کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5483863 1522782 2017 30 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات زمین شناسی اقتصادی
پیش نمایش صفحه اول مقاله
Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models
چکیده انگلیسی
Modeling the rate of penetration of the drill bit is essential for optimizing drilling operations. This paper evaluates two different approaches to ROP prediction: physics-based and data-driven modeling approach. Three physics-based models or traditional models have been compared to data-driven models. Data-driven models are built using machine learning algorithms, using surface measured input features - weight-on-bit, RPM, and flow rate - to predict ROP. Both models are used to predict ROP; models are compared with each other based on accuracy and goodness of fit (R2). Based on the results from these simulations, it was concluded that data-driven models are more accurate and provide a better fit than traditional models. Data-driven models performed better with a mean error of 12% and improve the R2 of ROP prediction from 0.12 to 0.84. The authors have formulated a method to calculate the uncertainty (confidence interval) of ROP predictions, which can be useful in engineering based drilling decisions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Petroleum Science and Engineering - Volume 159, November 2017, Pages 295-306
نویسندگان
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