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Data for Filtration Properties Estimation of Host Rocks
- Citation Author(s):
-
YanKuchin
IICT MES RKRavilMukhamedievIICT MES RK, Satbayev University - Submitted by:
- Ravil Muhamedyev
- Last updated:
- Tue, 05/17/2022 - 22:18
- DOI:
- 10.21227/fw57-ka70
- Data Format:
- Research Article Link:
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Abstract
To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .
The regression model is based on an ANN with one hidden layer consisting of 31 neurons. K_f_regression values were also calculated for all intervals of the specified dataset using the currently used procedure, K_f_calculation.
K_f_regression K_f_calculation values are not to be included to the input values list.
The delails of the metod see in "Ravil I. Mukhamediev,
, Yan Kuchin etc. Estimation of Filtration Properties of Host Rocks in Sandstone-type Uranium Deposits Us-ing Machine Learning Methods."
The file can be freely used to calculate filtration coefficients. K_f_regression K_f_calculation values are not to be included to the input values list.
The delails of the metod see in "Ravil I. Mukhamediev,, Yan Kuchin etc. Estimation of Filtration Properties of Host Rocks in Sandstone-type Uranium Deposits Us-ing Machine Learning Methods."
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