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基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例

万里明 吴均 卢军凯 刘彝 陈勉

万里明, 吴均, 卢军凯, 刘彝, 陈勉. 基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例[J]. 地质科技通报, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
引用本文: 万里明, 吴均, 卢军凯, 刘彝, 陈勉. 基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例[J]. 地质科技通报, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
Wan Liming, Wu Jun, Lu Junkai, Liu Yi, Chen Mian. Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
Citation: Wan Liming, Wu Jun, Lu Junkai, Liu Yi, Chen Mian. Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210

基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例

doi: 10.19509/j.cnki.dzkq.2020.0210
基金项目: 

国家自然科学基金联合基金项目“超深井井筒安全构建工程基础理论与方法” U1762215

国家自然科学基金联合基金项目“高温高压油气安全高效钻完井工程基础理论与方法 U19B6003-05

国家十三五自然科学基金项目“丛式井缝网构建理论与控制技术” 2017ZX05009

详细信息
    作者简介:

    万里明(1995—),男,现正攻读油气井工程专业硕士学位,主要从事石油工程岩石力学与压裂方面研究工作。E-mail:18811331063@163.com

  • 中图分类号: P588.21+2.3

Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag

  • 摘要: 致密砂岩储层脆性评价对于“甜点”区预测和压裂改造都有重要作用。针对目前脆性评价力学机理不足、脆性矿物组分分析准确性不高的问题,提出了一种考虑岩石力学性质、脆性矿物组分和岩石成熟度的Adam-神经网络脆性综合评价方法。根据南堡凹陷高北边坡27块岩样的三轴力学实验结果,分析了岩石应力-应变曲线和破坏形态得出Rickman脆性指数,根据全岩矿物X-衍射实验分析得到反映成熟度的黏土矿物和反映脆性组分的非黏土矿物的含量,然后以反映力学性质的Rickman脆性指数为目标函数,以黏土矿物和非黏土矿物含量为训练参数,通过改进的Adam算法建立神经网络脆性评价模型,最后用测井曲线验证模型的准确性。研究表明,该地区脆性矿物以石英、长石为主,中等脆性程度,岩石区域各向异性较强,测井动态力学参数计算的脆性指数与模型相吻合。该Adam-神经网络算法结合力学、地质和矿物学因素, 可以快速得到更加准确的区域脆性指数,对指导选井选层,压裂施工都有很好的指导意义。

     

  • 图 1  南堡凹陷北部区域构造单元划分图[21]

    Figure 1.  Tectonic unit zoning of the north region of Nanpu Sag

    图 2  南堡凹陷12块岩心应力应变曲线

    Figure 2.  Stress-strain curves of 12 specimens of Nanpu Sag

    图 3  27块岩心的Rickman脆性指数结果

    Figure 3.  The Rickman brittleness index of 27 specimens

    图 4  南堡凹陷致密砂岩典型破坏形式

    Figure 4.  Sketchs of typical failure mode of tight sandstone in Nanpu Sag

    图 5  南堡凹陷27块岩样矿物组成

    Figure 5.  Relative contents of mineral of 27 specimens in Nanpu Sag

    图 6  Adam-神经网络脆性评价示意图

    Figure 6.  Schematic diagram of Adam-neural network brittleness evaluation

    图 7  岩石矿物组分相关性分析(横坐标为相关系数)

    Figure 7.  The correlation analysis of the rock mineral composition

    图 8  脆性评价的Adam-神经网络结构示意图(w、b分别为权重和阈值)

    Figure 8.  The schematic diagram of Adam-neural network for brittleness evaluation

    图 9  神经网络不同学习率收敛图

    Figure 9.  Convergence curves of different learning rates in neural network

    图 10  神经网络不同算法收敛图

    Figure 10.  Convergence curves of different algorithms in neural network

    图 11  脆性指数神经网络测试集结果误差分析

    Figure 11.  Comparison diagram of brittleness index between prodection value and true value

    图 12  南堡凹陷高5区块64井测井曲线脆性预测对比图

    Figure 12.  Brittleness prediction and comparsion diagram from Well 64 logging curve in of Block Gao 5 Nanpu Sag

  • [1] Chong K K, Grieser W V, Passman A, et al.A completions guide book to shale-play development: A review of successful approaches toward shale-play stimulation in the last two decades[C]//Anon.Canadian Unconventional Resources and International Petroleum Conference.[S.l.], Canadia: Society of Petroleum Engineers, 2010.
    [2] Holt R M, Fjær E, Stenebråten J F, et al.Brittleness of shales: Relevance to borehole collapse and hydraulic fracturing[J].Journal of Petroleum Science and Engineering, 2015, 131:200-209. doi: 10.1016/j.petrol.2015.04.006
    [3] 付永强, 马发明, 曾立新, 等.页岩气藏储层压裂实验评价关键技术[J].天然气工业, 2011, 31(4):51-54, 127. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=trqgy201104012
    [4] Zhang D, Ranjith P, Perera M.The brittleness indices used in rock mechanics and their application in shale hydraulic fracturing: A review[J].Journal of Petroleum Science and Engineering, 2016, 143:158-170. doi: 10.1016/j.petrol.2016.02.011
    [5] Hucka V, Das B.Brittleness determination of rocks by different methods[J].International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1974, 11(10):389-392. http://www.sciencedirect.com/science/article/pii/0148906274911097
    [6] Lawn B, Marshall D.Hardness, toughness, and brittleness: An indentation analysis[J].Journal of the American Ceramic Society, 1979, 62(7/8):347-350. http://www.researchgate.net/publication/249461401_Hardness_Toughness_and_Brittleness_An_Indentation_Analysis
    [7] Rickman R, ullen M J, Petre J E, et al.A practical use of shale petrophysics for stimulation design optimization: All shale plays are not clones of the Barnett Shale[C]//SPE Annual Technical Conference and Exhibition.Denver: Society of Petroleum Engineers, 2008.
    [8] Li Qinghui, Chen Mian, Jin Yan, et al.Rock mechanical properties and brittleness evaluation of shale gas reservoir[J].Petroleum Drilling Techniques, 2012, 40(4):17-22. http://www.researchgate.net/publication/281021128_Rock_mechanical_properties_and_brittleness_evaluation_of_shale_gas_reservoir
    [9] Jarvie D M, Hill R J, Pollastro R M.Assessmentof the gas potential and yields from shales: The Barnett shale model[J].Oklahoma Geological Survey Circular, 2005, 110:37-50. http://www.researchgate.net/publication/311569482_Assessment_of_the_gas_potential_and_yields_from_shales_The_Barnett_Shale_model
    [10] Hou Bing, Zeng Yijin, Fan Meng, et al.Brittleness evaluation of shale based on the brazilian splitting test[J].Geofluids, 2018, 12:69-79. http://www.researchgate.net/publication/324565363_Brittleness_Evaluation_of_Shale_Based_on_the_Brazilian_Splitting_Test
    [11] Deng C, Tang D, Liu S, et al.Characterization of mineral composition and its influence on microstructure and sorption capacity of coal[J].Journal of Natural Gas Science and Engineering, 2015, 25:46-57. doi: 10.1016/j.jngse.2015.04.034
    [12] 李庆辉, 陈勉, Wang F P, 等.工程因素对页岩气产量的影响:以北美Haynesville页岩气藏为例[J].天然气工业, 2012, 32(4):54-59, 123. http://d.wanfangdata.com.cn/Periodical/trqgy201204013
    [13] 陈昀, 金衍, 陈勉.基于能量耗散的岩石脆性评价方法[J].力学学报, 2015, 47(6):984-993. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=lxxb201506010
    [14] 郝宪杰, 袁亮, 郭延定, 等.考虑峰后能量非稳态释放的硬煤脆性度指标[J].岩石力学与工程学报, 2017, 36(11):2641-2649. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711004
    [15] Meng F, Zhou H, Zhang C, et al.Evaluation methodology of brittleness of rock based on post-peak stress-strain curves[J].Rock Mechanics and Rock Engineering, 2015, 48(5):1787-1805. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=88edfd2167d75562dc4e66f1554326aa
    [16] Zhang B, Zhao T, Jin X, et al.Brittleness evaluation of resource plays by integrating petrophysical and seismic data analysis[J].Interpretation, 2015, 3(2):81-92. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=87aea56d3e2fd3b2a9353ad9668b1248
    [17] 杨宝刚, 潘仁芳, 赵丹, 等.四川盆地长宁示范区龙马溪组页岩岩石力学特性及脆性评价[J].地质科技情报, 2015, 34(4):183-188. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201504027
    [18] Kinga D, Adam J B.A method for stochastic optimization[C]//Anon.International Conference on Learning Representations.SanDego: ICLR, 2015.
    [19] 曹中宏, 杨晓利, 康海军.南堡凹陷北部沙三段岩性圈闭识别与评价[J].石油天然气学报, 2012, 34(3):80-83, 166. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jhsyxyxb201203016
    [20] 柯友亮, 郝杰, 王华, 等.基于叠后地震数据的南堡凹陷高南斜坡带三角洲扇体识别及演化特征[J].地质科技情报, 2019, 38(2):89-100, 303. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201902011
    [21] 李宏义, 姜振学, 董月霞, 等.渤海湾盆地南堡凹陷断层对油气运聚的控制作用[J].现代地质, 2010, 24(4):755-761. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xddz201004015
    [22] 周喻, 刘冰, 王莉, 等.单轴压缩条件下含双圆孔类岩石试样力学特性的细观研究[J].岩石力学与工程学报, 2017, 36(11):2662-2671. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711006
    [23] 赖锦, 王贵文, 王书南, 等.碎屑岩储层成岩相测井识别方法综述及研究进展[J].中南大学学报:自然科学版, 2013, 44(12):4942-4953. http://www.cqvip.com/QK/90745B/201312/48691412.html
    [24] Guthrie J M, Houseknecht D W, Johns W D.Relationships amongvitrinite reflectance, illite crystallinity, and organic geochemistry in Carboniferous strata, Ouachita Mountains, Oklahoma and Arkansas[J].AAPG Bulletin, 1986, 70(1):26-33. http://www.mendeley.com/research/relationships-among-vitrinite-reflectance-illite-crystallinity-organic-geochemistry-carboniferous-st/
    [25] Modica C, Lapierre P.Estimation of kerogen porosity in source rocks as a function of thermal transformation: Example from the Mowry Shale in the Powder River Basin of Wyoming[J].AAPG Bulletin, 2012, 96(1):87-108. doi: 10.1306/04111110201
    [26] Huang G B, Babri H A.Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions[J].IEEE Transactions on Neural Networks, 1998, 9(1):224-229. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ee260ec22503a6713ce4bbfd51b6a558
    [27] Gomez C.Exact solution of the Bogoyavlenskii equation using the improved Tanh-Coth method[J].Applied Mathematical Sciences, 2015, 9:4443-4447. doi: 10.12988/ams.2015.55377
    [28] Abadi M, arham P, Chen J, et al.Tensor Flow: A system for large-scale machine learning[C]//Anon.OSDI.Savannah: [s.n.], 2016: 265-283.
    [29] Duchi J, Hazan E, Singer Y.Adaptive subgradient methods for online learning and stochastic optimization[J].Journal of Machine Learning Research, 2011, 12:2121-2159. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9cc8f19eb6a700c641d083c837fb376b
    [30] Najibi A R, Ghafoori M, Lashkaripour G R, et al.Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran[J].Journal of Petroleum Science and Engineering, 2015, 126:78-82. doi: 10.1016/j.petrol.2014.12.010
    [31] Yang S, Harris N B, Dong T, et al.Natural fractures and mechanical properties in a horn river shale core from well logs and hardness measurements[J].SPE Reservoir Evaluation & Engineering, 2018, 21(3):671-682.
    [32] 赵彦德, 刘洛夫, 王旭东, 等.渤海湾盆地南堡凹陷古近系烃源岩有机相特征[J].中国石油大学学报:自然科学版, 2009, 3(5):23-29. http://d.wanfangdata.com.cn/Periodical/sydxxb200905005
    [33] 万涛, 蒋有录, 董月霞, 等.渤海湾盆地南堡凹陷油气运移路径模拟及示踪[J].地球科学:中国地质大学学报, 2013, 38(1):173-179. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqkx201301017
    [34] Yang Shen, Yi Yuanyuan, Lei Zhengdong, et al.Improving predictability of stimulated reservoir volume from different geological perspectives[J].Marine and Petroleum Geology, 2018, 95:219-227. doi: 10.1016/j.marpetgeo.2018.04.018
    [35] Hou Bing, Zhang Ruxin, Tan Peng, et al.Characteristics of fracture propagation in compact limestone formation by hydraulic fracturing in central Sichuan, China[J].Journal of Natural Gas Science and Engineering, 2018, 57: 122-134. doi: 10.1016/j.jngse.2018.06.035
    [36] Zhang F, Mack M.Integrating fully coupled geomechanical modeling with microsesmicity for the analysis of refracturing treatment[J].Journal of Natural Gas Science and Engineering, 2017, 46:16-25. doi: 10.1016/j.jngse.2017.07.008
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  • 收稿日期:  2019-01-13

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