用近邻神经网络提升油藏静态属性预测精度
Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network
- 基于近邻算法捕捉数据点局部空间关系
- 结合随机化实现插值过程的不确定性量化
- 适合需要高精度与置信度评估的油藏建模者
本文提出一种利用最近邻神经网络估计油藏建模中静态属性空间分布的方法。该方法借助神经网络对复杂非线性函数的逼近能力，尤其适用于空间插值任务。通过引入最近邻算法捕捉数据点间的局部空间关系，并结合随机化技术量化插值过程中的固有不确定性。相比传统地质统计方法（如反距离加权法IDW和克里金法Kriging），该方法能更好建模油藏数据中的复杂非线性依赖关系。通过融合空间邻近性和不确定性量化，所提方法可显著提升孔隙度、渗透率等静态属性预测的准确性。
原文摘要 · Abstract (English)
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.
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