油气田开发

基于自适应空间抽样由二维剖面重构三维地质模型的方法-以加拿大某区块McMurray组储集层为例

  • 王立鑫 ,
  • 尹艳树 ,
  • 王晖 ,
  • 张昌民 ,
  • 冯文杰 ,
  • 刘振坤 ,
  • 王盘根 ,
  • 程丽芳 ,
  • 刘炯
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  • 1.长江大学地球科学学院,武汉 430100;
    2.中海油研究总院有限责任公司,北京 100028;
    3.中国石化石油勘探开发研究院,北京 100083
王立鑫(1991-),男,湖北襄阳人,长江大学在读博士研究生,从事储集层建模方面研究。地址:湖北省武汉市蔡甸区大学路111号,长江大学地球科学学院,邮政编码:430100。E-mail:201571323@yangtzeu.edu.cn

收稿日期: 2020-06-14

  修回日期: 2021-01-30

  网络出版日期: 2021-03-19

基金资助

国家科技重大专项(2017ZX05005-004-002,2016ZX05031-002-001); 国家自然科学基金项目“多点地质统计学相控地震同时反演方法”(41872138); 长江大学地质资源与地质工程一流学科开放基金项目(2019KFJJ0818029)

A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada

  • WANG Lixin ,
  • YIN Yanshu ,
  • WANG Hui ,
  • ZHANG Changmin ,
  • FENG Wenjie ,
  • LIU Zhenkun ,
  • WANG Pangen ,
  • CHENG Lifang ,
  • LIU Jiong
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  • 1. School of Geosciences, Yangtze University, Wuhan 430100, China;
    2. CNOOC Research Institute Co., Ltd., Beijing 100028, China;
    3. Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China

Received date: 2020-06-14

  Revised date: 2021-01-30

  Online published: 2021-03-19

摘要

基于连井剖面、沉积相平面等地质分析结果构建正交的二维训练图像,通过线性池化方法获得3个方向上的二维概率,随后采用对数线性池化将3个方向上的概率融合,最终确定未知点处的三维多点模式概率,实现由二维剖面重构三维模型的目的。针对二维训练图像中模式变化性较小,概率分布代表性降低从而导致抽样不确定性增加的问题,引入自适应空间抽样方法,采用迭代模拟策略,从前次模拟结果中可信度高的区域抽取部分点作为附加条件点参与下一次模拟,从而提高了模式概率抽样稳定性。侧积层概念模型对比表明,采用自适应空间抽样的重构算法提高了模式抽样的准确性和空间结构特征的合理性,能够准确反映侧积层形态和分布样式。在加拿大某区块McMurray组曲流河储集层的实际应用表明,新方法准确地再现了潮汐影响下曲流河储集层内部复杂的侧积层形态、空间分布样式和发育特征。抽稀井检验表明,模拟准确度在85%以上,新钻水平井侧积层解释与预测结果符合率达到80%。图13表3参41

本文引用格式

王立鑫 , 尹艳树 , 王晖 , 张昌民 , 冯文杰 , 刘振坤 , 王盘根 , 程丽芳 , 刘炯 . 基于自适应空间抽样由二维剖面重构三维地质模型的方法-以加拿大某区块McMurray组储集层为例[J]. 石油勘探与开发, 2021 , 48(2) : 347 -359 . DOI: 10.11698/PED.2021.02.11

Abstract

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies; the 2D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3D multi-point pattern probabilities at the unknown points, to realize the reconstruction of a 3D model from 2D cross-section. To solve the problems of reducing pattern variability in the 2D training image and increasing sampling uncertainty, an adaptive spatial sampling method is introduced, and an iterative simulation strategy is adopted, in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability. The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics, and accurately reflect the morphology and distribution pattern of the lateral accretion layer. Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape, spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect. The test by sparse wells shows that the simulation accuracy is above 85%, and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.

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