Petroleum Reservoir Evaluation and Development ›› 2021, Vol. 11 ›› Issue (4): 577-585.doi: 10.13809/j.cnki.cn32-1825/te.2021.04.014
• Intelligent Evaluation • Previous Articles Next Articles
ZHAO Jun1(),ZHANG Tao1,HE Shenglin2,ZHANG Huanrong2,HAN Dong1,TANG Di2
Received:
2020-10-29
Online:
2021-08-26
Published:
2021-08-19
CLC Number:
ZHAO Jun,ZHANG Tao,HE Shenglin,ZHANG Huanrong,HAN Dong,TANG Di. Prediction of reservoir permeability by deep belief network based on optimized parameters[J].Petroleum Reservoir Evaluation and Development, 2021, 11(4): 577-585.
Table 2
Statistic of average relative error of permeability prediction of three models for five wells in study area"
井号 | 深度段 (m) | 常规孔隙度预测模型 (%) | BP神经 网络模型 (%) | 深度置信网络模型(%) |
---|---|---|---|---|
WC1井 | 3 663.3~3 678.9 | 32.70 | 22.30 | 10.60 |
WC2井 | 3 849.7~3 867.7 | 28.40 | 18.40 | 7.20 |
WC3井 | 3 987.8~4 006.1 | 30.30 | 19.20 | 8.14 |
WC4井 | 3 851~3 855.4 | 28.10 | 19.90 | 9.25 |
WC5井 | 3 753.6~3 771.6 | 31.20 | 20.10 | 10.40 |
平均值 | 30.14 | 19.98 | 9.12 |
[1] | 赵久玉, 王付勇, 杨坤. 致密砂岩分形渗透率模型构建及关键分形参数计算方法[J]. 特种油气藏, 2020, 27(4):73-78. |
ZHAO Jiuyu, WANG Fuyong, YANG Kun. Tight sandstone fractal permeability model and key fractal parameter calculation[J]. Special Oil & Gas Reservoirs, 2020, 27(4):73-78. | |
[2] | 程辉, 王付勇, 宰芸, 等. 基于高压压汞和核磁共振的致密砂岩渗透率预测[J]. 岩性油气藏, 2020, 32(3):122-132. |
CHENG Hui, WANG Fuyong, ZAI Yun, et al. Prediction of tight sandstone permeability based on high-pressure mercury intrusion(HPMI)and nuclear magnetic resonance(NMR)[J]. Lithologic Reservoirs, 2020, 32(3):122-132. | |
[3] | 赵天逸, 宁正福, 陈刚, 等. 致密砂岩储集层渗透率预测修正方法[J]. 新疆石油地质, 2020, 41(3):337-343. |
ZHAO Tianyi, NING Zhengfu, CHEN Gang, et al. Modified methods of permeability prediction for tight sandstone reservoirs[J]. Xinjiang Petroleum Geology, 2020, 41(3):337-343. | |
[4] | 张恒荣, 何胜林, 吴进波, 等. 一种基于Kozeny-Carmen方程改进的渗透率预测新方法[J]. 吉林大学学报(地球科学版), 2017, 47(3):899-906. |
ZHANG Hengrong, HE Shenglin, WU Jinbo, et al. A new method for predicting permeability based on modified Kozeny-Carmen[J]. Journal of Jilin University(Earth Science Edition), 2017, 47(3):899-906. | |
[5] | 于华, 令狐松, 王谦, 等. 一种砂岩储层渗透率计算新方法[J]. 西南石油大学学报(自然科学版), 2020, 42(2):125-132. |
YU Hua, LINGHU Song, WANG Qian, et al. A new method for calculating permeability of sandstone reservoir[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(2):125-132. | |
[6] | 张冲. 基于海上砂砾岩低渗透率成因分析及测井评价[J]. 测井技术, 2019, 43(5):524-530. |
ZHANG Chong. Log evaluation of offshore low permeability conglomerate based on permeability genesis analysis[J]. Well Logging Technology, 2019, 43(5):524-530. | |
[7] | 李奇, 高树生, 刘华勋, 等. 岩心渗透率的计算方法与适用范围[J]. 天然气工业, 2015, 35(3):68-73. |
LI Qi, GAO Shusheng, LIU Huaxun, et al. Core permeability calculation methods and application scopes[J]. Natural Gas Industry, 2015, 35(3) : 68-73. | |
[8] | 毛志勇, 黄春娟, 路世昌, 等. 基于APSO-WLS-SVM的含瓦斯煤渗透率预测模型[J]. 煤田地质与勘探, 2019, 47(2):66-71. |
MAO Zhiyong, HUANG Chunjuan, LU Shichang, et al. Model of gas-bearing coal permeability prediction based on APSO-WLS-SVM[J]. Coal Geology & Exploration, 2019, 47(2):66-71. | |
[9] | 邵良杉, 马寒. 煤体瓦斯渗透率的PSO-LSSVM预测模型[J]. 煤田地质与勘探, 2015, 43(4):23-26. |
SHAO Liangshan, MA Han. Model of coal gas permeability prediction based on PSO-LSSVM[J]. Coal Geology & Exploration, 2015, 43(4):23-26. | |
[10] | 古勇. 基于改进支持向量机的煤体瓦斯渗透率预测[J]. 数学的实践与认识, 2016, 46(20):149-155. |
GU Yong. Prediction of coal gas permeability based on PSOSVM[J]. Mathematics in Practice and Theory, 2016, 46(20):149-155. | |
[11] |
KABIRU O A, TAOREED O O, SUNDAY O O, et al. A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir[J]. Journal of Petroleum Science and Engineering, 2017, 150(2):43-53.
doi: 10.1016/j.petrol.2016.11.033 |
[12] | 汪雷, 林亮, 李晶晶, 等. 基于测井信息的煤储层渗透率BP神经网络预测方法[J]. 煤炭科学技术, 2015, 43(7):122-126. |
WANG Lei, LIN Liang, LI Jingjing, et al. Method to predict permeability of coal reservoir with bp neural network based on logging information[J]. Coal Science and Technology, 2015, 43(7):122-126. | |
[13] | 张言辉. 基于物性预测相对渗透率的改进神经网络方法[J]. 天然气与石油, 2020, 38(3):44-49. |
ZHANG Yanhui. Improved neural network method for predicting relative permeability based on physical properties[J]. Natural Gas and Oil, 2020, 38(3):44-49. | |
[14] | 朱林奇, 张冲, 何小菊, 等. 基于改进BPNN与T2全谱的致密砂岩储层渗透率预测[J]. 石油物探, 2017, 56(5):727-734. |
ZHU Linqi, ZHANG Chong, HE Xiaoju, et al. Permeability prediction of tight sandstone reservoir based on improved BPNN and T2 full-spectrum[J]. Geophysical Prospecting for Petroleum, 2017, 56(5):727-734. | |
[15] | 马晟翔, 李希建. 基于因子分析与BP神经网络的煤体瓦斯渗透率预测[J]. 煤矿开采, 2018, 23(6):108-111. |
MA Shengxiang, LI Xijian. Forecast of coal body gas permeability based on factor analysis and BP neural net[J]. Coal Mining Technology, 2018, 23(6):108-111. | |
[16] |
BARAKA M N, CHUANNO S, SOLOMON A O, et al. Prediction of permeability using group method of data handling(GMDH) neural network from well log data[J]. Energies, 2020, 13(6):1-18.
doi: 10.3390/en13010001 |
[17] |
HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
doi: 10.1126/science.1127647 |
[18] | 王俊, 曹俊兴, 尤加春, 等. 基于门控循环单元神经网络的储层孔渗饱参数预测[J]. 石油物探, 2020, 59(4):616-627. |
WANG Jun, CAO Junxing, YOU Jiachun, et al. Prediction of reservoir porosity permeability and saturation based on a gated recurrent unit neural network[J]. Geophysical Prospecting for Petroleum, 2020, 59(4):616-627. | |
[19] |
OLEG S, EVGENY B, DMITRY K. Driving digital rock towards machine learning: predicting permeability with gradient boosting and deep neural networks[J]. Computers and Geosciences, 2019, 127(6):91-98.
doi: 10.1016/j.cageo.2019.02.002 |
[20] | JIAN W T, CHONG C Q, YING F S, et al. Surrogate permeability modelling of low permeable rocks using convolutional neural networks[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 366(13):103-113. |
[21] | 王小艺, 李柳生, 孔建磊, 等. 基于深度置信网络-多类模糊支持向量机的粮食供应链危害物风险预警[J]. 食品科学, 2020, 41(19):17-24. |
WANG Xiaoyi, LI Liusheng, KONG Jianlei, et al. Risk pre-warning of hazardous materials in cereal supply chain based on deep belief network-multiclass fuzzy support vector machine(DBN-MFSVM)[J]. Food Science, 2020, 41(19):17-24. | |
[22] | 孟智慧. 基于深度置信网络的互联网流量预测方法[J]. 电信工程技术与标准化, 2020, 33(10):42-47. |
MENG Zhihui. Internet traffic prediction method based on deep belief network[J]. Telecom Engineering Technics and Standardization, 2020, 33(10):42-47. | |
[23] | 许若冰, 王璇, 赵倩宇, 等. 基于卷积神经网络和深度置信网络的多类型能源需求预测方法[J]. 供用电, 2020, 37(10):65-70. |
XU Ruobing, WANG Xuan, ZHAO Qianyu, et al. A multi-energy demand prediction method based on convolutional neural network and deep belief network[J]. Distribution & Utilization, 2020, 37(10):65-70. | |
[24] | 叶绍泽, 曹俊兴, 吴施楷, 等. 基于深度置信网络的总有机碳含量预测方法[J]. 地球物理学进展, 2018, 33(6):2490-2497. |
YE Shaoze, CAO Junxing, WU Shikai, et al. Prediction method of total organic carbon content based on deep belief nets[J]. Progress in Geophysics, 2018, 33(6):2490-2497. |
[1] | LIANG Yunpei, ZHANG Huaijun, WANG Lichun, QIN Chaozhong, TIAN Jian, CHEN Qiang, SHI Bowen. Numerical simulation of flow fields and permeability evolution in real fractures under continuous loading stress [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(6): 834-843. |
[2] | CHEN Minfeng,QIN Lifeng,ZHAO Kang,WANG Yiwen. Effective injection-production well spacing in pressure-sensitive reservoir with low permeability [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(6): 855-862. |
[3] | ZHENG Gongying, LYU Qibiao, YANG Yongjian, XU Shoucheng. Prestack seismic prediction of sandstone reservoirs in the fifth member of Xujiahe Formation in Dongfengchang area of Ziyang [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 569-580. |
[4] | QIAN Yugui. Application of machine deep learning technology in tight sandstones reservoir prediction: A case study of Xujiahe Formation in Xinchang, western Sichuan Depression [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 600-607. |
[5] | ZHAO Di, MA Sen, CAO Yanhui. Seismic rock physics analysis and prediction model establishment of Shaximiao Formation in Zhongjiang Gas Field [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 608-613. |
[6] | LUO Hongwen, ZHANG Qin, LI Haitao, XIANG Yuxing, LI Ying, PANG Wei, LIU Chang, YU Hao, WANG Yaning. Influence law of temperature profile for horizontal wells in tight oil reservoirs [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 676-685. |
[7] | ZHANG Fengxi, NIU Congcong, ZHANG Yichi. Evaluation of multi-stage fracturing a horizontal well of low permeability reservoirs in East China Sea [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 695-702. |
[8] | HU Zhijian, LI Shuxin, WANG Jianjun, ZHOU Hong, ZHAO Yulong, ZHANG Liehui. Productivity evaluation of multi-stage fracturing horizontal wells in shale gas reservoir with complex artificial fracture occurrence [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(4): 459-466. |
[9] | LIU Honglin,ZHOU Shangwen,LI Xiaobo. Application of PCA plus OPLS method in rapid reserve production rate prediction of shale gas wells [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(4): 474-483. |
[10] | LI Ying, MA Hansong, LI Haitao, GANZER Leonhard, TANG Zheng, LI Ke, LUO Hongwei. Dissolution of supercritical CO2 on carbonate reservoirs [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(3): 288-295. |
[11] | WANG Dianlin, YANG Qiong, WEI Bing, JI Bingxin, XIN Jun, SUN Lin. Effect of betaine surfactant structure on the properties of CO2 foam film [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(3): 313-321. |
[12] | ZHANG Ying,QU Lili,ZHU Lu,ZHANG Yan,HAN Siyang,ZENG Cheng. Application of SVM algorithm in fluid prediction of volcanic reservoirs in Nanpu Sag, Bohai Bay Basin [J]. Reservoir Evaluation and Development, 2023, 13(2): 181-189. |
[13] | ZHANG Jin,TIAN Hongbo,HU Yuxin. Technical countermeasures for deep pumping of highly deviated wells in deep reservoir [J]. Reservoir Evaluation and Development, 2023, 13(2): 247-253. |
[14] | LIANG Honggang,DENG Feng,MA Hongtao,SUN Li,DING Hui,YANG Junying. Application of JI-FI inversion technology to prediction of thin sandstone reservoir: A case study of K1bs 3 upper thin sandstone reservoir in XH area [J]. Petroleum Reservoir Evaluation and Development, 2022, 12(6): 910-917. |
[15] | PAN Yi,ZHAO Qiuxia,SUN Lei,LIU Jiang,WANG Tao,GUO Deming. Prediction model of minimum miscible pressure in CO2 flooding [J]. Petroleum Reservoir Evaluation and Development, 2022, 12(5): 748-753. |
|