Prediction of relative permeability and calculation of water cut of tight sandstone reservoir based on radial basis function neural network
WANG Qian1, TAN Maojin1, SHI Yujiang2, LI Gaoren2, CHENG Xiangzhi3, LUO Weiping4
1. School of Geophysics and Information Technology in China University of Geosciences, Beijing 100083, China; 2. Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi'an, Shaanxi 710021, China; 3. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China; 4. Research Institute of Exploration and Development, PetroChina Tarim Oilfield Company, Korla, Xinjiang 841000, China
Abstract：Tight sandstone reservoir is characterized by poor physical properties,complex pore structures and strong heterogeneity. It is difficult for conventional methods to predict or estimate the relative permeability and water cut. This paper proposes to use the radial basis function (RBF) neural network to predict the relative permeability of tight sandstone reservoir. Based on the RBF neural network,we choose the Gaussian function and the nearest neighbor algorithm to build a network model,and take water saturation,nuclear magnetic irreducible water saturation,porosity and permeability as inputs and relative oil and water permeability as output to define the best relative permeability network model and parameters after error analysis,and finally calculate the water cut using the split flow equation. For the Chang 8 reservoir of the Yanchang Formation in the Longdong area of the Ordos Basin,the relative oil and water permeability predicted by this method is consistent with the experimental results,and the water cut calculated is consistent with the measured value too.
王谦, 谭茂金, 石玉江, 李高仁, 程相志, 罗伟平. 径向基函数神经网络法致密砂岩储层相对渗透率预测与含水率计算[J]. 石油地球物理勘探, 2020, 55(4): 864-872.
WANG Qian, TAN Maojin, SHI Yujiang, LI Gaoren, CHENG Xiangzhi, LUO Weiping. Prediction of relative permeability and calculation of water cut of tight sandstone reservoir based on radial basis function neural network. Oil Geophysical Prospecting, 2020, 55(4): 864-872.
邹存友,玉立君.中国水驱砂岩油田含水与采出程度的量化关系[J].石油学报,2012,33(2):288-292.ZOU Cunyou,YU Lijun.A quantization relationship between water cut and degree of reserve recovery for waterflooding sandstone reservoirs in China[J].Acta Petrolei Sinica,2012,33(2):288-292.
[2]
闫星宇,顾汉明,肖逸飞,等. XGBoost算法在致密砂岩气储层测井解释中的应用[J].石油地球物理勘探,2019,54(2):447-455.YAN Xingyu,GU Hanming,XIAO Yifei,et al. XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data[J].Oil Geophysical Prospecting,2019,54(2):447-455.
[3]
Xu J C,Guo C H,Jiang R Z,et al. Study on relative permeability characteristics affected by displacement pressure gradient:Experimental study and numerical simulation[J]. Fuel,2016,163(1):314-323.
[4]
周鹏.新型水驱油田含水率预测模型的建立及其应用[J].新疆石油地质,2016,37(4):452-455.ZHOU Peng. New model for water cut forecasting in water flooding oilfields establishment and application[J].Xinjiang Petroleum Geology,2016,37(4):452-455.
[5]
季少聪,杨香华,朱红涛,等.下刚果盆地A区块Madingo组烃源岩TOC含量的地球物理定量预测[J].石油地球物理勘探,2018,53(2):369-380.JI Shaocong,YANG Xianghua,ZHU Hongtao,et al. Geophysical quantitative prediction of TOC content in source rocks of Madingo Formation in Block A, Lower Congo Basin[J]. Oil Geophysical Prospecting,2018,53(2):369-380.
[6]
孙延风,梁艳春,孟庆福.改进的神经网络最近邻聚类学习算法及其应用[J].吉林大学学报(信息科学版), 2002,20(1):63-66.SUN Yanfeng,LIANG Yanchun,MENG Qingfu. Improved neural network nearest neighbor clustering learning algorithm and its application[J].Journal of Jilin University(Information Science Edition),2002,20(1):63-66.
[7]
刘西雷.基于分形理论计算相渗分流量曲线[J].大庆石油地质与开发,2015,34(1):59-62.LIU Xilei. Relative permeability fractional flow curve calculation based on fractal theory[J].Petroleum Geo-logy & Oilfield Development in Daqing,2015,34(1):59-62.
[8]
Burdine N T. Relative permeability calculations from pore size distribution data[J]. Transactions of The Metallurgical Society of AIME,1953,19(8):71-79.
[9]
Jones S C,Roszelle W O.Graphical techniques for determining relative permeability from displacement experiments[J].Journal of Petroleum Technology, 1978,30(5):807-817.
[10]
董平川,马志武,赵常生. 储层相对渗透率评价方法[J].大庆石油地质与开发,2008,27(6):55-58.DONG Pingchuan,MA Zhiwu,ZHAO Changsheng. Evaluating method of relative permeabilities of a reservoir[J]. Petroleum Geology & Oilfield Development in Daqing,2008,27(6):55-58.
[11]
邓英尔,刘慈群,庞宏伟.考虑多因素的低渗透岩石相对渗透率[J]. 新疆石油地质,2003,24(2):152-154.DENG Yinger,LIU Ciqun,PANG Hongwei. Calculation of relative permeability of low permeability rock with multiple factors[J]. Xinjiang Petroleum Geology,2003,24(2):152-154.
[12]
王坤,张烈辉. 考虑应力敏感超低渗油藏油水相对渗透率的计算[J].石油天然气学报,2011,33(11):117-119.WANG Kun,ZHANG Liehui. Calculation of relative permeability of extra-low permeability reservoirs considering stress sensitivity[J]. Journal of Oil and Gas Technology,2011,33(11):117-119.
[13]
Chima A,Geiger S.An analytical equation to predict gas-water relative permeability curves in fracture[C]. SPE Latin America and Caribbean Petroleum Engineering Conference,2012.
[14]
高文君,徐冰涛,黄瑜,等. 水驱油田含水率预测方法研究及拓展[J].石油与天然气地质,2017,38(5):993-999.GAO Wenjun,XU Bingtao,HUANG Yu,et al. Research on and development of prediction method of water cut in water flooding oilfield[J]. Oil & Gas Geology,2017,38(5):993-999.
[15]
童宪章. 油井产状和油藏动态分析[M]. 北京:石油工业出版社,1981, 37-60.
[16]
尹大庆,林东维,朱文波,等.水驱砂岩油藏修正童氏图版含水率预测方法[J]. 大庆石油地质与开发,2014,33(2):54-57.YIN Daqing,LIN Dongwei,ZHU Wenbo,et al. Pre-diting method for the water cut of water flooded sandstone oil reservoirs by corrected Tong's chart board[J]. Petroleum Geology & Oilfield Development in Daqing,2014,33(2):54-57.
[17]
李松泉,程林松,李秀生,等. 特低渗透油藏非线性渗流模型[J].石油勘探与开发,2008,35(5):606-612.LI Songquan,CHENG Linsong,LI Xiusheng,et al. Non-linear seepage flow models of ultra-low perme-ability reservoirs[J].Petroleum Exploration and Deve-lopment,2008,35(5):606-612.
[18]
Moody J,Darken J. Fast learning in networks of locally-tuned processing units[J]. Neural Computation,1989,1(2):281-294.
[19]
王炜,吴耿峰,张博锋,等.径向基函数(RBF)神经网络及其应用[J].地震,2005,25(2):19-25.WANG Wei,WU Gengfeng,Zhang Bofeng,et al. Neural networks of radial basis function (RBF) and it's application to earthquake prediction[J]. Earthquake, 2005,25(2):19-25.
[20]
Tan M J, Liu Q,Zhang S Y. A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale[J]. Geophysics,2013,78(6):D445-D459.
[21]
邹友龙,胡法龙,李长喜,等.利用径向基函数预测岩石渗透率及流体分子组分[J].测井技术,2012,36(3):225-229.ZOU Youlong,HU Falong,LI Changxi,et al. Prediction of rock permeability and fluid molecular composition using radial basis function[J].Well Logging Technology,2012,36(3):225-229.
[22]
Komijani H,Rezaeihassanabadi S,Parsaei R,et al. Radial basis function neural network for electroche-mical impedance prediction at presence of corrosion inhibitor[J].Periodica Polytechnica-Chemical Engineering,2017,61(2):128-132.
[23]
谭茂金. 有机页岩测井岩石物理[M]. 北京:石油工业出版社,2015,58-67.TAN Maojin. Petrophysics and Rock Physics of Organic Shale[M].Petroleum Industry Press,Beijing,2015,58-67.
[24]
张伟,冯进,胡文亮,等. L油田古近系油藏含水率计算方法及其应用[J]. 石油钻探技术, 2016, 44(1):105-110.ZHANG Wei,FENG Jin,HU Wenliang,et al. Calculation method and application for water content of paleogene reservoirs in L oilfield[J].Petroleum Drilling Techniques,2016,44(1):105-110.
[25]
高楚桥,何宗斌,吴洪深,等.核磁共振T2截止值与毛细管压力的关系[J]. 石油地球物理勘探,2004,39(1):117-120.GAO Chuqiao,HE Zongbin,WU Hongshen,et al. Relationship between NMR T2 cutoff and capillary pressure[J]. Oil Geophysical Prospecting,2004,39(1):117-120.
