西南石油大学学报(自然科学版) ›› 2023, Vol. 45 ›› Issue (2): 43-57.DOI: 10.11885/j.issn.1674-5086.2021.01.29.01

• 地质勘探 • 上一篇    下一篇

复杂断裂系统浊积储层自相控反演技术研究

王宗俊1,2, 田楠1,2, 范廷恩1,2, 高云峰1,2   

  1. 1. 海洋石油高效开发国家重点实验室, 北京 朝阳 100028;
    2. 中海油研究总院有限责任公司, 北京 朝阳 100028
  • 收稿日期:2021-01-29 发布日期:2023-05-05
  • 通讯作者: 王宗俊,E-mail:wangzj@cnooc.com.cn
  • 作者简介:王宗俊,1985年生,男,汉族,山东潍坊人,高级工程师,硕士,主要从事深度学习、油藏地球物理及油气田开发等方面的研究工作。E-mail:wangzj@cnooc.com.cn
    田楠,1983年生,女,汉族,河北保定人,高级工程师,硕士,主要从事油气田开发地球物理方面的研究工作。E-mail:tiannan2@cnooc.com.cn
    范廷恩,1972年生,男,汉族,辽宁葫芦岛人,高级工程师,博士,主要从事油田开发方面的研究工作。E-mail:fante@cnooc.com.cn
    高云峰,1974年生,男,汉族,吉林磐石人,高级工程师,博士,主要从事油藏地球物理技术应用方面的研究工作。E-mail:gaoyf@cnooc.com.cn

Self-facies-control Pre-stack Inversion Technology for Turbidite Sandstone Reservoir with Complex Fault System

WANG Zongjun1,2, TIAN Nan1,2, FAN Ting'en1,2, GAO Yunfeng1,2   

  1. 1. State Key Laboratory of Offshore Oil Exploitation, Chaoyang, Beijing 100028, China;
    2. CNOOC Research Institute Co. Ltd., Chaoyang, Beijing 100028, China
  • Received:2021-01-29 Published:2023-05-05

摘要: 浊积砂岩储层为典型的重力流沉积,储层横向突变,纵向多期叠置,迁移摆动频繁。地震反演是进行储层精细描述的主要方法之一,但E油田复杂的断裂系统、储层的横向突变以及超限厚度层的发育均制约了储层反演的精度及后续应用。为此,针对E油田,重点研究并提出了复杂断裂系统约束下的自相控叠前反演方法,首先,利用基于断层接触关系图版库的深度学习算法构建复杂断裂系统模型,进而构建高精度地震地层格架;其次,利用自相控低频模型构建方法,构建高精度自相控低频模型;最后,在高精度地层格架和自相控低频模型的约束下实现自相控叠前反演,有效提高了断层附近砂体预测、超限厚储层刻画及储层横向边界的识别精度。E油田实践表明,该方法取得了较好的应用效果,16口新钻开发井水平段厚度预测与实钻结果吻合率为91%。

关键词: 深度学习, 复杂断层, 超限厚储层, 储层边界, 自相控低频模型

Abstract: Turbidite sandstone reservoir is a typical gravity flow deposit, which is characterized by lateral variation, vertical multi-stage superposition and frequent migration. Seismic inversion is one of the main methods for fine reservoir description, but the complex fault system, lateral abrupt variation and overlimit thickness of the reservoir in E Oilfield restrict the accuracy of reservoir inversion and its subsequent application. In order to solve the problem of reservoir prediction in E Oilfield, a self-facies-control pre-stack inversion technology with complex fault system is proposed in this paper. Firstly, the deep learning algorithm based on the fault contact relationship chart library is used to construct the complex fault system model, and then the high-precision seismic stratigraphic framework is constructed. Secondly, a high-precision self-facies-control low-frequency model is built using the self-facies-control low-frequency model construction technology. Finally, under the constraints of high-precision stratigraphic framework and self-facies-control low-frequency model, self-facies-control pre-stack inversion is realized, which effectively improves the accuracy of sand body prediction near the fault, overlimit thick reservoir characterization and reservoir lateral boundary identification. The application in E Oilfield shows that this method has achieved good results. The thickness coincidence rate of the horizontal length of 16 new drilled development wells is 91%.

Key words: deep learning, complex fault, overlimit thick reservoir, reservoir boundary, self-facies-control low frequency model

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