این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Mining and Environment، جلد ۱۶، شماره ۵، صفحات ۱۷۵۹-۱۷۸۰
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی An Approach for Estimation of Uniaxial Compressive Strength and Internal Friction Angle using Well Log Data and Deep Learning Algorithms
چکیده انگلیسی مقاله The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and CNN + LSTM (CL) by well log and core test data of one Iranian hydrocarbon field. As only 12 UCS and 6 FR core tests of single well in this field were available, they were firstly calculated, and then generalized to other depths using two newly derived equations and relevant logs. Next, the effective input logs' data for predicting these parameters have been selected by an auto-encoder DL method, and finally, the values of UCS and φ angle were predicted by the MLP, LSTM, CNN, and CL networks. The efficiency of these four prediction models was then evaluated using a blind dataset, and a range of statistical measures applied to training, testing, and blind datasets. Results show that all four models achieve satisfactory prediction accuracy. However, the CL model outperformed the others, yielding the lowest RMSE of 1.0052 and the highest R² of 0.9983 for UCS prediction, along with an RMSE of 0.0201 and R² of 0.9917 for φ angle prediction on the blind dataset. These findings highlight the high accuracy of deep learning algorithms, particularly the CL algorithm, which demonstrates superior precision compared to the MLP method.
کلیدواژه‌های انگلیسی مقاله Mechanical parameters,Log data,DL models,Core data,Feature Selection
نویسندگان مقاله Farhad Mollaei |
School of Mining, College of Engineering, University of Tehran, Tehran, Iran

Ali Moradzadeh |
School of Mining, College of Engineering, University of Tehran, Tehran, Iran

Reza Mohebian |
School of Mining, College of Engineering, University of Tehran, Tehran, Iran

نشانی اینترنتی https://jme.shahroodut.ac.ir/article_3330_2cd2b4d64f6db4db7e4ffde40a5ec54f.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات