Geophysical research: article

V.V. Spichak A.G. Goidina
Geoelectromagnetic Research Centre of Schmidt Institute of Physics of the Earth, Russian Academy of Scienc-es
Journal: Geophysical research
Tome: 24
Number: 1
Year: 2023
Pages: 44-60
UDK: 550.837+550.832.7+550.822.7+553.048+539.217.1
DOI: 10.21455/gr2023.1-3
Full text
Keywords: prediction, porosity, water saturation, electrical resistivity, magnetotelluric sounding, laboratory measurements, artificial neural network.
Аnnotation: Porosity prediction beyond wells is carried out on the basis of an artificial neural network trained on the corre-spondence between mercury porosimetry data and electrical resistivity obtained from the results of magne-totelluric sounding in the vicinity of the well. Models of open porosity and water saturation in the sedimentary cover up to a depth of 2 km are constructed based on the results of profile magnetotelluric sounding in the area of the geothermal zone of Soulz-sous-Foret (France), as well as laboratory measurements of porosity on rock samples from a borehole. A comparative analysis of the prediction accuracy in wells for which there were porosity data measured by direct and indirect methods showed that it is inappropriate to build a section porosity model using mixed data. At the same time, the accuracy of the electromagnetic prediction of the porosity obtained using an artifi-cial neural network calibrated on the data measured by mercury porosimetry is approximately three times higher than the accuracy of porosity estimation by indirect methods (in particular, by neutron gamma ray log-ging method). A new approach to constructing a water saturation section is proposed. It is based on a comparison of the total open porosity with the fluid porosity, which was estimated taking into account the dependence of the electrical conductivity of the fluid on the temperature distribution in the section.
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