研究论文

井震结合精细储层建模方法与实践

  • 张义 ,
  • 尹艳树 ,
  • 秦志勇 ,
  • 周文
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  • 1. 长江大学地球科学学院, 武汉 430100;
    2. 山东科技大学;山东省沉积成矿作用与沉积矿产重点实验室, 青岛 266590;
    3. 大港油田滨港集团博弘石油化工有限公司, 天津 300280
张义,硕士研究生,研究方向为精细油藏描述,电子信箱:1551269385@qq.com

收稿日期: 2015-05-07

  修回日期: 2015-06-25

  网络出版日期: 2015-10-16

基金资助

国家自然科学基金项目(41572081);山东省沉积成矿作用与沉积矿产重点实验室开放基金项目(DMSM201404);长江大学青年教师基金项目

Development of well-to-seismic integration reservoir modeling and its application

  • ZHANG Yi ,
  • YIN Yanshu ,
  • QIN Zhiyong ,
  • ZHOU Wen
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  • 1. School of Geosciences, Yangtze University, Wuhan 430100, China;
    2. Key Laboratory of Depositional Mineralization & Sedimentary Minerals of Shandong Province; Shandong University of Science and Technology, Qingdao 266590, China;
    3. Bohong Oil Petrochemical Co. Ltd., Bingang Group, Dagang Oilfield Company, PetroChina, Tianjin 300280, China

Received date: 2015-05-07

  Revised date: 2015-06-25

  Online published: 2015-10-16

摘要

融合地震资料与井资料分别在横向和纵向上高密度采样的特点,以中国海上某气田B 区块为例,通过井震联合标定,进行精细构造解释,用准确的速度模型搭建了时间域与深度域联系的桥梁;利用地震解释的断层、层位构建储层骨架模型,应用地质统计学协变量方法,以井数据作主变量,地震反演实现统计得到的岩性概率体作为第二变量约束井间插值、外推和模拟,建立砂体展布模型;依靠砂体展布模型约束沉积相建模,在相模型基础上构建物性模型。实践表明,井震结合储层建模研究断层更精确,能够弥补井间信息不足的缺陷,有效降低井间砂体预测的不确定性,提高模型预测精度。

本文引用格式

张义 , 尹艳树 , 秦志勇 , 周文 . 井震结合精细储层建模方法与实践[J]. 科技导报, 2015 , 33(19) : 91 -95 . DOI: 10.3981/j.issn.1000-7857.2015.19.015

Abstract

The characteristics of high-density sampling of seismic data and well data in the horizontal and vertical directions are integrated to construct the reservoir model for the Section B of a gas field of China. Firstly, the well-to-seismic integration calibration is used to interpret the fine structure, and the accurate velocity model is adopted to set up a bridge between the time domain of seismic data and depth domain of well data. Secondly, the reservoir structural model is built by well correlation under the constraint of the fault and horizon from seismic interpretation. Thirdly, a co-simulation method is developed to forecast the sand distribution, in which well data is the master variable and the lithology probability from seismic inversion is the second variable. The facies model is built on the basis of sand distribution model. Finally, the model of petro-physical property is built under the constraint of the facies model. Appliction has indicated that reservoir modeling with well-to-seismic integration is more accurate in terms of fault and sand body prediction between wells, thereby the improvement of accuracy of the reservoir model.

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