Multiple-Point Statistics for Training Image Selection
Multiple-Point Statistics for Training Image Selection
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DOI：
10.1007/s11053-008-9058-9
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发表时间：
2007-12
影响因子：
5.4
通讯作者：
J. Boisvert;M. Pyrcz;C. Deutsch
中科院分区：
文献类型：
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作者：
J. Boisvert;M. Pyrcz;C. Deutsch
选择代表目标空间现象（储层、矿物存款、土壤类型等）的训练图像（TI），是多点统计（MPS）模拟的有效应用。根据现有的地质知识，通常可以将潜在的TI缩小到一般子集;然而，这在很大程度上是主观的。提出了一种方法，比较运行的分布和多点密度函数从现有的勘探数据和TI。MPS的差异可用于选择最能代表数据集的TI。该工具可以应用于进一步缩小一套TI，以获得更现实的空间不确定性模型。此外，局部条件数据和TI的空间统计之间的显著差异可能导致MPS中的伪影。该工具的使用将识别调节数据和TI之间的矛盾。TI的选择是针对一个有32口威尔斯井的深水油藏进行论证的。
Selecting a training image (TI) that is representative of the target spatial phenomenon (reservoir, mineral deposit, soil type, etc.) is essential for an effective application of multiple-point statistics (MPS) simulation. It is often possible to narrow potential TIs to a general subset based on the available geological knowledge; however, this is largely subjective. A method is presented that compares the distribution of runs and the multiple-point density function from available exploration data and TIs. The difference in the MPS can be used to select the TI that is most representative of the data set. This tool may be applied to further narrow a suite of TIs for a more realistic model of spatial uncertainty. In addition, significant differences between the spatial statistics of local conditioning data and a TI may lead to artifacts in MPS. The utilization of this tool will identify contradictions between conditioning data and TIs. TI selection is demonstrated for a deepwater reservoir with 32 wells.