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CONSTRUCTION OF SECTIONS OF POROSITY AND WATER SATURATION ACCORDING TO THE ELECTROMAGNETIC SOUNDING DATA AND MEASUREMENTS IN WELLS
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
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Spichak
A.G V.V. CONSTRUCTION OF SECTIONS OF POROSITY AND WATER SATURATION ACCORDING TO THE ELECTROMAGNETIC SOUNDING DATA AND MEASUREMENTS IN WELLS
// . 2023. Т. 24. № 1. С. 44-60. DOI: 10.21455/gr2023.1-3
@article{Spichak
A.GCONSTRUCTION2023,
author = "Spichak
A.G, V. V.",
title = "CONSTRUCTION OF SECTIONS OF POROSITY AND WATER SATURATION ACCORDING TO THE ELECTROMAGNETIC SOUNDING DATA AND MEASUREMENTS IN WELLS
",
journal = "Geophysical research",
year = 2023,
volume = "24",
number = "1",
pages = "44-60",
doi = "10.21455/gr2023.1-3",
language = "English"
}
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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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Ahmadi M.A., Chen Z., Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petrophysical logs, Petroleum, 2019, vol. 5, pp. 271-284.
Ali G.H., Tawfeeq Y.J., Najmuldeen M.Y., Comparative estimation of water saturation in carbonate reservoir: A case study of northern Iraq, Periodicals of Engineering and Natural Sciences, 2019, vol. 7, no. 4, pp. 1743-1754.
Alimoradi A., Moradzadeh A., Bakhtiari M.R., Methods of water saturation estimation: Historical perspective, J. Petroleum and Gas Eng., 2011, vol. 2, no. 3, pp. 45-53.
Angeleri P., Carpi R., Porosity Prediction from Seismic Data, Geophysical Prospecting, 1982, vol. 30, pp. 580-607. DOI: https://doi.org/10.1111/j.1365-2478.1982.tb01328.x
Anovitz L.M., Cole D.R., Characterization and Analysis of Porosity and Pore Structures, Reviews in Mineralo-gy & Geochemistry, 2015, vol. 80, pp. 61-164.
Archie G.E., The electrical resistivity log as an aid in determining some reservoir characteristics, Amer. Inst. Mining Metall. Eng. Trans., 1942, vol. 146, pp. 54-62.
Baouche R., Baddari K., Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Egyptian Journal of Petroleum, 2017, vol. 26, pp. 763-778.
Berryman J.G., Berge P.A., Bonner B.P., Estimating rock porosity and fluid saturation using only seismic ve-locities, Geophysics, 2002, vol. 67, no. 2, pp. 391-404.
Bhatt A., Helle H.B., Committee neural networks for porosity and permeability prediction from well logs, Ge-ophysical Prospecting, 2002, vol. 50, pp. 645-660.
Busch A., Schweinar K., Kampman N., Coorn A.B., Pipich V., Feoktystov A., Leu L., Amann-Hildenbrand A., Bertier P., Determining the porosity of mudrocks using methodological pluralism, Geomechanical and Petrophysical Properties of Mudrocks. Geological Society, London, Special Publications, 2017, vol. 454, pp. 15-38. https://doi.org/10.1144/SP454.1
Calderon J.E., Castagna J., Porosity and lithologic estimation using rock physics and multi-attribute transforms in Balcon Field. Colombia, The Leading Edge, 2007, vol. 26, no. 2, pp. 142-150.
Chatterjee R., Singha D., Ojha M., Sen M.K., Sain K., Porosity estimation from pre-stack seismic data in gas-hydrate bearing sediments, Krishna-Godavari basin, India, Journal Natural Gas Science and Engineer-ing, 2016, vol. 33, pp. 562-572.
Clarkson C.R., Solano N., Bustin R.M., Bustin A.M.M., Chalmers G.R.L., Hec L., Melnichenko Y.B., Radlin-ski A.P., Blach T.P., Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion, Fuel, 2013, vol. 103, pp. 606-616.
Cosentini R.M., Foti S., Evaluation of porosity and degree of saturation from seismic and electrical data, Ge-otechnique, 2014, vol. 64, pp. 278-286.
De Boor C.A., Practical Guide to Splines, New York: Springer-Verlag, 1978, 392 p.
Dezayes C., Genter A., Hooijkaas G., Deep-seated geology and fracture system of the EGS Soultz reservoir (France) based on recent 5km depth boreholes, Proceedings World Geothermal Congress, 2005, pp. 24-29.
De With G., Glass H.J., Reliability and Reproducibility of Mercury Intrusion Porosimetry, J. Europ. Cer. Soc., 1997, vol. 17, pp. 753-757.
Dolberg D.M., Helgesen J., Hanssen T.H., Magnus I., Saigal G., Pedersen B.K., Porosity prediction from seis-mic inversion, Lavrans Field, Halten Terrace, Norway, The Leading Edge, 2000, vol. 19, no. 4, pp. 392-399.
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Flóvenz O.G., Georgsson L.S., Árnason K., Resistivity structure of the upper crust in Iceland, J. Geophys Res.: Solid Earth, 2011, 1985, vol. 90, pp. 10136-10150.
Geiermann J., Schill E., 2-D Magnetotellurics at the geothermal site at Soultz-sous-Forêts: Resistivity distribu-tion to about 3000 m depth, Comptes Rendus Geoscience, 2010, vol. 342, pp. 587-599.
Genter A., Castaing C., Dezayes C., Tenzer H., Traineau H., Villemin T., Comparative analysis of direct (core) and indirect (borehole imaging tools) collection of fracture data in the Hot Dry Rock Soultz reservoir (France), J. Geophys. Res.: Solid Earth, 1997, vol. 102, pp. 15419-15431.
Gogoi T., Chatterjee R., Estimation of Petrophysical Parameters using Seismic Inversion and Neural Network Modeling in Upper Assam Basin, India, Geoscience Frontiers, 2019, vol. 10, pp. 1113-1124.
Guo H., Keppler H., Electrical Conductivity of NaCl – Bearing Aqueous Fluids to 900 °C and 5 GPa, J. Ge-ophys. Res.: Solid Earth, 2019, vol. 124, pp. 1397-1411.
Hashin Z., Shtrikman S., A variational approach to the theory of effective magnetic permeability of multiphase materials, J. Appl. Phys., 1962, vol. 33, pp. 3125-3131.
Haykin S., Neural networks: a comprehensive foundation, New York, Prentice Hall, 1999, 842 p.
Hayley K., Bentley L.R., Gharibi M., Nightingale M., Low temperature dependence of electrical resistivity: Implications for near surface geophysical monitoring, Geophys. Res. Lett., 2007, vol. 34, L18402, 5 p. doi: 10.1029/2007GL031124
Jorgensen D.G., Using geophysical logs to estimate porosity, water resistivity, and intrinsic permeability. USGS water-supply paper 2321, Denver, Colorado, USA: US Geological Survey, US Department of the Interior, 1988, 30 p.
Kalkomey C.T., Potential risks when using seismic attributes as predictors of reservoir properties, The Leading Edge, 1997, vol. 16, no. 3, pp. 247-251.
Kumar R., Das B., Chatterjee R., Sain K., A Methodology of Porosity Estimation from Inversion of Post-Stack Seismic Data, J. Natural Gas Science and Engineering, 2016, vol. 28, pp. 356-364.
Kummerow J., Raab S., Temperature dependence of electrical resistivity - Part I: Experimental investigations of hydrothermal fluids, Energy Procedia, 2015, vol. 76, pp. 240-246.
Ledesert B., Fracturation et paleocirculations hydrothermales. Application au granite de Soultz-sous-Forêts, Ph.D. Dissertation, Poitiers: These Universite de Poitiers, France, 1993, 219 p.
Leiphart D.J., Hart B.S., Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico, Ge-ophysics, 2001, vol. 66, pp. 1349-1358.
Log Interpretation Chartbook. Schlumberger Doll Research, 2009. URL: http://www.epgeology.com/gallery/
image_page.php?album_id=3&image_id=125
Malvic T., Prskalo S., Significance of the amplitude attribute in porosity prediction. Drava Depression Case Study, Nafta, 2008, vol. 59, no. 1, pp. 39-46.
Mota R., Monteiro Santos F.A., 2D sections of porosity and water saturation from integrated resistivity and seismic surveys, Near Surface Geophysics, 2010, vol. 8, pp. 575-584.
Pan R., Ma X., An Approach to Reserve Estimation Enhanced with 3-D Seismic Data, Nonrenewable Re-sources, 1997, vol. 6, pp. 251-255.
Quist A.S., Marshall W.L., Electrical conductances of aqueous sodium chloride solutions from 0 to 800 C and at pressures to 4000 bars, J. Phys. Chem., 1968, vol. 72, no. 2, pp. 684-703.
Rosener M., Etude petrophysique et modelisation des transferts thermiques entre roche et fluide dans le contexte geothermique de Soultz-sous-Forêts, Ph.D. Dissertation, Strasbourg: These Universite Louis Pasteur Strasbourg, France, 2007, 208 p.
Sanjuan B., Millot R., Dezayes C., Brach M., Main characteristics of the deep geothermal brine (5 km) at Soultz-sous-Forêts (France) determined using geochemical and tracer test data, Comptes Rendus Geosci-ence, 2010, vol. 342, pp. 546-559.
Sausse J., Dezayes C., Dorbath L., Genter A., Place J., 3D model of fracture zones at Soultz-sous-Forêts based on geological data, image logs, induced microseismicity and vertical seismic profiles, Comptes Rendus Geoscience, 2010, vol. 342, pp. 531-545.
Spichak V.V., Application of electromagnetic techniques for searching, exploration and monitoring of the hy-drocarbons, Geofisika (Geophysics), 2017, no. 6, pp. 33-44. [In Russian].
Spichak V.V., Elektromagnitnaya tomografiya zemnykh nedr (Electromagnetic tomography of the Earth’s inte-rior), Moscow, Nauchnii mir, 2019, 374 p. [In Russian].
Spichak V.V., Geiermann J., Zakharova O., Calcagno P., Genter A., Schill E., Estimating deep temperatures in the Soultz-sous-Forêts geothermal area (France) from magnetotelluric data, Near Surface Geophysics, 2015, vol. 13, pp. 397-408.
Spichak V.V., Goidina A.G., Neural Network Estimate of Seismic Velocities and Resistivity of Rocks from Electromagnetic and Seismic Sounding Data, Izvestiya, Physics of the Solid Earth, 2016, vol. 52, no. 3, pp. 371-381.
Spichak V.V., Zakharova O.K., Elektromagnitnii geotermometr (Electromagnetic geothermometer), Moscow, Nauchnii mir, 2013, 172 p. [In Russian].
Spichak V.V., Zakharova O.K., Porosity forecast at depths below well bootomholes from the electrormagnetic sounding data, Geofizika (Geophysics), 2015, no. 6, pp. 43-49. [In Russian].
Spichak V.V., Zakharova O.K., Electromagnetic forecast of the permeability beyond boreholes, Geofizicheskie issledovaniya (Geophysical Research), 2022, vol. 23, no. 2, pp. 18-38. [In Russian].
Surma F., Determination of the porosity of damaged zones around faults and the role of the state of the materi-al on the properties of fluid-rock exchange: mineralogy, porosity structures, mechanical characteristics, Ph.D. Dissertation, Strasbourg: Theses Universite Louis Pasteur Strasbourg, France, 2003, 142 p.
Ucok H., Temperature dependence of the electrical resistivity of aqueous salt solutions and solution saturated porous rocks, Ph.D. Dissertation, Los Angeles: University of Southern California, USA, 1979, 164 p.
Urang J.G., Ebong E.D., Akpan A.E., Akaerue E.I., A new approach for porosity and permeability prediction from well logs using artificial neural network and curve fitting techniques. A case study of Niger Delta, Nigeria, J. Appl. Geophys., 2020, vol. 183, pp. 104207.
Uyanik O., Estimation of the Porosity of Clay Soils Using Seismic P and S-Wave Velocities, J. Appl. Geophys., 2019, vol. 170, pp. 103832. DOI: https://doi.org/10.1016/j.jappgeo.2019.103832
Van Brakel J., Modry S., Svata M., Mercury Porosimetry: State of the Art, Powder Technology, 1981, vol. 29, pp. 1-12.
Verma A.K., Cheadle B.A., Routray A., Mohanty W.K., Mansinha L., Porosity and Permeability Estimation us-ing Neural Network Approach from Well Log Data, Expanded Abstracts. CSPG/CSEG/CWLS GeoCon-vention 2012, Calgary, Canada, 2014, pp. 1-6.
Vidal J., Genter A., Schmittbuhl J., How do permeable fractures in the Triassic sediments of Northern Alsace characterize the top of hydrothermal convective cells? Evidence from Soultz geothermal boreholes (France), Geothermal Energy, 2015, vol. 3, 28 p. DOI: 10.1186/s40517-015-0026-4
Vuataz F.-D., Brach M., Criaud A., Fouillac C., Geochemical monitoring of drilling fluids: a powerful tool to forecast and detect formation waters, SPE Formation Evaluation, 1990, vol. 5, pp. 177-184.
Wood D.A., Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data, Journal of Petroleum Science and Engi-neering, 2020, vol. 184, 106587, 17 p.
Agbasi O.E., Estimation of Water Saturation Using a Modeled Equation and Archie’s Equation from Wire-Line Logs, Niger Delta Nigeria, IOSR J. Appl. Phys., 2013, vol. 3, pp. 66-71.
Ahmadi M.A., Chen Z., Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petrophysical logs, Petroleum, 2019, vol. 5, pp. 271-284.
Ali G.H., Tawfeeq Y.J., Najmuldeen M.Y., Comparative estimation of water saturation in carbonate reservoir: A case study of northern Iraq, Periodicals of Engineering and Natural Sciences, 2019, vol. 7, no. 4, pp. 1743-1754.
Alimoradi A., Moradzadeh A., Bakhtiari M.R., Methods of water saturation estimation: Historical perspective, J. Petroleum and Gas Eng., 2011, vol. 2, no. 3, pp. 45-53.
Angeleri P., Carpi R., Porosity Prediction from Seismic Data, Geophysical Prospecting, 1982, vol. 30, pp. 580-607. DOI: https://doi.org/10.1111/j.1365-2478.1982.tb01328.x
Anovitz L.M., Cole D.R., Characterization and Analysis of Porosity and Pore Structures, Reviews in Mineralo-gy & Geochemistry, 2015, vol. 80, pp. 61-164.
Archie G.E., The electrical resistivity log as an aid in determining some reservoir characteristics, Amer. Inst. Mining Metall. Eng. Trans., 1942, vol. 146, pp. 54-62.
Baouche R., Baddari K., Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Egyptian Journal of Petroleum, 2017, vol. 26, pp. 763-778.
Berryman J.G., Berge P.A., Bonner B.P., Estimating rock porosity and fluid saturation using only seismic ve-locities, Geophysics, 2002, vol. 67, no. 2, pp. 391-404.
Bhatt A., Helle H.B., Committee neural networks for porosity and permeability prediction from well logs, Ge-ophysical Prospecting, 2002, vol. 50, pp. 645-660.
Busch A., Schweinar K., Kampman N., Coorn A.B., Pipich V., Feoktystov A., Leu L., Amann-Hildenbrand A., Bertier P., Determining the porosity of mudrocks using methodological pluralism, Geomechanical and Petrophysical Properties of Mudrocks. Geological Society, London, Special Publications, 2017, vol. 454, pp. 15-38. https://doi.org/10.1144/SP454.1
Calderon J.E., Castagna J., Porosity and lithologic estimation using rock physics and multi-attribute transforms in Balcon Field. Colombia, The Leading Edge, 2007, vol. 26, no. 2, pp. 142-150.
Chatterjee R., Singha D., Ojha M., Sen M.K., Sain K., Porosity estimation from pre-stack seismic data in gas-hydrate bearing sediments, Krishna-Godavari basin, India, Journal Natural Gas Science and Engineer-ing, 2016, vol. 33, pp. 562-572.
Clarkson C.R., Solano N., Bustin R.M., Bustin A.M.M., Chalmers G.R.L., Hec L., Melnichenko Y.B., Radlin-ski A.P., Blach T.P., Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion, Fuel, 2013, vol. 103, pp. 606-616.
Cosentini R.M., Foti S., Evaluation of porosity and degree of saturation from seismic and electrical data, Ge-otechnique, 2014, vol. 64, pp. 278-286.
De Boor C.A., Practical Guide to Splines, New York: Springer-Verlag, 1978, 392 p.
Dezayes C., Genter A., Hooijkaas G., Deep-seated geology and fracture system of the EGS Soultz reservoir (France) based on recent 5km depth boreholes, Proceedings World Geothermal Congress, 2005, pp. 24-29.
De With G., Glass H.J., Reliability and Reproducibility of Mercury Intrusion Porosimetry, J. Europ. Cer. Soc., 1997, vol. 17, pp. 753-757.
Dolberg D.M., Helgesen J., Hanssen T.H., Magnus I., Saigal G., Pedersen B.K., Porosity prediction from seis-mic inversion, Lavrans Field, Halten Terrace, Norway, The Leading Edge, 2000, vol. 19, no. 4, pp. 392-399.
Ezekwe N., Petroleum Reservoir Engineering Practice, Boston: Pearson Education Inc., 2011, 60 p.
Flóvenz O.G., Georgsson L.S., Árnason K., Resistivity structure of the upper crust in Iceland, J. Geophys Res.: Solid Earth, 2011, 1985, vol. 90, pp. 10136-10150.
Geiermann J., Schill E., 2-D Magnetotellurics at the geothermal site at Soultz-sous-Forêts: Resistivity distribu-tion to about 3000 m depth, Comptes Rendus Geoscience, 2010, vol. 342, pp. 587-599.
Genter A., Castaing C., Dezayes C., Tenzer H., Traineau H., Villemin T., Comparative analysis of direct (core) and indirect (borehole imaging tools) collection of fracture data in the Hot Dry Rock Soultz reservoir (France), J. Geophys. Res.: Solid Earth, 1997, vol. 102, pp. 15419-15431.
Gogoi T., Chatterjee R., Estimation of Petrophysical Parameters using Seismic Inversion and Neural Network Modeling in Upper Assam Basin, India, Geoscience Frontiers, 2019, vol. 10, pp. 1113-1124.
Guo H., Keppler H., Electrical Conductivity of NaCl – Bearing Aqueous Fluids to 900 °C and 5 GPa, J. Ge-ophys. Res.: Solid Earth, 2019, vol. 124, pp. 1397-1411.
Hashin Z., Shtrikman S., A variational approach to the theory of effective magnetic permeability of multiphase materials, J. Appl. Phys., 1962, vol. 33, pp. 3125-3131.
Haykin S., Neural networks: a comprehensive foundation, New York, Prentice Hall, 1999, 842 p.
Hayley K., Bentley L.R., Gharibi M., Nightingale M., Low temperature dependence of electrical resistivity: Implications for near surface geophysical monitoring, Geophys. Res. Lett., 2007, vol. 34, L18402, 5 p. doi: 10.1029/2007GL031124
Jorgensen D.G., Using geophysical logs to estimate porosity, water resistivity, and intrinsic permeability. USGS water-supply paper 2321, Denver, Colorado, USA: US Geological Survey, US Department of the Interior, 1988, 30 p.
Kalkomey C.T., Potential risks when using seismic attributes as predictors of reservoir properties, The Leading Edge, 1997, vol. 16, no. 3, pp. 247-251.
Kumar R., Das B., Chatterjee R., Sain K., A Methodology of Porosity Estimation from Inversion of Post-Stack Seismic Data, J. Natural Gas Science and Engineering, 2016, vol. 28, pp. 356-364.
Kummerow J., Raab S., Temperature dependence of electrical resistivity - Part I: Experimental investigations of hydrothermal fluids, Energy Procedia, 2015, vol. 76, pp. 240-246.
Ledesert B., Fracturation et paleocirculations hydrothermales. Application au granite de Soultz-sous-Forêts, Ph.D. Dissertation, Poitiers: These Universite de Poitiers, France, 1993, 219 p.
Leiphart D.J., Hart B.S., Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico, Ge-ophysics, 2001, vol. 66, pp. 1349-1358.
Log Interpretation Chartbook. Schlumberger Doll Research, 2009. URL: http://www.epgeology.com/gallery/
image_page.php?album_id=3&image_id=125
Malvic T., Prskalo S., Significance of the amplitude attribute in porosity prediction. Drava Depression Case Study, Nafta, 2008, vol. 59, no. 1, pp. 39-46.
Mota R., Monteiro Santos F.A., 2D sections of porosity and water saturation from integrated resistivity and seismic surveys, Near Surface Geophysics, 2010, vol. 8, pp. 575-584.
Pan R., Ma X., An Approach to Reserve Estimation Enhanced with 3-D Seismic Data, Nonrenewable Re-sources, 1997, vol. 6, pp. 251-255.
Quist A.S., Marshall W.L., Electrical conductances of aqueous sodium chloride solutions from 0 to 800 C and at pressures to 4000 bars, J. Phys. Chem., 1968, vol. 72, no. 2, pp. 684-703.
Rosener M., Etude petrophysique et modelisation des transferts thermiques entre roche et fluide dans le contexte geothermique de Soultz-sous-Forêts, Ph.D. Dissertation, Strasbourg: These Universite Louis Pasteur Strasbourg, France, 2007, 208 p.
Sanjuan B., Millot R., Dezayes C., Brach M., Main characteristics of the deep geothermal brine (5 km) at Soultz-sous-Forêts (France) determined using geochemical and tracer test data, Comptes Rendus Geosci-ence, 2010, vol. 342, pp. 546-559.
Sausse J., Dezayes C., Dorbath L., Genter A., Place J., 3D model of fracture zones at Soultz-sous-Forêts based on geological data, image logs, induced microseismicity and vertical seismic profiles, Comptes Rendus Geoscience, 2010, vol. 342, pp. 531-545.
Spichak V.V., Application of electromagnetic techniques for searching, exploration and monitoring of the hy-drocarbons, Geofisika (Geophysics), 2017, no. 6, pp. 33-44. [In Russian].
Spichak V.V., Elektromagnitnaya tomografiya zemnykh nedr (Electromagnetic tomography of the Earth’s inte-rior), Moscow, Nauchnii mir, 2019, 374 p. [In Russian].
Spichak V.V., Geiermann J., Zakharova O., Calcagno P., Genter A., Schill E., Estimating deep temperatures in the Soultz-sous-Forêts geothermal area (France) from magnetotelluric data, Near Surface Geophysics, 2015, vol. 13, pp. 397-408.
Spichak V.V., Goidina A.G., Neural Network Estimate of Seismic Velocities and Resistivity of Rocks from Electromagnetic and Seismic Sounding Data, Izvestiya, Physics of the Solid Earth, 2016, vol. 52, no. 3, pp. 371-381.
Spichak V.V., Zakharova O.K., Elektromagnitnii geotermometr (Electromagnetic geothermometer), Moscow, Nauchnii mir, 2013, 172 p. [In Russian].
Spichak V.V., Zakharova O.K., Porosity forecast at depths below well bootomholes from the electrormagnetic sounding data, Geofizika (Geophysics), 2015, no. 6, pp. 43-49. [In Russian].
Spichak V.V., Zakharova O.K., Electromagnetic forecast of the permeability beyond boreholes, Geofizicheskie issledovaniya (Geophysical Research), 2022, vol. 23, no. 2, pp. 18-38. [In Russian].
Surma F., Determination of the porosity of damaged zones around faults and the role of the state of the materi-al on the properties of fluid-rock exchange: mineralogy, porosity structures, mechanical characteristics, Ph.D. Dissertation, Strasbourg: Theses Universite Louis Pasteur Strasbourg, France, 2003, 142 p.
Ucok H., Temperature dependence of the electrical resistivity of aqueous salt solutions and solution saturated porous rocks, Ph.D. Dissertation, Los Angeles: University of Southern California, USA, 1979, 164 p.
Urang J.G., Ebong E.D., Akpan A.E., Akaerue E.I., A new approach for porosity and permeability prediction from well logs using artificial neural network and curve fitting techniques. A case study of Niger Delta, Nigeria, J. Appl. Geophys., 2020, vol. 183, pp. 104207.
Uyanik O., Estimation of the Porosity of Clay Soils Using Seismic P and S-Wave Velocities, J. Appl. Geophys., 2019, vol. 170, pp. 103832. DOI: https://doi.org/10.1016/j.jappgeo.2019.103832
Van Brakel J., Modry S., Svata M., Mercury Porosimetry: State of the Art, Powder Technology, 1981, vol. 29, pp. 1-12.
Verma A.K., Cheadle B.A., Routray A., Mohanty W.K., Mansinha L., Porosity and Permeability Estimation us-ing Neural Network Approach from Well Log Data, Expanded Abstracts. CSPG/CSEG/CWLS GeoCon-vention 2012, Calgary, Canada, 2014, pp. 1-6.
Vidal J., Genter A., Schmittbuhl J., How do permeable fractures in the Triassic sediments of Northern Alsace characterize the top of hydrothermal convective cells? Evidence from Soultz geothermal boreholes (France), Geothermal Energy, 2015, vol. 3, 28 p. DOI: 10.1186/s40517-015-0026-4
Vuataz F.-D., Brach M., Criaud A., Fouillac C., Geochemical monitoring of drilling fluids: a powerful tool to forecast and detect formation waters, SPE Formation Evaluation, 1990, vol. 5, pp. 177-184.
Wood D.A., Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data, Journal of Petroleum Science and Engi-neering, 2020, vol. 184, 106587, 17 p.
