Entropy Evaluation Method for Sandstone Uranium Reservoir Characteristics Based on Convex Hull Search

• Main Authors: Kang He, Ming-Tao Jia, Chen Mei-Fang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Sandstone-hosted uranium deposits; multiple-point density function; convex hull search; spatial information entropy; reservoir homogeneity and continuity
• Online Access: https://ieeexplore.ieee.org/document/9022951/

The continuity and homogeneity of sandstone uranium deposits are inherent characteristics that affect in situ leaching performance indexes and have important guiding effects on the optimization of mining design. In this work, we propose a spatial information entropy calculation method based on a convex hull search strategy to quantitatively evaluate the geological characteristics of uranium deposits. We use the 3-D block model of sandstone-hosted uranium deposits as hard data. First, we use the Alpha-Shapes algorithm to calculate the convex hull of the space area where various types of unit blocks are located. Second, we formulate a suitable multi-point configuration based on the convex hull size. Then, we use the multi-point density function. Scanning the convex hull point cloud establishes a probability density function to calculate the local information entropy of various sub-regions. Finally, the local spatial information entropy is aggregated using addition and multiplication to obtain the joint spatial information entropy of the entire evaluation area. A series of parameter sensitivity and performance analysis experiments prove the stability of the method and the accuracy of the quantitative and homogeneous evaluations of the complex geological conditions. In order to solve the problem of the difference between the designed production capacity and actual production capacity of a uranium deposit, we use a new method to evaluate the homogeneity and continuity of the lithology and grade of the deposit. The new method and evaluation conclusions can be used for the automation and understanding of future engineering optimization processes.