Известия Томского политехнического университета: Инжиниринг георесурсов (Aug 2022)
SIMULATION OF LIQUID MOVEMENT WHILE WELL FUND OPERATION IN URANIUM DEPOSITS PRODUCED BY ISL METHOD
Abstract
The relevance of the study is caused by the possibility of expanding the use of induction logging data for spatial modeling of the spreading of technological solutions in hydrogenous uranium deposits. Regression modeling is based on the dependence of changes in the geoelectric properties of rocks on changes in their hydrophysical properties during leaching, and can be used to improve the efficiency of leaching monitoring and predicting. Purpose: to substantiate the reliability of the model of the movement of process fluids in reservoir-infiltration type deposits, developed on the basis of the least squares method using the results of induction logging as input data. Objects: induction logging data from wells of block X of the Moinkum deposit, Chu-Sarysu uranium ore province. Methods: induction logging, correlation and regression analysis using Excel, Statistica, Curve Editor, Matlab and LibreCad software environments. The hardware for induction logging sounding is represented by a single probe three-coil instrument PIK-50 and IK-42M. Results. The calculated indicators of the adequacy of the regression model for changing the electrical conductivity and effective power of the block, such as the coefficient of determination, the estimate of the variances of the least squares estimates, the Student's coefficient, the F-criterion, revealed its reliability and veracity. On the example of a block of the Moinkum deposit, Chu-Sarysu uranium ore province, the effectiveness of monitoring the movement of process solutions using IR data using a least squares regression model was established. The expediency of using induction logging data for modeling the spreading of technological solutions on the example of the reservoir-infiltration of the Moinkum deposit, Chu-Sarysu uranium ore province is proved and a model of solutions spreading is formed using the proposed algorithm for applying regression modeling using the least squares method.
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