Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network
This is an incremental improvement for reservoir modeling in geoscience, targeting more accurate predictions of properties like porosity and permeability.
The paper tackles the problem of estimating spatial distributions of static properties in reservoir modeling by developing a nearest-neighbor neural network method that incorporates randomization for uncertainty quantification, addressing limitations of traditional geostatistical approaches like IDW and Kriging.
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.