期刊
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
卷 159, 期 -, 页码 295-306出版社
ELSEVIER
DOI: 10.1016/j.petrol.2017.09.020
关键词
ROP; Data-driven; Machine learning; Drilling; Data analytics
资金
- Wider Windows Industrial Affiliate Program
- University of Texas at Austin
- BHP Billiton
- British Petroleum
- Chevron
- ConocoPhillips
- Halliburton
- Marathon
- National Oilwell Varco
- Shell
- Occidental Oil and Gas
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 (R-2). 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 R-2 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.
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