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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749


Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

Sridharan Chandrasekaran
Department of Ocean Engineering, Indian Institute Technology Madras, Chennai-600036, India.

G. Suresh Kumar
Department of Ocean Engineering, Indian Institute Technology Madras, Chennai-600036, India.

DOI https://doi.org/10.33889/IJMEMS.2020.5.3.032

Received on November 03, 2019
  ;
Accepted on February 20, 2020

Abstract

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

Keywords- Neural network, Rate of penetration, Optimization, Drilling.

Citation

Chandrasekaran, S., & Kumar, G. S. (2020). Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics. International Journal of Mathematical, Engineering and Management Sciences, 5(3), 381-394. https://doi.org/10.33889/IJMEMS.2020.5.3.032.

Conflict of Interest

The authors confirm that there is no conflict of interest to declare for this publication.

Acknowledgements

The authors sincerely thank Mr. Jon Curtis (M/s Petrolink) and Mr. James Lazar (M/s Petrolink) for their support and encouragement in doing this research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors also sincerely appreciate the editor and reviewers for their time and valuable comments.

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