Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network

TL;DR

This paper introduces a neural network-based method that combines nearest-neighbor algorithms with uncertainty quantification to improve the spatial estimation of static reservoir properties over traditional geostatistical techniques.

Contribution

It presents a novel approach integrating neural networks with nearest-neighbor algorithms and uncertainty quantification for reservoir property estimation.

Findings

Enhanced accuracy in porosity and permeability predictions.

Better modeling of complex non-linear spatial dependencies.

Quantified uncertainty improves confidence in estimates.

Abstract

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.