کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1758273 | 1019155 | 2011 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Computational intelligence for deepwater reservoir depositional environments interpretation
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
Finite state transducerGenetic algorithms - الگوریتم های ژنتیکGenetic programming - برنامه نویسی ژنتیکیSegmentation - تقسیم بندیWell log - خوب وارد شویدTime series - سری زمانیClassification rules - قوانین طبقه بندیDepositional environment - محیط رسوبیFuzzy logic - منطق فازیCo-evolution - هم فرگشتیComputational intelligence - هوش کامپیوتری
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم زمین و سیاره ای (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterize a deepwater reservoir and to predict its producibility is by analyzing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deepwater reservoir characterization. In this framework, one critical task is the identification and labeling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Natural Gas Science and Engineering - Volume 3, Issue 6, December 2011, Pages 716-728
Journal: Journal of Natural Gas Science and Engineering - Volume 3, Issue 6, December 2011, Pages 716-728
نویسندگان
Tina Yu, Dave Wilkinson, Julian Clark, Morgan Sullivan,