Abstract
In recent years, machine learning has been adopted in the Oil and Gas industry as a promising technology for solutions to the most demanding problems like downhole parameters estimations and incidents detection. A big amount of available data makes this technology an attractive option for solving a wide variety of drilling problems, as well as a reliable candidate for performing big-data analysis and interpretation. Nevertheless, this approach may cause, in some cases, that petroleum engineering concepts are disregarded in favor of more data-intensive approaches. This study aims to evaluate the impact of drilling data measurement correction on data-driven model performance. In our study, besides using the standard data processing technologies, like gap filling, outlier removal, noise reduction etc., the physics-based drilling models are also implemented for data quality improvement and data correction in consideration of the measurement physics, rarely mentioned in most of publications. In our case study, recurrent neural networks (RNN) that are able to capture temporal natures of a signal are employed for the rate of penetration (ROP) estimation with an adjustable predictive window. The results show that the RNN model produces the best results when using the drilling data recovered through analytical methods. Moreover, the comprehensive data-driven model evaluation and engineering interpretation are conducted to facilitate better understanding of the data-driven models and their applications.
NSTL
Elsevier