| 南翼山富钾锂卤水储层识别方法 |
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| 引用本文： | 侯献华,冯磊,郑绵平,王伟,樊馥,赵为永,高雪峰.南翼山富钾锂卤水储层识别方法[J].地球科学,2022,47(1):45-55. |
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| 作者姓名： | 侯献华 冯磊 郑绵平 王伟 樊馥 赵为永 高雪峰 |
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| 作者单位： | 1.中国地质科学院矿产资源研究所自然资源部盐湖资源与环境重点实验室, 北京 100037 |
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| 基金项目： | 国家重点研发计划课题(No.2017YFC0602802);中国地质调查局项目(Nos.DD20201115,DD20211343)。 |
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| 摘 要： | 油井调查显示，在柴达木西部以南翼山为代表的储油构造区，广泛分布含钾、锂等有用元素的深层卤水，在古近纪-新近纪不同层位均有分布，具有极大的社会经济价值. 根据构造及地层岩性特征，认为卤水储存空间为孔隙、裂隙型，由于南翼山地区含水层在声波测井曲线中响应特征并不明显，传统以声波测井为约束条件的波阻抗反演技术难以有效识别卤水储层，到目前为止，对该类型卤水在各层位的赋存状态仍没有好的判识方法. 因此，基于神经网络的联合反演技术，将对卤水层响应更明显的感应测井信息与地震信息相结合，利用神经网络极强的特征模式快速提取能力，融合感应测井、地震波形、地震属性、波阻抗等数据，获得了能够更精确反映富钾锂卤水储层的三维感应数据体，提高了卤水储层的识别能力，为预测南翼山富钾锂卤水在纵向和横向的分布提供了重要依据.
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| 关 键 词： | 南翼山 深层卤水 地震反演 神经网络 柴达木盆地 矿床学 |
| 收稿时间： | 2021-01-06 |
Recognition Method of Potassium-Rich Lithium Brine Reservoir in Nanyishan |
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| Hou Xianhua,Feng Lei,Zheng Mianping,Wang Wei,Fan Fu,Zhao Weiyong,Gao Xuefeng.Recognition Method of Potassium-Rich Lithium Brine Reservoir in Nanyishan[J].Earth Science-Journal of China University of Geosciences,2022,47(1):45-55. |
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| Authors: | Hou Xianhua Feng Lei Zheng Mianping Wang Wei Fan Fu Zhao Weiyong Gao Xuefeng |
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| Affiliation: | (Ministry of Natural Resources Key Laboratory of Saline Lake Resources and Environments,Institute of Mineral Resources,Chinese Academy of Geological Sciences,Beijing 100037,China;Institute of Resources&Environment,Henan Polytechnic University,Jiaozuo 454000,China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;PetroChina Qinghai Oilfield Company,Dunhuang 736202,China) |
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| Abstract: | The survey of oil wells shows that deep brine containing useful elements such as lithium and potassium is widely distributed in the oil storage structure area represented by Nanyishan in the west of Qaidam.The brine is distributed from the Paleogene to the Neogene and has great social and economic values.According to the structural and stratum lithology characteristics,the brine storage space is considered to be pores and fractures.As the response characteristics of the aquifer in the acoustic logging curve are not obvious in the Nanyishan area,it is difficult to effectively identify the brine reservoir by the traditional impedance inversion technology with acoustic logging as a constraint.There is still no suitable method to identify the occurrence state of this type of brine in each layer.For this reason,in this study it combines seismic information with induction logging information that has a more obvious response to the brine layer based on the neural network joint inversion technology,and uses the strong extraction ability to feature pattern of neural network and integrates induction logging,seismic waveform,seismic attributes,impedance and other data to obtain a three-dimensional induction data volume that can more accurately reflect the lithium-potassium brine reservoir,which improves the identification ability of the brine reservoir.In the paper it provides an important basis for predicting the vertical and horizontal distribution of potassium-rich lithium brine in Nanyishan. |
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| Keywords: | Nanyishan deep brine earthquake inversion neural network Qaidam Basin mineral geology |
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