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فیزیک زمین و فضا، جلد ۴۹، شماره ۴، صفحات ۵۷-۶۷
عنوان فارسی A New Approach for Electromagnetic Log Prediction Using Electrical Logs, South California
چکیده فارسی مقاله 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.
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی A New Approach for Electromagnetic Log Prediction Using Electrical Logs, South California
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Electromagnetic Log, Groundwater, well logging, Data Smoothing, South California
نویسندگان مقاله Saeed Aftab |
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.

Ahsan Leisi |
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.

Navid Shad Manaman |
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.

Rasoul Hamidzadeh Moghadam |
Department of Mining Exploration, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.

نشانی اینترنتی https://jesphys.ut.ac.ir/article_94160_9e39361fae40db0314f87ff5ec422137.pdf
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