Xinjiang Petroleum Geology ›› 2023, Vol. 44 ›› Issue (2): 231-237.doi: 10.7657/XJPG20230214
• APPLICATION OF TECHNOLOGY • Previous Articles Next Articles
CHEN Yumao1(), ZHAO Hu2, YANG Hongwei1, WEI Guohua1, LUO Pingping1
Received:
2022-03-21
Revised:
2022-07-28
Online:
2023-04-01
Published:
2023-03-31
CLC Number:
CHEN Yumao, ZHAO Hu, YANG Hongwei, WEI Guohua, LUO Pingping. A Sand Body Thickness Prediction Method Based on Deep Learning From Small Sample Data and Its Application[J]. Xinjiang Petroleum Geology, 2023, 44(2): 231-237.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
[1] | 刘雅丽, 刘鹏, 伊伟. 渤南洼陷沙四上亚段沉积相及有利储集层分布[J]. 新疆石油地质, 2014, 35(1):39-44. |
LIU Yali, LIU Peng, YI Wei. Depositional facies and favorable reservoir distribution of Sha-4 upper member of Shahejie formation in Bonan sub-sag[J]. Xinjiang Petroleum Geology, 2014, 35(1):39-44. | |
[2] | 付航. 渤南油田义176区块沙四油层组储层特征及分类评价研究[D]. 黑龙江大庆: 东北石油大学, 2012. |
FU Hang. Study on characteristics and classification of low porosity and low permeability reservoirs of Sha-4 oil layer group in Yi 176,Bonan oilfield[D]. Daqing,Heilongjiang: Northeast Petroleum University, 2012. | |
[3] | 谢启, 李恒权, 李磊, 等. 春光探区强反射背景下的薄储集层预测[J]. 新疆石油地质, 2021, 42(5):617-623. |
XIE Qi, LI Hengquan, LI Lei, et al. Thin reservoir prediction under strong reflection shielding background in Chunguang area[J]. Xinjiang Petroleum Geology, 2021, 42(5):617-623. | |
[4] | 于江龙, 陈刚, 吴俊军, 等. 玛湖凹陷风城组页岩油地质工程甜点地震预测方法及应用[J]. 新疆石油地质, 2022, 43(6):757-766. |
YU Jianglong, CHEN Gang, WU Junjun, et al. Seismic prediction method of geological and engineering shale oil sweet spots and its application in Fengcheng formation of Mahu sag[J]. Xinjiang Petroleum Geology, 2022, 43(6):757-766. | |
[5] | 王世瑞, 王树平, 狄帮让, 等. 基于地震属性特征的河道砂体预测方法[J]. 石油地球物理勘探, 2009, 44(3):304-313. |
WANG Shirui, WANG Shuping, DI Bangrang, et al. Prediction of channel sand body based on seismic attributes[J]. Oil Geophysical Prospecting, 2009, 44(3):304-313. | |
[6] |
迟唤昭, 刘财, 单玄龙, 等. 谱反演方法在致密薄层砂体预测中的应用研究[J]. 石油物探, 2015, 54(3):337-344.
doi: 10.3969/j.issn.1000-1441.2015.03.013 |
CHI Huanzhao, LIU Cai, SHAN Xuanlong, et al. Application of spectral inversion for tight thin-bed sand body prediction[J]. Geophysical Prospecting for Petroleum, 2015, 54(3):337-344.
doi: 10.3969/j.issn.1000-1441.2015.03.013 |
|
[7] | 蔡义丰, 熊婷, 姚卫江, 等. 地震多属性分析技术在薄层砂体预测中的应用[J]. 石油地球物理勘探, 2017, 52(2):140-145. |
CAI Yifeng, XIONG Ting, YAO Weijiang, et al. Application of seismic multi-attribute analysis technology in the prediction of thin-bed sand bodies[J]. Oil Geophysical Prospecting, 2017, 52(2):140-145. | |
[8] | 李伟, 岳大力, 胡光义, 等. 分频段地震属性优选及砂体预测方法:秦皇岛32-6油田北区实例[J]. 石油地球物理勘探, 2017, 52(1):121-130. |
LI Wei, YUE Dali, HU Guangyi, et al. Optimization of seismic properties in sub-frequency band and prediction method of sand body:an example of the northern area of Qinhuangdao 32-6 oilfield[J]. Oil Geophysical Prospecting, 2017, 52(1):121-130. | |
[9] | 杨军. 匹配追踪技术在薄互层砂体预测中的应用[J]. 西部探矿工程, 2018, 30(11):52-55. |
YANG Jun. Application of matching tracking technology in the prediction of thin cross-layered sand[J]. West-China Exploration Engineering, 2018, 30(11):52-55. | |
[10] | 罗钏江, 罗耀华, 鲁建隆, 等. 基于地质统计学反演方法的洛带气田储层预测[J]. 物探化探计算技术, 2019, 41(2):200-206. |
LUO Chuanjiang, LUO Yaohua, LU Jianlong, et al. The application for the geostatistical inversion in Luodai gasfield[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2019, 41(2):200-206. | |
[11] |
HINTON G, SALAKHUTDINOV R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
doi: 10.1126/science.1127647 pmid: 16873662 |
[12] | 安鹏, 曹丹平, 赵宝银, 等. 基于LSTM循环神经网络的储层物性参数预测方法研究[J]. 地球物理进展, 2019, 34(5):1 849-1 858. |
AN Peng, CAO Danping, ZHAO Baoyin, et al. Reservoir physical parameters prediction based on LSTM recurrent neural network[J]. Progress in Geophysics, 2019, 34(5):1 849-1 858. | |
[13] | 安鹏, 刘凤轩, 李佩瑾, 等. 基于PCA-RBFN的河道砂体厚度预测方法[C]. 中国石油学会2021年物探技术研讨会论文集,石油物探编辑部, 2021:752-755. |
AN Peng, LIU Fengxuan, LI Peijin, et al. Prediction method of river sand body thickness based on PCA-RBFN[C]. 2021 Geophysical Technology Seminar of China Petroleum Society,Editorial Department of Geophysical Prospecting for Petroleum, 2021:752-755. | |
[14] |
郭丽丽, 丁世飞. 深度学习研究进展[J]. 计算机科学, 2015, 42(5):28-33.
doi: 10.11896/j.issn.1002-137X.2015.05.006 |
GUO Lili, DING Shifei. Research progress on deep learning[J]. Computer Science, 2015, 42(5):28-33.
doi: 10.11896/j.issn.1002-137X.2015.05.006 |
|
[15] |
丁燕, 杜启振, YASIN Q, 等. 基于深度学习的裂缝预测在S区潜山碳酸盐岩储层中的应用[J]. 石油物探, 2020, 59(2):267-275.
doi: 10.3969/j.issn.1000-1441.2020.02.013 |
DING Yan, DU Qizhen, YASIN Q, et al. Fracture prediction based on deep learning:application to a buried hill carbonate reservoir in the S area[J]. Geophysical Prospecting for Petroleum, 2020, 59(2):267-275.
doi: 10.3969/j.issn.1000-1441.2020.02.013 |
|
[16] | 张国印, 王志章, 林承焰, 等. 基于小波变换和卷积神经网络的地震储层预测方法及应用[J]. 中国石油大学报(自然科学版), 2020, 44(4):83-93. |
ZHANG Guoyin, WANG Zhizhang, LIN Chengyan, et al. Seismic reservoir prediction method based on wavelet transform and convolutional neural network and its application[J]. Journal of China University of Petroleum(Edition of Natural Science), 2020, 44(4):83-93. | |
[17] | CHEN Xueguo. The method of sand body thickness prediction based on attribute optimization and network function approximation and its application[J]. Journal of Oil and Gas Technology, 2017, 39(2):30-35. |
[18] |
TIAN Xingda, HUANG Handong, CHENG Suo, et al. A carbonate reservoir prediction method based on deep learning and multiparameter joint inversion[J]. Energies, 2022, 15(7):2 506.
doi: 10.3390/en15072506 |
[19] | TANG Jian, QIAO Junfei, CHAI Tianyou, et al. Modeling multiple components mechanical signals by means of virtual sample generation technique[J]. Acta Automatic Sinica, 2018, 44(9):1 569-1 589. |
[20] | ZHENG Runan, LIU Wenbo. Research on SAR image target recognition based on virtual sample[J]. Machinery & Electronics, 2017, 35(6):12-17. |
[21] | DAN Lingling, SHI Changlin, ZHANG Jian, et al. Application of Xgboost algorithm based on machine learning in reservoir prediction of offshore oilfield[J]. Springer Series in Geomechanics and Geoengineering, 2021, 33(2):3 157-3 167. |
[22] |
SANG Kaiheng, YIN Xingyao, ZHANG Fanchang. Machine learning seismic reservoir prediction method based on virtual sample generation[J]. Petroleum Science, 2021, 18(6):1 662-1 674.
doi: 10.1016/j.petsci.2021.09.034 |
[23] | 温凯, 韩旭, 李灿, 等. 基于神经网络的天然气流量计检定工艺智能控制系统[J]. 天然气工业, 2021, 41(7):124-133. |
WEN Kai, HAN Xu, LI Can, et al. Neural network based intelligent control system of natural gas flowmeter verification process[J]. Natural Gas Industry, 2021, 41(7):124-133. | |
[24] |
BASHEER I A, HAJMEER M. Artificial neural networks: fundamentals,computing,design and application[J]. Journal of Microbiological Methods, 2000, 43(1):3-31.
doi: 10.1016/S0167-7012(00)00201-3 |
[1] | WU Shunwei, XIA Xueling, ZHU Shijie. Genesis of Calcareous Sandy Conglomerate of Baikouquan Formation in Well XIA72 Fault Block, Mabei Oilfield [J]. Xinjiang Petroleum Geology, 2022, 43(4): 404-409. |
[2] | LIU Jun, LI Wei, GONG Wei, HUANG Chao. Seismic Identification and Description of Ultra-Deep Fault-Controlled Reservoirs in Shunbei Area [J]. Xinjiang Petroleum Geology, 2021, 42(2): 238-245. |
[3] | LUO Yu, WANG Yin, WANG Rong, YUAN Wen. Construction and Analysis of Pore-Fracture Network Model of Carbonate Rock [J]. Xinjiang Petroleum Geology, 2021, 42(1): 107-112. |
[4] | JIA Shuguang, WANG Jun, WANG Lingling, YU Hongguo, QI Jie, ZENG Tan. Seismic Prediction Methods of Shale Oil Sweet Spots in Lucaogou Formation of Jimsar Sag [J]. Xinjiang Petroleum Geology, 2020, 41(5): 535-541. |
[5] | ZHOU Zhou, YANG Fei, LIU Jinshuai, ZHOU Qian, GONG Weicheng. Fine Extrapolation of Stratigraphic Oil-Gas Reservoir Boundary Based on Seismic Forward Modeling: A Case Study of the First Member of Dainan Formation in ShuaiduoChenjiashe Belt, Qintong Sag [J]. , 2017, 38(4): 1-1. |
[6] | LIU Ling1, WANG Feng2, LIU Yuxia3. Application of Seismic Sedimentology to Organic ReefBank Body Identification in Y District [J]. , 2015, 36(3): 1-1. |
[7] | WEI Hongxue1, SHANG Zhonglei2, ZHANG Jining1. Feasibility Analysis of 3D Seismic Exploration of the Coal Goaf [J]. , 2013, 34(6): 1-1. |
[8] | DONG Wen-bo, WU Yu-han, WU Cai-xi, MAO Dan-feng. Sublacustrine Fan Identification by Seismic Interpretation Technology—An example from Karamay oilfield [J]. Xinjiang Petroleum Geology, 2011, 32(2): 183-184. |
[9] | JIN Zhen-kui, SHI Xiao-zhang, HE Miao. Identification Methods for Single-Channel Sand Body [J]. Xinjiang Petroleum Geology, 2010, 31(6): 572-575. |
[10] | ZENG Bo, WU Yu-han, QU Jian-hua, WU Cai-xi, WEN De-jin, HU Song. Application of Seismic Multi-Attributes to Identification of Non-Structural Traps—An example from 201 Well Area in Karamay field [J]. Xinjiang Petroleum Geology, 2010, 31(2): 197-198. |
[11] | WANG Yan-jie, ZHANG Yu-liang, LIU Nian-zhou, WU Shun-wei, YANG Zuo-ming, LI Dao-qing. Favorable Reservoir Prediction of Carboniferous Igneous Rock in Kelameili Gas Field in Junggar Basin [J]. Xinjiang Petroleum Geology, 2010, 31(1): 7-9. |
[12] | TANG Hua-feng, CUI Feng-lin, WANG Pu-jun, CAO Guo-yin, YANG Bo. Seismic Identification of Volcanic Reservoir Constrained by Geology Model [J]. Xinjiang Petroleum Geology, 2009, 30(5): 563-565. |
[13] | YU Xiao-wei, ZHENG Xiao-dong, LI Yan-dong, YANG Hao. Using Optimized Seismic Attributes to Predict Reef Reservoir [J]. Xinjiang Petroleum Geology, 2009, 30(2): 221-224. |
[14] | FENG Ming-you, HAN Qiang, PU Ren-hai. Application of Reflection Strength Slope to Identification of Sequence and Sedimentary Environment [J]. Xinjiang Petroleum Geology, 2008, 29(3): 380-381. |
[15] | GAO Lei, WANG Yong-gang, ZHANG Ke, LIU Zhen-yu, HUANG Li-liang. Prediction of Deep Volcanic Rocks in Wu-Xia Fault Belt by Seismic Attributes [J]. Xinjiang Petroleum Geology, 2007, 28(4): 416-418. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||