Application of BP neural network to sedimentary micro-facies identification

Liu Junling; Wu Xuedong; Wang Dongzhai; He Zhenggang

doi:10.11781/sysydz2014S1052

Volume 36 Issue s1

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > PETROLEUM GEOLOGY & EXPERIMENT > 2014 > 36(s1): 52-55

Fan Ming, Chen Hongyu, Yu Lingjie, Zhang Wentao, Liu Weixin, Bao Yunjie. Evaluation standard of mudstone caprock combining specific surface area and breakthrough pressure[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2011, 33(1): 87-90. doi: 10.11781/sysydz201101087

Citation:

Liu Junling, Wu Xuedong, Wang Dongzhai, He Zhenggang. Application of BP neural network to sedimentary micro-facies identification[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2014, 36(s1): 52-55. doi: 10.11781/sysydz2014S1052

Citation:

PDF( 612 KB)

Application of BP neural network to sedimentary micro-facies identification

doi: 10.11781/sysydz2014S1052

No.3 Tahe Oil Production Plant, SINOPEC Northwest Company, Luntai, Xinjiang 841600, China

Received Date: 2014-10-15
Rev Recd Date: 2014-12-03
Publish Date: 2014-12-28

Abstract

Abstract

A method of sedimentary micro-facies identification based on logging data and BP neural network was proposed in this paper. Through deeply exploring limited logging data, sedimentological sample indexes were gained and the utilization rate of logging data was improved. A series of experiments were conducted in order to find the optimization criterion of the BP artificial neural network and a growing network training method was put forward. Finally, a actual case of net training and micro-facies identification by using sample set and natural samples was analyzed, which showed an accuracy ratio for 83% and realized both high efficiency and precision of micro-facies identification.
- BP neural network,
- further study of logging data,
- growing artificial neural network,
- identification of microfacies

FullText(HTML)

References(20)

References

[1]	靳松,朱筱敏,钟大康.变差函数在沉积微相自动识别中的应用[J].石油学报,2006,27(3):57-60.
[2]	Deutsch C V,Journal,A G. GSLIB:geostatistical software library and user’s guide [M].New York: Oxford University Press,1992:340.
[3]	唐为清,郭荣坤,王忠东,等.沉积微相测井资料神经网络判别方法研究[J].沉积学报,2001,19(4):581-585.
[4]	许少华,刘扬,梁久祯,等.基于遗传—BP算法和图像处理的沉积微相识别[J].石油学报,2002,23 (3):48-51.
[5]	许少华,陈可为,梁久祯,等.基于遗传—BP算法和图像处理的沉积微相识别[J].大庆石油学院学报,2001,25(3):51-54.
[6]	E M Iloghalu. "Application of neural networks technique in lithofacies classifications used for 3-D reservoir geological modelling and exploration studies:a novel computer-based methodology for depositional environment interpretation"// AAPG Annual Convention.Salt Lake City,Utah,May 2003:11-14.
[7]	杨斌,匡立春,孙中春,等.神经网络及其在石油测井中的应用[M].北京:石油工业出版社,2005.
[8]	张昭昭,乔俊飞,杨刚.自适应前馈神经网络结构优化设计[J].智能系统学报,2011,6(4):312-317.
[9]	王金荣,刘洪涛.测井沉积微相识别方法及应用[J].大庆石油学院学报, 2004,28 (4):18-20.
[10]	王楠.测井曲线模式识别及其在地层对比中的应用[D].黑龙江大学硕士论文,2008.
[11]	李君,李少华,毛平,等.VC++结合Fortran升级地质统计学算法[J].物探与化探,2009,33 (6):715-717.
[12]	Carr J R, Miranda F P. The semivariogram in comparison to the co-occurrence matrix for classification of image texture[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(6):1945-1952.
[13]	Funabashi K. On the approximate realization of continues mapping by neural networks[J].Neural Networks,1989,2:183-192.
[14]	Lippmann R P. Review of neural networks for speech recognition[J].Neural Computation,1989,1:1-38.
[15]	Cyberno G. Approximation by superpositions of a sigmoidal function[J].Mathematics of Control,Signals and Systems,1989,2(4):303-314.
[16]	孙鲁平,首皓,赵晓龙,等.基于微电阻率扫描成像测井的沉积微相识别[J].测井技术, 2009,33(4): 379-383.
[17]	周金应,桂碧雯,李茂,等.基于岩控的人工神经网络在渗透率预测中的应用[J].石油学报,2010,31(6):985-988.
[18]	K Aminian, S Ameri. Application of artificial neural networks for reservoir characterization with limited data[J].Journal of Petroleum Science and Engineering, 2005,49:212-222.
[19]	宋延杰,杨艳,杨青山,等.过程神经网络在厚层细分水淹解释中的应用[J].测井技术,2009,33(4) :340-344.
[20]	Fischetti A, Andrade A.Porosity images from well logs[J].Journal of Petroleum Science and Engineering,2002,36:149-158.

Relative Articles

[1]	ZHANG Qingfeng, LI Ziling, ZHANG Jikun, HAO Shuai, SUN Xiaoguang, SHANG Yanjie, ZUO Yun. Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2025, 47(1): 204-212. doi: 10.11781/sysydz2025010204
[2]	YANG Zijie, CHEN Dongxia, WANG Qiaochu, WANG Fuwei, LI Sha, TIAN Ziye, CHEN Shumin, ZHANG Wanrong, YAO Dongsheng, WANG Yuchao. Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(2): 428-440. doi: 10.11781/sysydz202402428
[3]	NING Weike, JU Wei, XIANG Ru. Pressure prediction and genesis analysis of Huangliu Formation reservoir in DF block of Yinggehai Basin based on neural networks[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(5): 1088-1097. doi: 10.11781/sysydz2024051088
[4]	WANG Xiang, MA Jinfeng, WANG Feilong, WANG Zhenliang, CHEN Rongtao, YAN Xinyu. Prediction of organic facies of deep source rocks in southwestern part of Bozhong Sag, Bohai Bay Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2022, 44(6): 1070-1080. doi: 10.11781/sysydz2022061070
[5]	YU Xiaolu, YE Kai, DU Chongjiao, GONG Hanning, MA Zhongliang. Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2021, 43(5): 880-885. doi: 10.11781/sysydz202105880
[6]	MA Yong, ZENG Jianhui, FENG Xiao. Three-dimensional simulation of oil distribution during waterflooding in a micrometer-sized pore network system of tight sandstone[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2020, 42(1): 139-146. doi: 10.11781/sysydz202001139
[7]	Qin Weiqiang, Sun Zhaohui. Logging identification of Ordovician karst cave filling in the Tahe Oil Field[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2015, 37(S1): 1-4. doi: 10.11781/sysydz2015S1001
[8]	Feng Qiong, Wei Shuijian. Well logging identification of gypsum cap rocks in middle-upper Yangtze region[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2011, 33(1): 100-104. doi: 10.11781/sysydz201101100
[9]	Wang Jiayue, Yan Hong, Ao Lide, Qu Hong. STUDY ON THE DISTRIBUTION OF THE MICRO-FRACTURES BY ANN——TAKING THE CA OILFIELD IN GAOYOU SAGIN THE NORTH JIANGSU BASIN AS AN EXAMPLE[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2006, 28(4): 395-398. doi: 10.11781/sysydz200604395
[10]	Zhao Kebin, Sun Changqing. FEATURES OF ANOMALIES RELATED TO RESERVOIR AND ANOMALY PATTERN RECOGNITION IN INTENSIVE OIL AND GAS GEOCHEMICAL EXPLORATION[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2005, 27(5): 539-543. doi: 10.11781/sysydz200505539
[11]	DONG Chun-mei, SONG Chun-gang. STUDY ON THE SEDIMENTARY MICROFACIES OF THE SHA-3 MEMBER IN BONAN OILFIELD[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2003, 25(4): 375-380. doi: 10.11781/sysydz200304375
[12]	ZHOU De-hua, JIAO Fang-zheng, GE Jia-li. ELECTRIC ANALOGY MODELLING EXPERIMENT OF FRACTURE NETWORK RESERVOIR DEVELOPMENT BY HORIZONTAL WELLS[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2003, 25(2): 216-220. doi: 10.11781/sysydz200302216
[13]	TANG Ping. CALCULATION OF RESERVOIR PARAMETERS BY THE NEURAL NETWORK MODEL WITH THE LOGGING DATA OF MULTIPLE WELLS[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2003, 25(4): 413-416. doi: 10.11781/sysydz200304413
[14]	WU Chong-long, LIU Hai-bin, MAO Xiao-ping, LI Shao-hu, WANG Xie-pei, WU JING fu, HE Da-wei, ZHANG Yun-fei, PAN Ming-tai. ARTIFICIAL NEURAL NETWORK SIMULATION ON HYDROCARBON MIGRATION AND ACCUMULATION[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2001, 23(2): 203-212. doi: 10.11781/sysydz200102203
[15]	LIU Yong-jian, SHEN Jun, LIU Yi-jian, WANG Lin. AN ARTIFICIAL NEURAL NETWORK-BASED METHOD FOR EVALUATING THE OIL PROSPECTS OF ROCKS[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2000, 22(3): 276-279. doi: 10.11781/sysydz200003276
[16]	PENG Dunlu, XU Shijin, WANG Rucheng, GUO Yangjun. STUDY ON THE APPLICATION OF THE NEURAL NETWORK EXPERT SYSTEM TO THE PREDICTION OF DAILY OUTPUT PER WELL[J]. PETROLEUM GEOLOGY & EXPERIMENT, 1999, 21(2): 156-160. doi: 10.11781/sysydz199902156
[17]	XIE Congjiao, GUAN Zhenliang. A METHODOLOGICAL APPROACH FOR RESERVOIR HETEROGENEITY CHAR ACTERIZATION OF QIULIN OIL FIELD USING ARTIFICAL NEURAL NETWORKS[J]. PETROLEUM GEOLOGY & EXPERIMENT, 1999, 21(2): 161-165. doi: 10.11781/sysydz199902161
[18]	QIU Liwei, KANG Zhijiang. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO RESERVOIR DESCRIPTION[J]. PETROLEUM GEOLOGY & EXPERIMENT, 1999, 21(2): 166-169. doi: 10.11781/sysydz199902166
[19]	Cai Yudong, Xu Weijie. A SELF-ORGANIZING NERVE-TREE MODEL OF VARIOUS SEIMIC INFORMATION FOR HYDROCARBON PREDICTION[J]. PETROLEUM GEOLOGY & EXPERIMENT, 1996, 18(1): 111-116. doi: 10.11781/sysydz199601111

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Citation

XML

PDF-CN

Article Metrics

Article views (1043) PDF downloads(707)

Application of BP neural network to sedimentary micro-facies identification

doi: 10.11781/sysydz2014S1052

Abstract

References

Relative Articles

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Application of BP neural network to sedimentary micro-facies identification

doi: 10.11781/sysydz2014S1052

Abstract

References

Relative Articles

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content