Estimation of Filtration Properties of Host Rocks in Sandstone-Type Uranium Deposits Using Machine Learning Methods

Abstract

The nuclear decay of uranium is one of the cleanest ways to meet the growing energy demand. The uranium needed for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in-situ leaching (ISL). The effective use of ISL requires, among other things, the correct determination of the filtration characteristics of the host rocks. In Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago, and in some cases, give inaccurate results. At the same time, knowledge of filtration characteristics is necessary for the calculation of recoverable reserves, prediction of production dynamics, calculation of the optimum number of wells, etc. This paper describes a method for calculating the filtration coefficient of ore-bearing rocks using machine learning. The proposed method is based on nonlinear regression models. It also allows the estimation of the filtration properties of rocks within the process acidification zone, where the existing method is not applicable. The proposed method applies to approximately half of the uranium mined in the world and makes it possible to significantly (by 22 %-70%) increase the accuracy of the filtration coefficient determination and, accordingly, improve the accuracy of recoverable reserves calculation and economic indicators of mining processes.

Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning

“…The following metrics are widely used to assess the quality of the regression models [75][76][77]: Mean Squared Error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). To evaluate the hydrological models, the Nash-Sutcliffe model efficiency (NSE) coefficient [78] is widely used, the formula for calculating of which coincides with the formula for calculating the coefficient of determination (R 2 ) (Table 5).…”

Research into mine working fastening technology in the zones of increased rock pressure behind the longwall face to ensure safe mining operations

“…In general, maintenance costs increase with increasing depth, ranging from total costs at medium depths (up to 600 m) 4-5%, and at high depths (up to 750-800 m) up to 15%. In the Karaganda Coal Basin as a whole, 25% of the balance coal reserves are confined to areas with simple, 40%medium and 35%difficult mining conditions [47]- [49].…”

Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods

“…ML methods have been used at uranium deposits for lithologic classification [27]; stratigraphy [28]; the determination of the filtration properties of host rocks [2]; and the assessment of the influence of expert marking of logging data [29]. It turned out that the quality of interpretation of logging data in uranium fields, as well as in some tasks solved in oil fields (problems of classifying the connectivity quality (among six ordinal classes) and hydraulic isolation (two classes)) [30], depends crucially on expert data labeling [31].…”

“…The methods of applying machine learning to lithologic classification have been discussed in a number of papers (see Section 2). The problem of determining the filtration coefficient is considered in [2,3], in which machine learning methods demonstrate an almost twofold increase in accuracy compared to the currently used methodology.…”