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A New Approach for Electromagnetic Log Prediction Using Electrical Logs, South California | ||
فیزیک زمین و فضا | ||
مقاله 5، دوره 49، شماره 4، اسفند 1402، صفحه 57-67 اصل مقاله (1.48 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jesphys.2023.361204.1007534 | ||
نویسندگان | ||
Saeed Aftab؛ Ahsan Leisi؛ Navid Shad Manaman* ؛ Rasoul Hamidzadeh Moghadam | ||
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
چکیده | ||
Well logging data shows the change of physical properties of rocks and fluids in lithology units with depth. Well logging is one of the main parts of natural resources exploration. But in some cases, due to the lack of geophysical equipment or due to high exploration costs, it is not possible to record some geophysical logs. In this paper, electromagnetic log predicted using electrical logs for the first time. In such cases, estimating the desired log using other geophysical logs is a suitable solution. For the estimation of geophysical logs, machine learning algorithms are used in most cases. In this research, a new strategy developed for processing and preparation of geophysical logs. This strategy consists of three parts: data smoothing, correlation intensifier, and MLR (Multiple Linear Regression) or ANN (Artificial Neural Network). The purpose of the data smoothing and correlation intensifier section is to remove outliers and identify the pattern of main changes in the log data, and as a result, the accuracy in estimating the target log increases. In this article, the determination of the electromagnetic log has been done using electric logs. The well logging data have been recorded in Southern California and the Central Valley. A total of six wells have been selected, four wells for MLR and ANN training and two wells for testing. By applying data smoothing and correlation intensifier to these data, the correlation between electrical and electromagnetic data increased significantly and caused the estimation accuracy of electromagnetic log to be above 95%. The use of this strategy is not limited to the estimation of electromagnetic log and can be used in all well logging data. | ||
کلیدواژهها | ||
Electromagnetic Log؛ Groundwater؛ Well Logging؛ Data Smoothing؛ South California | ||
مراجع | ||
Aftab, S., & Hamidzadeh Moghadam, R. (2022). Robust data smoothing algorithms and wavelet filter for denoising sonic log signals. Journal of Applied Geophysics, 206, 104836. https://doi.org/10.1016/j.jappgeo.2022.104836 Aftab, S., Hamidzadeh, R., & Leisi, A. (2023a). New interpretation approach of well logging data for evaluation of Kern aquifer in South California. Journal of Applied Geophysics. 215, 105138. https://doi.org/10.1016/j.jappgeo.2023.105138 Aftab, S., Leisi, A., & Kadkhodaie, A. (2023b). Reservoir Petrophysical Index (RPI) as a robust tool for reservoir quality assessment. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01049-w Asfahani, J. (2005). Directional Borehole Logging Configurations Using DC and Telluric Methods for Detecting Deep Conductors Not Intersected by Wells. Pure and Applied Geophysics, 162, 2523–2556. https://doi.org/10.1007/s00024-005-2785-5 Bryan, B. K. (1940). Contributions to the geography of Egypt. Geological Magazine, 77(4), 334–335. https://doi.org/10.1017/S0016756800071454 Davis, G., Green, J. H., Olmsted, F. H., & Brown, D. W. (1959). Ground-Water Conditions and Storage Capacity in the San Joaquin Valley California. Geological Survey Water-Supply Paper 1469. Donaldson, E. C. (1989). Well logging for earth scientists. Journal of Petroleum Science and Engineering (Vol. 2, Issue 4). https://doi.org/10.1016/0920-4105(89)90013-2 Fiordelisi, A., Berto, R., Brambilla, F., & Casini, M. (2020). Advanced Well-Log Analysis in Geothermal Wells for Fracture Identification. May. https://doi.org/10.3997/2214-4609-pdb.5.c038 Folch, A., del Val, L., Luquot, L., Martínez-Pérez, L., Bellmunt, F., Le Lay, H., Rodellas, V., Ferrer, N., Palacios, A., Fernández, S., Marazuela, M. A., Diego-Feliu, M., Pool, M., Goyetche, T., Ledo, J., Pezard, P., Bour, O., Queralt, P., Marcuello, A., Garcia-Orellana, J., & Carrera, J. (2020). Combining fiber optic DTS, cross-hole ERT and time-lapse induction logging to characterize and monitor a coastal aquifer. Journal of Hydrology, 588(May), 125050. https://doi.org/10.1016/j.jhydrol.2020.125050 Hsieh, B. Z., Lewis, C., & Lin, Z. S. (2005). Lithology identification of aquifers from geophysical well logs and fuzzy logic analysis: Shui-Lin Area, Taiwan. Computers and Geosciences, 31(3), 263–275. https://doi.org/10.1016/j.cageo.2004.07.004 Johansson, R. (2018). Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Second Edition. https://doi.org/10.1007/978-1-4842-4246-9 Kaufman, A., & Itskovich, G. (2017). Basic Principles of Induction Logging. https://doi.org/10.1016/c2014-0-01182-3 Kheirollahi, H., Shad Manaman, N., & Leisi, A. (2023). Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms. Journal of Applied Geophysics, 211, 104971. https://doi.org/10.1016/j.jappgeo.2023.104971 Köhn, J., Kruse, E. E., & Santos, J. E. (2002). Hydrogeologic behavior of an alluvial aquifer, Salta Province, Argentina: Simulations of hydraulic conductivity field, groundwater flow, and chloride migration. Natural Resources Research, 11(3), 157–166. https://doi.org/10.1023/A:1019822820426 Leisi, A., Kheirollahi, H., & Shadmanaman, N. (2022). Investigation and comparison of conventional methods for estimating shear wave velocity from well logging data in one of the sandstone reservoirs in southern Iran. Iranian Gournal of Geophysics. https://doi.org/10.30499/IJG.2022.320098.1385 Leisi, A., & Saberi, M. R. (2022). Petrophysical parameters estimation of a reservoir using integration of wells and seismic data : a sandstone case study. Earth Science Informatics, 1–16. https://doi.org/10.1007/s12145-022-00902-8 Liu, H. (2017). Principles and Applications of Well Logging. https://doi.org/10.1007/978-3-662-54977-3 Mendenhall, W. C., Dole, R. B., & Stabler, H. (1916). Ground Water in San Joaquin Valley, California. United States Geological Survey. Metzger, L. F., & Izbicki, J. A. (2013). Electromagnetic-Induction Logging to Monitor Changing Chloride Concentrations. GroundWater, 51(1), 108–121. https://doi.org/10.1111/j.1745-6584.2012.00944.x Mosaad, S., & Basheer, A. A. (2020). Utilizing the Geophysical and Hydrogeological Data for the Assessment of the Groundwater Occurrences in Gallaba Plain , Western Desert , Egypt. Pure and Applied Geophysics. https://doi.org/10.1007/s00024-019-02414-x Novo, S. M., Silva, L. C., & Teixeira, F. L. (2008). Comparison of Coupled-Potentials and Field-Based Finite-Volume Techniques for Modeling of Borehole EM Tools. IEEE Geoscience And Remote Sensing Letters. 5(2). Olmsted, F. H., & Davis, G. H. (1961). Geologic features and ground-water storage capacity of the Sacramento Valley, California. U.S. Geological Survey Water-Supply Paper 1497, 241. Page, R. W. (1983). Geology of the tulare formation and other continental deposits, kettleman city area, san joaquin valley, california, with a section on ground-water management considerations and use of texture maps. U.S. Geological Survey, Water-Resources Investigations Report 83-4000. Pant, P. R., & Gupta, D. (1998). Simple Electrical Logging Technique for Base Metal Exploration. Pure and Applied Geophysics, 152(12), 759–772. Planert, M., & Williams, J. S. (1995). Ground Water Atlas of the United States: Segment 1 California, Nevada. Hydrologic Investigations Atlas 730-B, 30. Prasad, R. G. (2018). Well Logging Importance in Oil and Gas Exploration and Production. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6(I), 2154–2161. https://doi.org/www.ijraset.com Prothero, D. R. (2017). Californias amazing geology. CRC. Qin, Z., Wu, D., Luo, S., Ma, X., Huang, K., Tian, F., Xiao, K., Chen, X., Hou, M., & Pan, H. (2020). A Novel Method to Obtain Permeability in a Dual-Pore System Using Geophysical Logs: A Case Study of an Upper Triassic Formation, Southwest Ordos Basin, China. Natural Resources Research, 29(4), 2619–2634. https://doi.org/10.1007/s11053-019-09612-3 Rasouli, F. S., & Masoudi, S. F. (2020). Effect of modeling porous media on the response of gamma-gamma well-logging tool. Scientific Reports, 10(1), 1–10. https://doi.org/10.1038/s41598-020-63323-x Revil, A., Jardani, A., Sava, P., & Haas, A. (2015). The Seismoelectric Method: Theory and applications. https://doi.org/10.1002/9781118660270 Robinson, D. A., Abdu, H., Lebron, I., & Jones, S. B. (2012). Imaging of hill-slope soil moisture wetting patterns in a semi-arid oak savanna catchment using time-lapse electromagnetic induction. Journal of Hydrology, 416–417, 39–49. https://doi.org/10.1016/j.jhydrol.2011.11.034 Senosy, A. H., Ewida, H. F., Soliman, H. A., & Ebraheem, M. O. (2020). Petrophysical analysis of well logs data for identification and characterization of the main reservoir of Al Baraka Oil Field, Komombo Basin, Upper Egypt. SN Applied Sciences, 2(7), 1–14. https://doi.org/10.1007/s42452-020-3100-x Stewart, M., & Hermeston, S. (1990). Monitoring saltwater interfaces in PVC-cased boreholes using induction logs. Southwest Florida Water Management District, Project Report, Brooksville FL. Tixier, M. P, Alger, R. P. (1970). Log evaluation of nonmetallic mineral deposits. Geophysics, 35(1), 124_142. Wentworth, C. M., Fisher, G. R., Levine, P., & Jachens, R. C. (1995). The surface of crystalline basement, great valley and sierra nevada, california: a digital map database. Department of the interior U.S. Geological Survey. Xing, G., Wang, H., & Ding, Z. (2008). A New Combined Measurement Method of the Electromagnetic Propagation Resistivity Logging. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. 5(3). Zhang, Z., Chunduru, R. K., & Jervis, M. A. (2000). Determining bed boundaries from inversion of EM logging data using general measures of model structure and data misfit. Geophysics, 65(1), 76–82. https://doi.org/10.1190/1.1444727 | ||
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