Application of GeoEast's characteristic techniques to prediction of sandy debris flow reservoirs in Northeast Ring of West Sag of Well Pen-1
WANG Libao1, FU Libing2, HOU Gangfu1, YE Yueming1, LI Lisheng1, YANG Cun1
1. PetroChinaHangzhou Research Institute of Geo-logy, Hangzhou, Zhejian 310023, China; 2. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100086, China
Abstract：It is difficult to predict sandy debris flow reservoirs because of their strong heterogeneity and rapid vertical and horizontal changes. Therefore, it is necessary to finely describe the sandy debris flow reservoirs to expand the new field of oil and gas exploration. The prediction of sandy debris flow reservoirs by the multi-attribute fusion technology requires attribute optimization, and thus, it is difficult to obtain clear interpretation results. Considering this, we use GeoEast's kernel principal component attribute optimization technology and the neural network inversion technology to qualitatively and quantitatively predict sandy debris flow reservoirs, respectively, to determine the development area of the reservoirs. Specifically, we analyze the seismic response characteristics of sandy debris flow and employ the kernel principal component compression technique to qualitatively predict the sandy debris flow. Given the sensitivity of the sensitivity curve (GR curve) to the sandy debris flow reservoirs, we use neural network inversion to quantitatively predict the distribution range of the sandy debris flow reservoirs. The results show that six sandy debris flow sand bodies are determined on the plane by use of the neural network inversion results, which are in good agreement with the drilling results.
王力宝, 傅礼兵, 厚刚福, 叶月明, 李立胜, 杨存. GeoEast软件特色技术在盆1井西凹陷北东环带砂质碎屑流储层预测中的应用[J]. 石油地球物理勘探, 2022, 57(s1): 154-159.
WANG Libao, FU Libing, HOU Gangfu, YE Yueming, LI Lisheng, YANG Cun. Application of GeoEast's characteristic techniques to prediction of sandy debris flow reservoirs in Northeast Ring of West Sag of Well Pen-1. Oil Geophysical Prospecting, 2022, 57(s1): 154-159.
乔玉雷, 隋风贵, 林会喜, 等.准噶尔盆地中部车莫古隆起对油气的控制作用[J].石油天然气学报, 2013, 35(12):56-61.QIAO Yulei, SUI Fenggui, LIN Huixi, et al.The control of Chemo palaeohigh on oil and gas in Central Junggar basin[J].Journal of Oil and Gas Technology, 2013, 35(12):56-61.
[2]
张枝焕, 秦黎明, 李伟, 等.准噶尔盆地腹部车莫古隆起南北两侧含油构造油源及烃源灶转移[J].中国地质, 2009, 36(4):826-836.ZHANG Zhihuan, QIN Liming, LI Wei, et al.The distribution of oil sources and the transformation of hydrocarbon kitchens in oil-bearing structural belts on northern and southern sides of the Chemo plaeo-uplift within central Junggar basin[J].Geology in China, 2009, 36(4):826-836.
[3]
吴晓智, 张年富, 石昕, 等.准噶尔盆地车莫古隆起构造特征与成藏模式[J].中国石油勘探, 2006, 11(1):65-68, 84.WU Xiaozhi, ZHANG Nianfu, SHI Xin, et al.Characteristics and reservoiring mode of Chepaizi-Mosuowan paleo-uplift in Junggar basin[J].China Petroleum Exploration, 2006, 11(1):65-68, 84.
[4]
施尚明, 王杰, 段彦清.基于RGB多地震属性融合的储层预测[J].黑龙江科技大学学报, 2016, 26(5):502-505.SHI Shangming, WANG Jie, DUAN Yanqing.Re-servoir prediction based on RGB multiple seismic attribu-tes fusion[J].Journal of Heilongjiang University of Science & Technology, 2016, 26(5):502-505.
[5]
HAMPTON M A.The role of subaqueous debris flow in generating turbidity currents[J].Journal of Sedimentary Research, 1972, 42(4):775-793.
[6]
SHANMUGAM G, MOIOLA R J, MCPHERSON J G, et al.Comparison of turbidite facies associations in modern passive-margin Mississippi fan with ancient active-margin fans[J].Sedimentary Geology, 1988, 58(1):63-77.
[7]
SHANMUGAM G, MOIOLA R J.Reinterpretation of deposition al processes in a classic flysch sequence (Pennsylvanian Jackfork Group), Quachita Mountains, Arkansas and Oklahoma[J].AAPG Bulletin, 1995, 79(5):672-695.
[8]
邹才能, 赵政璋, 杨华, 等.陆相湖盆深水砂质碎屑流成因机制与分布特征——以鄂尔多斯盆地为例[J].沉积学报, 2009, 27(6):1065-1075.ZOU Caineng, ZHAO Zhengzhang, YANG Hua, et al.Genetic mechanism and distribution of sandy debris flows in terrestrial lacustrine basin[J].Acta Sedimentologica Sinica, 2009, 27(6):1065-1075.
[9]
李相博, 付金华, 陈启林, 等.砂质碎屑流概念及其在鄂尔多斯盆地延长组深水沉积研究中的应用[J].地球科学进展, 2011, 26(3):286-294.LI Xiangbo, FU Jinhua, CHEN Qilin, et al.The concept of sandy debris flow and its application in the Yanchang Formation deep water sedimentation of the Ordos Basin[J].Advances in Earth Science, 2011, 26(3):286-294.
[10]
夏景生, 刘晓涵, 王政军, 等.渤海湾盆地南堡凹陷西部东营组三段-沙河街组一段砂质碎屑流沉积特征及油气勘探意义[J].石油学报, 2017, 38(4):399-413.XIA Jingsheng, LIU Xiaohan, WANG Zhengjun, et al. Sedimentary characteristics of sandy debris flow in the 3rd member of Dongying formation and the 1st member of Shahejie formation of the western Nanpu sag, Bohai Bay basin and its significance in hydrocarbon exploration[J].Acta Petrolei Sinica, 2017, 38(4):399-413.
[11]
李楠, 李国辉, 吴长江, 等.坡折带控制下的砂质碎屑流对油气勘探的意义——以四川盆地上三叠统须家河组为例[J].四川地质学报, 2014, 34(4):505-509.LI Nan, LI Guohui, WU Changjiang, et al.The application of sandy debris flow under the contrl of slope break to oil-gas exploration[J].Acta Geologica Sichuan, 2014, 34(4):505-509.
[12]
厚刚福, 曾德龙, 牛志杰, 等.准噶尔盆地砂质碎屑流砂体新发现及其油气勘探意义[J].天然气工业, 2020, 40(11):41-49.HOU Gangfu, ZENG Delong, NIU Zhijie, et al.New discovery of sandy debris flow sandbody and its implications for oil and gas exploration in the Junggar Basin[J].Natural Gas Industry, 2020, 40(11):41-49.
[13]
王开燕, 徐清彦, 张桂芳, 等.地震属性分析技术综述[J].地球物理学进展, 2013, 28(2):815-823.WANG Kaiyan, XU Qingyan, ZHANG Guifang, et al.Summary of seismic attribute analysis[J].Progress in Geophysics, 2013, 28(2):815-823.
[14]
郑和忠, 魏长江, 王树华.基于主成分分析和核主成分分析的地震属性优化的研究[J].青岛大学学报(自然科学版), 2017, 30(3):76-80.ZHENG Hezhong, WEI Changjiang, WANG Shuhua.Seismic attribute optimization research based on principal component analysis and kernel principal component analysis[J].Journal of Qingdao University(Natu-ral Science Edition), 2017, 30(3):76-80.
[15]
印兴耀, 孔国英, 张广智.基于核主成分分析的地震属性优化方法及应用[J].石油地球物理勘探, 2008, 43(2):179-183.YIN Xingyao, KONG Guoying, ZHANG Guangzhi.Seismic attributes optimization based on kernel principal component analysis(KPCA) and application[J].Oil Geophysical Prospecting, 2008, 43(2):179-183.
[16]
郑庆生, 韩大匡.高阶神经网络在储层分布参数定量预测中的应用[J].地球物理学进展, 2007, 22(2):552-555.ZHENG Qingsheng, HAN Dakuang.Estimation of reservoir distribution parameters using higher-order neural network spproach[J].Progress in Geophysics, 2007, 22(2):552-555.
[17]
李东安, 宁俊瑞, 刘振峰.用神经网络和地质统计学综合多元信息进行储层预测[J].石油与天然气地质, 2010, 31(4):493-498, 503.LI Dongan, NING Junrui, LIU Zhenfeng.Reservoir prediction with integrated information based on artificial neural network technology and geostatistics[J].Oil & Gas Geology, 2010, 31(4):493-498, 503.
[18]
周杰.神经网络法地震反演应用及效果[J].断块油气田, 2010, 17(5):560-562, 570.ZHOU Jie.Application and effect of seismic inversion based on neural network[J].Fault-Block Oil & Gas Field, 2010, 17(5):560-562, 570.
