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ПОСТРОЕНИЕ РАЗРЕЗОВ ПОРИСТОСТИ И ВОДОНАСЫЩЕННОСТИ ПО ДАННЫМ ЭЛЕКТРОМАГНИТНЫХ ЗОНДИРОВАНИЙ И ИЗМЕРЕНИЙ В СКВАЖИНАХ
Центр геоэлектромагнитных исследований Института физики Земли им. О.Ю. Шмидта РАН
Журнал: Геофизические исследования
Том: 24
Номер: 1
Год: 2023
Страницы: 44-60
УДК: 550.837+550.832.7+550.822.7+553.048+539.217.1
DOI: 10.21455/gr2023.1-3
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Спичак В.В., Гойдина
А.Г. ПОСТРОЕНИЕ РАЗРЕЗОВ ПОРИСТОСТИ И ВОДОНАСЫЩЕННОСТИ ПО ДАННЫМ ЭЛЕКТРОМАГНИТНЫХ ЗОНДИРОВАНИЙ И ИЗМЕРЕНИЙ В СКВАЖИНАХ
// Геофизические исследования. 2023. Т. 24. № 1. С. 44-60. DOI: 10.21455/gr2023.1-3
@article{СпичакПОСТРОЕНИЕ2023,
author = "Спичак, В. В. and Гойдина
, А. Г.",
title = "ПОСТРОЕНИЕ РАЗРЕЗОВ ПОРИСТОСТИ И ВОДОНАСЫЩЕННОСТИ ПО ДАННЫМ ЭЛЕКТРОМАГНИТНЫХ ЗОНДИРОВАНИЙ И ИЗМЕРЕНИЙ В СКВАЖИНАХ
",
journal = "Геофизические исследования",
year = 2023,
volume = "24",
number = "1",
pages = "44-60",
doi = "10.21455/gr2023.1-3",
language = "Russian"
}
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Ключевые слова: прогноз, пористость, водонасыщенность, удельное электрическое сопротивление, магнитотеллурическое зондирование, лабораторные измерения, искусственная нейросеть.
Аннотация: Прогноз пористости вне скважин проводится на основе искусственной нейросети, обучен-ной на соответствии данных ртутной порометрии и электрического сопротивления, полученного по результатам магнитотеллурического зондирования в окрестности скважины. Построены модели открытой пористости и водонасыщенности в осадочном чехле до глубины 2 км на основе результатов профильного магнитотеллурического зондирования на участке геотермальной зоны Сульц-су-Форе (Франция), а также лабораторных измерений пористости на образцах пород из скважины.
Сравнительный анализ точности прогнозов в скважинах, по которым имелись данные пористости, измеренные прямым и косвенным способом, показал, что построение модели пористости разреза нецелесообразно проводить по данным смешанного типа. В то же время, точность электромагнитного прогноза пористости, полученная с помощью искусственной нейросети, калиброванной на измеренных методом ртутной порометрии данных, примерно в три раза выше, чем точность оценки пористости косвенными методами (в частности,
методом нейтронного гамма-каротажа).
Предложен новый подход к построению разреза водонасыщенности. Он основан на сравнении общей открытой пористости с флюидной пористостью, оценка которой была выполнена с учётом зависимости электропроводности флюида от распределения темпера-туры в разрезе.
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Bhatt A., Helle H.B. Committee neural networks for porosity and permeability prediction from well logs // Geophysical Prospecting. 2002. V. 50. P.645–660.
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Calderon J.E., Castagna J. Porosity and lithologic estimation using rock physics and multi-attribute transforms in Balcon Field, Colombia // The Leading Edge. 2007. V. 26, N 2. P.142–150.
Chatterjee R., Singha D., Ojha M., Sen M. K., Sain K. Porosity estimation from pre-stack seismic da-ta in gas-hydrate bearing sediments, Krishna-Godavari basin, India // Journal of Natural Gas Sci-ence and Engineering. 2016. V. 33. P.562–572.
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De With G., Glass H.J. Reliability and Reproducibility of Mercury Intrusion Porosimetry // J. Europ. Cer. Soc. 1997. V. 17. P.753–757.
Dolberg D.M., Helgesen J., Hanssen T.H., Magnus I., Saigal G., Pedersen B.K. Porosity prediction from seismic inversion, Lavrans Field, Halten Terrace, Norway // The Leading Edge. 2000. V. 19, N 4. P.392–399.
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Geiermann J., Schill E. 2-D Magnetotellurics at the geothermal site at Soultz-sous-Forêts: Resistivity distribution to about 3000 m depth // Comptes Rendus Geoscience. 2010. V. 342. P.587–599.
Genter A., Castaing C., Dezayes C., Tenzer H., Traineau H., Villemin T. Comparative analysis of di-rect (core) and indirect (borehole imaging tools) collection of fracture data in the Hot Dry Rock Soultz reservoir (France) // J. Geophys. Res.: Solid Earth. 1997. V. 102. P.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. V. 10. P.1113–1124. https://doi.org/10.1016/j.gsf.2018.07.002
Guo H., Keppler H. Electrical Conductivity of NaCl – Bearing Aqueous Fluids to 900 °C and 5 GPa // J. Geophys. Res.: Solid Earth. 2019. V. 124. P.1397–1411.
Hashin Z., Shtrikman S. A variational approach to the theory of effective magnetic permeability of multiphase materials // J. Appl. Phys. 1962. V. 33. P.3125–3131.
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Hayley K., Bentley L.R., Gharibi M., Nightingale M. Low temperature dependence of electrical resis-tivity: Implications for near surface geophysical monitoring // Geophys. Res. Lett. 2007. V. 34, L18402. 5 p. doi: 10.1029/2007GL031124
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Kalkomey C.T. Potential risks when using seismic attributes as predictors of reservoir properties // The Leading Edge. 1997. V. 16, N 3. P.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. V. 28. P.356–364. https://doi.org/10.1016/j.jngse.2015.12.028
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Mota R., Monteiro Santos F.A. 2D sections of porosity and water saturation from integrated resistivi-ty and seismic surveys // Near Surface Geophysics. 2010. V. 8. P.575–584.
Pan R., Ma X. An Approach to Reserve Estimation Enhanced with 3-D Seismic Data // Nonrenewa-ble Resources. 1997. V. 6. P.251–255.
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Cпичак В.В., Гойдина А.Г. Нейросетевая оценка сейсмических скоростей и удельного сопро-тивления пород по данным электромагнитного и сейсмического зондирования // Физика Земли. 2016. № 3. C.38–49.
Спичак В.В., Захарова О.К. Электромагнитный геотермометр. М.: Научный мир, 2013. 172 с.
Спичак В.В., Захарова О.К. Прогноз пористости на глубину ниже забоя скважин по данным электромагнитных зондирований и электрокаротажа // Геофизика. 2015. № 6. С.43–49.
Спичак В.В., Захарова О.К. Электромагнитный прогноз проницаемости вне скважин // Геофи-зические исследования. 2022. Т. 23, № 2. С.18–38.
Adekanle A., Enikanselu P.A. Porosity Prediction from Seismic Inversion Properties over ‘XLD’ Field, Niger Delta // Amer. J. Scient. Indust. Res. 2013. V. 4, N 1. P.31–35.
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. V. 3. P.66–71.
Ahmadi M.A., Chen Z. Comparison of machine learning methods for estimating permeability and po-rosity of oil reservoirs via petrophysical logs // Petroleum. 2019. V. 5. P.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. V. 7, N 4. P.1743–1754.
Alimoradi A., Moradzadeh A., Bakhtiari M.R. Methods of water saturation estimation: Historical per-spective // J. Petroleum and Gas Eng. 2011. V. 2, N 3. P.45–53.
Angeleri P., Carpi R. Porosity Prediction from Seismic Data // Geophysical Prospecting. 1982. V. 30. P.580–607.
Anovitz L.M., Cole D.R. Characterization and Analysis of Porosity and Pore Structures // Reviews in Mineralogy & Geochemistry. 2015. V. 80. P.61–164.
Archie G.E. The electrical resistivity log as an aid in determining some reservoir characteristics. // Amer. Inst. Mining Metall. Eng. Trans. 1942. V. 146. P.54–62.
Baouche R., Baddari K. Prediction of permeability and porosity from well log data using the nonpar-ametric regression with multivariate analysis and neural network // Egyptian Journal of Petrole-um. 2017. V. 26. P.763–778.
Berryman J.G., Berge P.A., Bonner B.P. Estimating rock porosity and fluid saturation using only seismic velocities // Geophysics. 2002. V. 67, N 2. P.391–404.
Bhatt A., Helle H.B. Committee neural networks for porosity and permeability prediction from well logs // Geophysical Prospecting. 2002. V. 50. P.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 plural-ism // Geomechanical and Petrophysical Properties of Mudrocks // Geological Society, London, Special Publications. 2017. V. 454. P.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. V. 26, N 2. P.142–150.
Chatterjee R., Singha D., Ojha M., Sen M. K., Sain K. Porosity estimation from pre-stack seismic da-ta in gas-hydrate bearing sediments, Krishna-Godavari basin, India // Journal of Natural Gas Sci-ence and Engineering. 2016. V. 33. P.562–572.
Clarkson C.R., Solano N., Bustin R.M., Bustin A.M.M., Chalmers G.R.L., Hec L., Melnichenko Y.B., Radlinski A.P., Blach T.P. Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion // Fuel. 2013. V. 103. Р.606–616.
Cosentini R.M., Foti S. Evaluation of porosity and degree of saturation from seismic and electrical data // Geotechnique. 2014. V. 64. P.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 Con-gress. 2005. P.24–29.
De With G., Glass H.J. Reliability and Reproducibility of Mercury Intrusion Porosimetry // J. Europ. Cer. Soc. 1997. V. 17. P.753–757.
Dolberg D.M., Helgesen J., Hanssen T.H., Magnus I., Saigal G., Pedersen B.K. Porosity prediction from seismic inversion, Lavrans Field, Halten Terrace, Norway // The Leading Edge. 2000. V. 19, N 4. P.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. Ge-ophys. Res.: Solid Earth. 1985. V. 90. P.10136–10150.
Geiermann J., Schill E. 2-D Magnetotellurics at the geothermal site at Soultz-sous-Forêts: Resistivity distribution to about 3000 m depth // Comptes Rendus Geoscience. 2010. V. 342. P.587–599.
Genter A., Castaing C., Dezayes C., Tenzer H., Traineau H., Villemin T. Comparative analysis of di-rect (core) and indirect (borehole imaging tools) collection of fracture data in the Hot Dry Rock Soultz reservoir (France) // J. Geophys. Res.: Solid Earth. 1997. V. 102. P.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. V. 10. P.1113–1124. https://doi.org/10.1016/j.gsf.2018.07.002
Guo H., Keppler H. Electrical Conductivity of NaCl – Bearing Aqueous Fluids to 900 °C and 5 GPa // J. Geophys. Res.: Solid Earth. 2019. V. 124. P.1397–1411.
Hashin Z., Shtrikman S. A variational approach to the theory of effective magnetic permeability of multiphase materials // J. Appl. Phys. 1962. V. 33. P.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 resis-tivity: Implications for near surface geophysical monitoring // Geophys. Res. Lett. 2007. V. 34, L18402. 5 p. doi: 10.1029/2007GL031124
Jorgensen D.G. Using geophysical logs to estimate porosity, water resistivity, and intrinsic permea-bility. USGS water-supply paper 2321. Denver, Colorado, USA: US Geological Survey, US De-partment of the Interior, 1988. 30 p.
Kalkomey C.T. Potential risks when using seismic attributes as predictors of reservoir properties // The Leading Edge. 1997. V. 16, N 3. P.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. V. 28. P.356–364. https://doi.org/10.1016/j.jngse.2015.12.028
Kummerow J., Raab S. Temperature dependence of electrical resistivity - Part I: Experimental inves-tigations of hydrothermal fluids // Energy Procedia. 2015. V. 76. P.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 pre-dict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, south-east New Mexico // Geophysics. 2001. V. 66. P.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. V. 59, N 1. P.39–46.
Mota R., Monteiro Santos F.A. 2D sections of porosity and water saturation from integrated resistivi-ty and seismic surveys // Near Surface Geophysics. 2010. V. 8. P.575–584.
Pan R., Ma X. An Approach to Reserve Estimation Enhanced with 3-D Seismic Data // Nonrenewa-ble Resources. 1997. V. 6. P.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. V. 72, N 2. P.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 Univer-site Louis Pasteur Strasbourg, France. 2007. 208 p.
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