地球科学进展 ›› 2020, Vol. 35 ›› Issue (7): 761 -768. doi: 10.11867/j.issn.1001-8166.2020.046

新学科 新技术 新发现 上一篇    

海鸥优化算法在四川盆地渝西区块 H井区页岩气储层最优化测井解释中的应用
陈愿愿 1( ), 杨晓 1, 邓小江 1, 王小兰 1, 何奇 1, 程莉莉 1, 陈科贵 2   
  1. 1.东方地球物理公司西南物探研究院,四川 成都 610213
    2.西南石油大学,四川 成都 610500
  • 收稿日期:2020-03-02 修回日期:2020-05-13 出版日期:2020-08-20
  • 基金资助:
    国家自然科学基金项目“四川盆地油钾兼探的地球物理评价方法研究”(41372103);四川省科技计划项目“四川盆地深层钾盐勘探开发评价研究”(2019YJ0312)

Application of Seagull Optimization Algorithm Log Interpretation to Shale Gas Reservoir of Well H in Sichuan Basin Yuxi Block

Yuanyuan Chen 1( ), Xiao Yang 1, Xiaojiang Deng 1, Xiaolan Wang 1, Qi He 1, Lili Cheng 1, Kegui Chen 2   

  1. 1.Southwest Geophysical Research Institute of BGP CNPC, Chengdu 610213, China
    2.School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
  • Received:2020-03-02 Revised:2020-05-13 Online:2020-08-20 Published:2020-08-20
  • About author:Chen Yuanyuan (1988-), female, Chengdu City, Sichuan Province, Engineer. Research areas include logging interpretation and seismic geology comprehensive research. E-mail: chenyy_wt@cnpc.com.cn
  • Supported by:
    the National Natural Science Foundation of China “Research on the geophysical evaluation method of oil and potassium simultaneous exploration in the Sichuan Basin”(41372103);The Sichuan Science and Technology Plan Project “Research on the evaluation and development of deep potassium salt in the Sichuan Basin”(2019YJ0312)

页岩气储层矿物类型多样,常规的测井解释方法难以建立储层参数体积模型。最优化测井可以有效地评价多矿物组成的复杂岩性油气藏,但该方法的关键是优化算法的选择。利用最新提出的海鸥优化算法对四川盆地渝西区块H井区页岩气储层进行了矿物、物性参数等最优化测井解释,并与遗传算法和遗传—复合形混合算法进行了对比。结果显示,海鸥优化算法的最优化测井解释与岩心分析资料吻合较高、误差小、计算速度较快,弥补了遗传算法过早收敛、容易陷入局部最优以及遗传—复合形混合算法需要二次优化、搜索速度较慢等不足。这为海鸥优化算法在其他地区页岩气储层评价中的应用提供了借鉴。

Due to the diversity of mineral types in shale gas reservoirs, it is difficult to establish reservoir parameter volume model by conventional log interpretation methods. The optimization log interpretation method can evaluate complex lithology reservoirs effectively, and the key is optimization algorithm. With the newly proposed seagull optimization algorithm method, we calculate the mineral and physical parameters of shale gas reservoir in Well H of Yuxi block, Sichuan Basin, and compare with the genetic algorithm and the genetic algorithm-complex hybrid algorithm. It shows that calculation results of seagull optimization algorithm optimization log interpretation match well with core analysis data, and calculation error is small, calculation speed is fast. Seagull optimization algorithm also makes up for the shortcomings of premature convergence and easy to fall into local optimization of genetic algorithm, the need for secondary optimization and slow search speed of genetic-complex hybrid algorithm. It provides a reference for the application of seagull optimization algorithm in other shale gas reservoirs regions.

中图分类号: 

图1 海鸥迁徙和攻击方式示意图
Fig.1 Migration and attacking behaviors of seagulls
图2 海鸥迁徙行为和攻击行为示意图
Fig.2 Movement and attacking behaviors of seagulls
图3 页岩气多矿物组分模型示意图
Fig.3 Multi-mineral composition model of shale gas
图4 SOA测井解释流程图
Fig.4 Log interpretation flowchart of SOA
表1 矿物骨架成分和孔隙流体的测井响应特征值
Table 1 Logging response characteristic values of matrix components and pore fluid
图5 HSOA的测井解释成果图
Fig.5 Log interpretation results of SOA in Well H
表2 H井各优化算法运行时间对比
Table 2 Comparison of operating time of each optimization algorithm in Well H
表3 H井各算法的矿物相对误差统计表
Table 3 Relative errors minerals in algorithms of Well H
1 Dong Dazhong, Wang Yuman, Li Xinjing, et al. Breakthrough and prospect of shale gas exploration and development in China[J]. Natural Gas Industry, 2016, 36( 1): 19- 32.
董大忠, 王玉满, 李新景, 等. 中国页岩气勘探开发新突破及发展前景思考[J]. 天然气工业, 2016, 36( 1): 19- 32.
2 Wang Ruyue, Ding Wenlong, Wang Zhe, et al. Progress of geophysical well logging in shale gas reservoir evaluation [J]. Progress in Geophysics, 2015, 30 ( 1): 228- 241.
王濡岳, 丁文龙, 王哲, 等. 页岩气储层地球物理测井评价研究现状[J]. 地球物理学进展, 2015, 30( 1): 228- 241.
3 Li Shizhen, Jiang Wenli, Wang Qian, et al. Research status and currently existent problems of shale gas geological survey and evaluation in China[J]. Geological Bulletin of China, 2013, 32( 9): 1 440- 1 446.
4 Liu Junhua, Jin Yunzhi, Gong Shiyu. Application of the optimization method to complex lithology reservoir log interpretation[J]. Well Logging Technology, 2008, 32( 6): 542- 561.
刘俊华, 金云智, 龚时雨. 最优化方法在复杂岩性储层测井解释中的应用[J]. 测井技术, 2008, 32( 6): 542- 561.
5 Sondergeld H, Newsham E, Comisky J T, et al. Petrophysical consideration in evaluating and producing shale gas resources[C]// SPE Hydraulic Fracturing Technology Conference, 2010: 441- 449.
6 Sharma K, Chopra S. Unconventional reservoir characterization using conventional tools[C]//SEG Technical Program Expanded Abstracts, 2013, 32: 2 264-2 268.
7 Han Xue, Pan Baozhi, Zhang Yi, et al. GA-Optimal log interpretation applied in Glutenite reservoir evaluation[J]. Well Logging Technology, 2012, 36( 4): 392- 396.
韩雪, 潘保芝, 张意, 等. 遗传最优化算法在砂砾岩储层测井评价中的应用[J]. 测井技术, 2012, 36( 4): 392- 396.
8 Mo Xiuwen, Li Xiao, Zhang Qiang. Application of glowworm swarm optimization algorithm in the loginterpretation for tuffaceous sandstone reservoir[J]. Geophysical Prospecting for Petroleum, 2016, 55( 6): 869- 878.
莫修文, 李晓, 张强. 萤火虫算法在凝灰质砂岩储层测井解释中的应用[J]. 石油物探, 2016, 55( 6): 869- 878.
9 Sun Ruxue, Pan Baozhi, Shi Yujiang, et al. Application of artificial bee colony optimization log interpretation method to tight sandstone reservoir evaluation[J]. Well Logging Technology, 2017, 41( 3): 320- 324.
孙茹雪, 潘保芝, 石玉江, 等. 人工蜂群最优化测井解释方法在致密砂岩储层评价中的应用[J]. 测井技术, 2017, 41( 3): 320- 324.
10 Pan Baozhi, Duan Ya'nan, Zhang Haitao, et al. BFA-CM optimization log interpretation method[J]. Chinese Journal of Geophysics, 2016, 59( 1): 391- 398.
潘保芝, 段亚男, 张海涛, 等. BFA-CM最优化测井解释方法[J]. 地球物理学报, 2016, 59( 1): 391- 398.
11 Sun Ruxue, Pan Baozhi, Duan Yanan, et al. CM and PSO method for evaluating of intermediate-basic volcanic rock mineral components comparison[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2016, 38( 2): 206- 211.
孙茹雪, 潘保芝, 段亚男, 等. CM与PSO方法评价中基性火山岩矿物组份对比[J]. 物探化探计算技术, 2016, 38( 2): 206- 211.
12 Dhiman G, Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems[J]. Knowlege-based Systems, 2019, 165: 169- 196.
13 Han Yi, Xu Zibin, Zhang Liang, et al. A new foreign intelligent optimization algorithm—Seagull Optimization Algorithm[J]. Modern Marketing (Management Edition), 2019, ( 10): 70- 71.
韩毅, 徐梓斌, 张亮, 等. 国外新型智能优化算法——海鸥优化算法[J]. 现代营销:经营版, 2019,( 10): 70- 71.
14 Yong Shihe, Sun Jianmeng. Selection of optimization methods in digital logging processing [J]. Journal of China University of Petroleum, 1988, 12( 4/5): 11- 26.
雍世和, 孙建孟. 测井数字处理中最优化方法的选择[J]. 中国石油大学学报, 1988, 12( 4/5): 11- 26.
15 Cheng Chao, Sang Qin, Yang Shuangding, et al. Application of the optimization log interpretayion method to complex clastic reservoir[J]. Well Logging Technology, 2011, 35( 5): 455- 459.
程超, 桑琴, 杨双定, 等. 最优化测井解释方法在复杂碎屑岩储层中的应用[J]. 测井技术, 2011, 35( 5): 455- 459.
16 Liu Yuxiang, Wang Kailiang, Hu Peiqing, et al. A discussion on measuring methods of shale mineral components [J]. Natural Gas Geoscience, 2015, 26( 9): 1 737- 1 743.
刘玉祥, 王开亮, 胡沛青, 等. 页岩中矿物组分测定方法探讨[J]. 天然气地球科学, 2015, 26( 9): 1 737- 1 743.
17 Wang Ruyue, Ding Wenlong, Wang Zhe, et al. Application of geophysical well logging in shale gas reservoir evaluation [J]. Progress in Geophysics, 2015, 30( 1): 228- 241.
18 Jia Lingyun, Li Lin, Wang Qianyao, et al. Optimization of the rock physical model in tight sandstone reservoir[J]. Advances in Earth Science, 2018, 33( 4): 416- 424.
贾凌云, 李琳, 王千遥, 等. 致密砂岩储层岩石物理模型的优化建立[J]. 地球科学进展, 2018, 33( 4): 416- 424.
19 Qin Ruibao, Yu Jie. Application of multi-mineral treatment method in logging evaluation of shale oil and gas reservoirs in North America [J]. Petroleum Geophysics, 2013, 48( ): 175- 180, 203.
秦瑞宝, 余杰. 多矿物处理方法在北美页岩油气藏测井评价中的应用[J]. 石油地球物理勘探, 2013, 48( ): 175- 180, 203.
20 Tian Yunying, Xia Hongquan. Optimization logging interpretation based on analysis of multimineral model[J]. Journal of Southwest Petroleum Institute, 2006, 28( 4): 8- 11, 101.
田云英, 夏宏泉. 基于多矿物模型分析的最优化测井解释[J]. 西南石油学院学报, 2006, 28( 4): 8- 11, 101.
21 Yu Jie, Qin Ruibao, Liu Chuncheng, et al. Logging evaluation and production "sweet spot" identification of shale play: A case study on Eagle Ford shale play in the USA[J]. China Petroleum Exploration, 2017, 22( 3): 104- 112.
余杰, 秦瑞宝, 刘春成, 等. 页岩气储层测井评价与产量“甜点”识别——以美国鹰潭页岩气储层为例[J]. 中国石油勘探, 2017, 22( 3): 104- 112.
22 Yang Fuqiang, Chen Kegui, Huang Changbing, et al. Application of PSO-LIBSVM in recognition of potassium salt deposits[J]. Advances in Earth Science, 2019, 34( 7): 757- 764.
杨福强, 陈科贵, 黄长兵, 等. PSO-LIBSVM 在钾盐矿层识别中的应用研究[J]. 地球科学进展, 2019, 34( 7): 757- 764.
23 Zhang Zuoqing, Sun Jianmeng. Progress of logging evaluation on shale gas reservoirs[J]. Journal of Oil and Gas Technology, 20l 3, 35( 3): 90- 95.
张作清, 孙建孟. 页岩气测井评价进展[J]. 石油天然气学报, 2013, 35( 3): 90- 95.
[1] 陈愿愿, 杨晓, 邓小江, 王小兰, 何奇, 程莉莉, 陈科贵. 海鸥优化算法在四川盆地渝西区块 H井区页岩气储层最优化测井解释中的应用[J]. 地球科学进展, 2020, 35(7): 750-760.
[2] 李亚龙, 刘先贵, 胡志明, 端祥刚, 张杰, 詹鸿铭. 页岩气水平井产能预测数值模型综述[J]. 地球科学进展, 2020, 35(4): 350-362.
[3] 程超, 于文刚, 贾婉婷, 林海宇, 李莲庆. 岩石热物理性质的研究进展及发展趋势[J]. 地球科学进展, 2017, 32(10): 1072-1083.
[4] 琚宜文, 戚宇, 房立志, 朱洪建, 王国昌, 王桂梁. 中国页岩气的储层类型及其制约因素[J]. 地球科学进展, 2016, 31(8): 782-799.
[5] 徐祖新, 郭少斌. 基于NMR和X-CT的页岩储层孔隙结构研究 *[J]. 地球科学进展, 2014, 29(5): 624-631.
[6] 琚宜文, 卜红玲, 王国昌. 页岩气储层主要特征及其对储层改造的影响[J]. 地球科学进展, 2014, 29(4): 492-506.
[7] 张盼盼, 刘小平, 王雅杰, 孙雪娇. 页岩纳米孔隙研究新进展[J]. 地球科学进展, 2014, 29(11): 1242-1249.
[8] 张雪芬,陆现彩,张林晔,刘庆. 页岩气的赋存形式研究及其石油地质意义[J]. 地球科学进展, 2010, 25(6): 597-604.
阅读次数
全文


摘要