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Optimizing Drilling Parameters Using a Random Forests ROP Model in the Permian Basin

Authors
Ehsaan Nasir (BHGE) | Caleb Rickabaugh (BHGE)
DOI
https://doi.org/10.2118/191796-MS
Document ID
SPE-191796-MS
Publisher
Society of Petroleum Engineers
Source
SPE Liquids-Rich Basins Conference - North America, 5-6 September, Midland, Texas, USA
Publication Date
2018
Document Type
Conference Paper
Language
English
ISBN
978-1-61399-608-9
Copyright
2018. Society of Petroleum Engineers
Disciplines
1.12 Drilling Measurement, Data Acquisition and Automation, 1.12.6 Drilling Data Management and Standards, 1.6 Drilling Operations
Keywords
machine learning, Drilling optimization, ROP modeling, data mining
Downloads
17 in the last 30 days
261 since 2007
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SPE Member Price: USD 9.50
SPE Non-Member Price: USD 28.00

A random forests Rate Of Penetration (ROP) model, along with heat maps, was used to challenge and optimize the drilling parameters for new wells based on the surface drilling data acquired from previous wells. The goal was to analyze the data to observe surface drilling parameter trends aiding in increased bit life and reduced bit wear resulting in maximizing ROP and minimizing Mechanical Specific Energy (MSE).

The four key variables investigated were weight on bit (WOB), surface RPM, mud flowrate and the drilling formation. Surface drilling data for this study was utilized from wells, within a 20 mile radius, where the same bit and motor drilled the entire vertical interval to TD. Heat maps and ROP models (created using support vector regression, random forests and boosted trees) were employed for this purpose. Data was cleaned up using cutoffs (from the minimum and maximum values expected by the drilling engineer) and plotting data distributions. K-fold Cross validation was applied when generating the ROP models. The aim was to focus on the optimization of drilling parameters using surface data only, due to the lack of sub-surface data availability. Using the methodology developed, the drilling parameters could be optimized to extend bit life and reduce bit trips by maximizing ROP and minimizing MSE.

The random forests ROP model was found to be the best with a 12% mean absolute error. The error could have been reduced further by introducing additional variables into the model that capture the changes in formation mechanical properties, downhole parameters and vibrations. This paper only focuses on learnings from surface drilling data. After a certain threshold (which differed for the different formations encountered) an increase in WOB didn' t result in a corresponding increase in ROP. Moreover, most of the ROP gains were observed to be in the shallower formations drilled. For the deeper formations, it was more beneficial to reduce MSE as the ROP was relatively lower no matter what the parameters.

This study used random forests, support vector regression and boosted tree methods to generate ROP models instead of neural networks. Even though neural networks are the most extensive, random forests are generally faster and were the most accurate of the three aforementioned methods used. The less time and computational resources required when compared to neural networks made random forests an attractive option for such a study.

File Size  2 MBNumber of Pages   13

Supporting information

  • SUPPLEMENTARY/SPE-191796-SUP.pdf

James, G.,Witten, D.,Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R.

Hegde, C.,Wallace, S., & Gray, K. (2015, September 15). Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling. Society of Petroleum Engineers. doi:10.2118/176792-MS

Pessier, R. C.,Wallace, S. N., & Oueslati, H. (2012, January 1). Drilling Performance is a Function of Power at the Bit and Drilling Efficiency. Society of Petroleum Engineers. doi:10.2118/151389-MS

Amer, M. M.,Dahab, A. S., & El-Sayed, A.-A. H. (2017, June 1). An ROP Predictive Model in Nile Delta Area Using Artificial Neural Networks. Society of Petroleum Engineers. doi:10.2118/187969-MS

Wiktorski, E.,Kuznetcov, A., & Sui, D. (2017, April 5). ROP Optimization and Modeling in Directional Drilling Process. Society of Petroleum Engineers. doi:10.2118/185909-MS

Evangelatos, G. I., & Payne, M. L. (2016, March 1). Advanced BHA-ROP Modeling Including Neural Network Analysis of Drilling Performance Data. Society of Petroleum Engineers. doi:10.2118/178852-MS

Elahifar, B.,Thonhauser, G.,Fruhwirth, R. K., & Esmaeili, A. (2012, January 1). ROP Modeling using NeuralNetwork and Drill String Vibration Data. Society of Petroleum Engineers. doi:10.2118/163330-MS

Mantha, B., & Samuel, R. (2016, September 26). ROP Optimization Using Artificial Intelligence Techniques with Statistical Regression Coupling. Society of Petroleum Engineers. doi:10.2118/181382-MS

Jiang, W., & Samuel, R. (2016, March 1). Optimization of Rate of Penetration in a Convoluted Drilling Framework using Ant Colony Optimization. Society of Petroleum Engineers. doi:10.2118/178847-MS

Bradford, J. F.,Ferrari, L. A.,Rickabaugh, C.,Rothe, M. A., & Tipton, B. J. (2012, January 1). Hybrid Drill Bit Combining Fixed-Cutter And Roller-Cone Elements Improves Drilling Performance In Marcellus Shale Surface Interval. Society of Petroleum Engineers. doi:10.2118/154831-MS

Tao, D. Z.,Long, J. Z.,Zhi, L. B.,Zhi, W.,Zhang, X. L.,Ying, T. K. X., … Liu, X. J. (2012, January 1). Solving Slow Drilling/Hole Deviation Issues: Modeling Effort Increases ROP by 250% Saving Operator 14-Days Rig Time, Tuha Basin China. Society of Petroleum Engineers. doi:10.2118/156252-MS

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