This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. The method leverages the strengths of neural networks in approximating complex, non-linear functions, particularly for tasks involving spatial interpolation. It incorporates a nearest-neighbor algorithm to capture local spatial relationships between data points and introduces randomization to quantify the uncertainty inherent in the interpolation process. This approach addresses the limitations of traditional geostatistical methods, such as Inverse Distance Weighting (IDW) and Kriging, which often fail to model the complex non-linear dependencies in reservoir data. By integrating spatial proximity and uncertainty quantification, the proposed method can improve the accuracy of static property predictions like porosity and permeability.

翻译：本文提出了一种利用最近邻神经网络估算储层建模中静态属性空间分布的方法。该方法充分发挥了神经网络在逼近复杂非线性函数方面的优势，尤其适用于涉及空间插值的任务。它结合了最近邻算法以捕捉数据点之间的局部空间关系，并引入随机化机制来量化插值过程中固有的不确定性。此方法解决了传统地质统计学方法（如反距离加权法和克里金法）的局限性，这些方法往往难以对储层数据中复杂的非线性依赖关系进行建模。通过整合空间邻近性与不确定性量化，所提出的方法能够提高孔隙度和渗透率等静态属性预测的准确性。