کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8125561 | 1522781 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A method for identifying the thin layer using the wavelet transform of density logging data
ترجمه فارسی عنوان
یک روش برای شناسایی لایه نازک با استفاده از تبدیل موجک داده های تراکم
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کلمات کلیدی
تبدیل موجک، ورودی تراکم، موجک مادر، سطح تجزیه، شناسایی لایه نازک،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
زمین شناسی اقتصادی
چکیده انگلیسی
In the late stage of oilfield development, thin reservoirs become particularly important for oil and gas exploration. However, current density logging, as a primary method of reservoir identification, has a lower resolution in identifying thin-layers. In this study, a discrete wavelet transform (DWT) is utilized in density logs to identify thin-layers. By adopting different Daubechies (dbN) wavelets and decomposition levels, we analyze the approximation coefficients (cA) and detailed coefficients (cD) and identify the thin-layer signal from detailed coefficients. And then, we reconstruct a new density curve with enhanced thin-layer signal for identifying the thin layer. Results show that db4 wavelet and 3 level are the optimum mother wavelet and decomposition level for the density logging. Detailed coefficients (cD3) from 3rd level decomposition are highly consistent with the thin-layer information, which is suitable for thin-layer identification. Besides, the reconstructed density curve shows a higher thin-layer resolution. This method is successfully applied in the oilfield, and the thin-layer resolution of density curve is improved from 30Â cm to 15Â cm in accordance with microspherically focused logging (RXO).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Petroleum Science and Engineering - Volume 160, January 2018, Pages 433-441
Journal: Journal of Petroleum Science and Engineering - Volume 160, January 2018, Pages 433-441
نویسندگان
Quanying Zhang, Feng Zhang, Juntao Liu, Xinguang Wang, Qian Chen, Liang Zhao, Lili Tian, Yang Wang,