Data for Filtration Properties Estimation of Host Rocks

Citation Author(s):
Yan
Kuchin
IICT MES RK
Ravil
Mukhamediev
IICT MES RK, Satbayev University
Nadia
Yunicheva
IICT MES RK
Elena
Muhamedijeva
IICT MES RK
Submitted by:
Ravil Muhamedyev
Last updated:
Wed, 10/06/2021 - 01:26
DOI:
10.21227/fw57-ka70
Data Format:
License:
0
0 ratings - Please login to submit your rating.

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." 

Instructions: 

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." 

Comments

g

Submitted by shubhangi Kharke on Mon, 09/20/2021 - 05:05