUE Liang, et al. Time-series well performance prediction based on long short-term memory (LSTM) neural network model[J]. Journal of Petroleum Science and Engineering, 2020, 186: 106682. doi: 10.1016/j.petrol.2019.106682

    [37]

    CHEN Xianchao, LI Jiang, GAO Ping, et al. Prediction of shale gas horizontal wells productivity after volume fracturing using machine learning–an LSTM approach[J]. Petroleum Science and Technology, 2022, 40(15): 1861–1877. doi: 10.1080/10916466.2022.2032739

    [38]

    LEE K, LIM J, YOON D, et al. Prediction of shale-gas production at Duvernay Formation using deep-learning algorithm[J]. SPE Journal, 2019, 24(6): 2423–2437. doi: 10.2118/195698-PA

    [39]

    KOCOGLU Y, GORELL S, MCELROY P. Application of Bayesian optimized deep Bi-LSTM neural networks for production forecasting of gas wells in unconventional shale gas reservoirs[R]. URTEC-2021-5418-MS, 2021. .

    [40]

    YANG Run, LIU Xiangui, YU Rongze, et al. Long short-term memory suggests a model for predicting shale gas production[J]. Applied Energy, 2022, 322: 119415. doi: 10.1016/j.apenergy.2022.119415

    [41]

    WANG Tianyu, WANG Qisheng, SHI Jing, et al. Productivity prediction of fractured horizontal well in shale gas reservoirs with machine learning algorithms[J]. Applied Sciences, 2021, 11(24): 12064. doi: 10.3390/app112412064

    [42]

    BEN TAIEB S, BONTEMPI G, ATIYA A F, et al. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition[J]. Expert Systems with Applications, 2012, 39(8): 7067–7083. doi: 10.1016/j.eswa.2012.01.039

    [43]

    TADJER A, HONG A, BRATVOLD R B. Machine learning based decline curve analysis for short-term oil production forecast[J]. Energy Exploration & Exploitation, 2021, 39(5): 1747–1769.

    [44] 李丽哲,周福建,王博. 水平井多级压裂高维参数智能优化方法研究[J]. 石油科学通报,2023,8(3):347–359. doi: 10.3969/j.issn.2096-1693.2023.03.025

    LI Lizhe, ZHOU Fujian, WANG Bo. Method investigation on intelligent optimization of high dimension HWMHF parameters[J]. Petroleum Science Bulletin, 2023, 8(3): 347–359. doi: 10.3969/j.issn.2096-1693.2023.03.025

    [45]

    DU Yihe, LIU Hualin, SUN Yuping, et al. An improved integrated numerical simulation method to study main controlling factors of EUR and optimization of development strategy[J]. Energies, 2023, 16(4): 2011. doi: 10.3390/en16042011

    [46]

    RAHMANIFARD H, ALIMOHAMADI H, GATES I. Well performance prediction in Montney Formation using machine learning approaches[R]. URTEC-2020-2465-MS, 2020.

    [47]

    LI Dongshuang, YOU Shaohua, LIAO Qinzhuo, et al. Prediction of shale gas production by hydraulic fracturing in Changning Area using machine learning algorithms[J]. Transport in Porous Media, 2023, 149(1): 373–388. doi: 10.1007/s11242-023-01935-3

    [48]

    MORADIDOWLATABAD M, JAMIOLAHMADY M. The performance evaluation and design optimisation of multiple fractured horizontal wells in tight reservoirs[J]. Journal of Natural Gas Science and Engineering, 2018, 49: 19–31. doi: 10.1016/j.jngse.2017.10.011

    [49]

    RAHMANIFARD H, PLAKSINA T. Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs[J]. Journal of Natural Gas Science and Engineering, 2018, 52: 367–378. doi: 10.1016/j.jngse.2018.01.047

    [50]

    YAO Jun, LI Zhihao, LIU Lijun, et al. Optimization of fracturing parameters by modified variable-length particle-swarm optimization in shale-gas reservoir[J]. SPE Journal, 2021, 26(2): 1032–1049. doi: 10.2118/205023-PA

    [51] 隋微波,温长云,孙文常,等. 水力压裂分布式光纤传感联合监测技术研究进展[J]. 天然气工业,2023,43(2):87–103.

    SUI Weibo, WEN Changyun, SUN Wenchang, et al. Joint application of distributed optical fiber sensing technologies for hydraulic fracturing monitoring[J]. Natural Gas Industry, 2023, 43(2): 87–103.

    [52]

    ZHAO Yu, BESSA F, SAHNI V, et al. Key learnings from hydraulic fracturing test site-2 (HFTS-2), Delaware Basin[R]. URTEC-2021-5229-MS, 2021.

    [53]

    LIU Huihai, ZHANG Jilin, LIANG Feng, et al. Incorporation of physics into machine learning for production prediction from unconventional reservoirs: a brief review of the gray-box approach[J]. SPE Reservoir Evaluation & Engineering, 2021, 24(4): 847–858.

    [54]

    BUTLER E, PERTUSO D, HUA G, et al. Automated hydraulic fracturing integrated with predictive machine learning[R]. SPE 209165, 2022.

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  • 收稿日期:  2023-09-03
  • 网络出版日期:  2023-09-20
  • 刊出日期:  2023-10-30

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