多点模式优选与压缩及在储层建模中的应用
发布时间:2018-06-24 06:59
本文选题:SNESIM + 紧凑型树 ; 参考:《中国地质大学(北京)》2015年硕士论文
【摘要】:本论文通过对多点地质统计学进行研究,选取其中经典序贯非迭代算法SNESIM(Single Normal Equation Simulation)进行剖析,并对该算法建模过程中影响性较大的几个因素分别进行了分析。针对算法在建模过程中内存消耗过多这个问题进行深入研究,进而提出优化方案,通过“紧凑型搜索树”进行模式优选与压缩,编码实施。并对优化算法进行实际工区建模应用,最终对建模效率进行记录,进而对记录分析,最终对建模效果进行评估。目标比率与伺服系统修正参数、扫描模板节点个数、多级网格、最小重复个数四个因素对建模过程,具有重要的影响作用。前期算法分析的过程中,通过对目标比率与伺服系统修正参数的实验,可以对整体建模结果中各种模拟结果比例进行量化控制。这两个参数通过互补作用影响河道走向。扫描模板节点个数对速度以及模拟效果影响较大。通过对该参数实验,分析,发现模板节点个数与模拟工区大小关系紧密,较大的工区需要进行相应增大,但是不可过大,否则会影响速度,但不会对模拟效果有所提升。多级网格方法的提出主要是解决在节点数一定的情况下,对大范围数据结构再现的一个问题。通过多级网格的应用,在较小模板节点个数的情况下,能够对大范围空间结构进行再现。最小重复个数保证提取出来模式的有效性。通过最小重复个数的限制,把其中不重要的模式排除在外,使得模拟结果更加精确。SNESIM算法创新点主要通过搜索树的引入使得原来多点地质统计学建模方法变得实际可用。搜索树大大优化了内存的消耗,使得模拟速度加快,同时对建模过程进行了优化,从而使多点地质统计学的发展,从理论研究发展到了真正的实用阶段。为三维模型的建立,提供了非常实用可靠的算法支撑。但随着石油开采的发展,所产生的数据量在飞速的增长。导致大型三维建模对内存的要求更高。本文通过对搜索树结构认真研究的基础上,提出紧凑型树进行优化,主要通过对训练图像中提取的模式进行优选与压缩,从而使得内存消耗大大降低,并进行代码编写,使得模式优化得以实现,提升了计算速度。最终通过工区的实际应用。在多种训练图像为基础的前提下,进行了多次多点建模。发现这种算法能够在保证信息完整的前提下,加快三维模型建模速度。
[Abstract]:In this paper, we select SNESIM (single normal equation Simulation), a classical sequential non-iterative algorithm, to analyze the multi-point geostatistics, and analyze several influential factors in the modeling process of the algorithm. In order to solve the problem of excessive memory consumption in the modeling process of the algorithm, the optimization scheme is put forward, and the pattern is optimized and compressed by "compact search tree", and the coding is implemented. Finally, the efficiency of modeling is recorded, then the record is analyzed, and the modeling effect is evaluated. The ratio of target to the modified parameters of servo system, the number of scanning template nodes, the number of multilevel grids and the minimum number of duplicates play an important role in the modeling process. In the process of early algorithm analysis, through the experiment of target ratio and servo system correction parameters, the proportion of simulation results in the whole modeling results can be quantitatively controlled. These two parameters influence the river course through complementary action. The number of scan template nodes has great influence on the speed and simulation effect. Through the experiment of this parameter, it is found that the number of template nodes is closely related to the size of the simulated work area, and the larger work area needs to be increased correspondingly, but not too large, otherwise it will affect the speed, but it will not improve the simulation effect. The multilevel grid method is proposed to solve the problem of reproducing the large scale data structure in the case of a certain number of nodes. With the application of multilevel grid, the large spatial structure can be reproduced under the condition of small number of template nodes. The minimum number of duplicates ensures the validity of the extracted pattern. By limiting the minimum number of duplicates, the unimportant patterns are excluded, which makes the simulation results more accurate. The innovation point of SNESIM algorithm makes the original multi-point geostatistical modeling method practical and practical mainly through the introduction of search tree. The search tree greatly optimizes the memory consumption, speeds up the simulation speed, and optimizes the modeling process, which leads to the development of multi-point geostatistics, from the theoretical research to the real practical stage. It provides a very practical and reliable algorithm support for the establishment of three-dimensional model. But with the development of petroleum exploitation, the amount of data generated is increasing rapidly. This leads to higher memory requirements for large-scale 3D modeling. Based on the careful study of the structure of the search tree, this paper puts forward the compact tree optimization, mainly through the optimal selection and compression of the pattern extracted from the training image, so that the memory consumption is greatly reduced, and the code is compiled. The mode optimization is realized and the calculation speed is improved. Finally, through the practical application of the work area. Based on multiple training images, multiple multi-point modeling is carried out. It is found that this algorithm can accelerate the modeling speed of 3D model on the premise of information integrity.
【学位授予单位】:中国地质大学(北京)
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:P618.13;P628
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