自适应直接取样岩心三维重建算法
Adaptive direct sampling core 3D reconstruction algorithm
作者:许诗涵(电子信息学院);滕奇志(四川大学电子信息学院);冯俊羲(四川大学电子信息学院);丁凯(四川大学电子信息学院)
Author:Xu Shi-Han(College of Electronics and Information Engineering);Teng Qi Zhi(College of Electronics and Information Engineering, Sichuan University);Feng Jun Xi(College of Electronics and Information Engineering, Sichuan University);Ding kai(College of Electronics and Information Engineering, Sichuan University)
收稿日期:2018-05-12 年卷(期)页码:2019,56(2):260-266
期刊名称:四川大学学报: 自然科学版
Journal Name:Journal of Sichuan University (Natural Science Edition)
关键字:三维重建;自适应直接取样算法;储集层岩心图像;训练图像;三级网格
Key words:3D reconstruction;Adaptive direct sampling algorithm;Reservoir core images;Training image;Three-grid
基金项目:国家自然科学基金(61372174); 四川大学研究生课程建设项目(2016KCJS5113)
中文摘要
由于直接取样算法在重建过程中引入了多个需要手动调节的参数,使其在实际运用中具有一定的难度.针对这一问题,提出了一种自适应直接取样岩心三维重建算法.首先,使用三级网格对图像进行逐级重建;其次,使用高斯加权来提高模式匹配的准确性;然后,根据待匹配数据事件的条件数据点自适应的选择模式搜索范围,将距离最小模式的中心点赋给待模拟点;最后,使用算法与传统直接取样算法分别对多张储集层岩心图像进行三维重建.通过比较重建结果与真实结构在统计分布、孔隙结构上的差异,证明了算法的有效性.
英文摘要
The direct sampling algorithm has certain difficulties in practical application because it introduces a number of parameters that need to be manually adjusted during the reconstruction process. Aiming at this problem, an adaptive direct sampling core three dimensional (3D) reconstruction algorithm is proposed. the image is first reconstructed step by step with three level grid. Secondly, Gaussian weighting is used to improve the accuracy of pattern match. Then, according to the conditional data points of the data event to be matched, the pattern search range is adaptively selected, and the center point of the minimum distance pattern is assigned to the point to be simulated. Finally, the proposed algorithm and the traditional direct sampling algorithm are used to perform 3D reconstruction for multiple reservoir core images respectively. The effectiveness of the adaptive direct sampling core 3D reconstruction algorithm is demonstrated by comparing the difference between the reconstruction result and the real structure in the statistical distribution and pore structure.
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