کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5782155 | 1637144 | 2017 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An improved model for estimating the TOC in shale formations
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
زمین شناسی اقتصادی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Determination of the total organic carbon (TOC) from well logs is an important step in formation evaluation of shale reservoirs. The ÎlogR model is one of the most commonly used methods in determining the TOC of the source rocks. Wang et al. (2016) made three revisions on ÎlogR model to enhance the prediction accuracy, one of which is to use a changeable slope of log10Rt versus porosity logs to replace a fixed value. Based on the original ÎlogR model and Wang's revision, we propose an improved model for a better TOC estimation in this paper. Specifically, the approximate linear baseline is replaced by a theoretical one. Under this condition, both the slope and resistivity and porosity log values of the baselined rocks vary from depth to depth. The determination of theoretical baseline is presented, and the varied baseline log values are obtained by a simple mathematical technique. In addition to the shale play of Western Canada Sedimentary Basin, two study areas of Sichuan Basin and Ordos Basin in China, were used to assess the applications of the proposed model and its improvement over the original model. The analysis and discussions presented in this paper indicate that the improved model offers more reliable results in shale plays with large lithological variations. Also, the TOC content of the three study areas was estimated using the proposed model. The results showed that the TOC values estimated from well logs and core analysis are in good agreement.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Marine and Petroleum Geology - Volume 83, May 2017, Pages 174-183
Journal: Marine and Petroleum Geology - Volume 83, May 2017, Pages 174-183
نویسندگان
Peiqiang Zhao, Huolin Ma, Vamegh Rasouli, Wenhui Liu, Jianchao Cai, Zhenhua Huang,