Application of Machine Learning for Bit-formation Matching in Drilling Operations

Shedrach Igemhokhai; Kelani Bello; Abiodun Olaoye; Ehigie Momodu; Abayomi Adejumo; Oladele Akindayini

doi:doi:10.11648/j.pse.20250901.14

Research Article |

| Peer-Reviewed

Application of Machine Learning for Bit-formation Matching in Drilling Operations

Shedrach Igemhokhai^*

, Kelani Bello

, Abiodun Olaoye

, Ehigie Momodu

, Abayomi Adejumo

, Oladele Akindayini

Published in Petroleum Science and Engineering (Volume 9, Issue 1)

Received: 9 April 2025 Accepted: 27 April 2025 Published: 29 May 2025

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Abstract

Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.

Published in	Petroleum Science and Engineering (Volume 9, Issue 1)
DOI	10.11648/j.pse.20250901.14
Page(s)	38-47
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Machine Learning, Bit-Formation Matching, Drilling, Artificial Neural Networks, XGBOOST, Random Forest

References

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[2]	Watalingam, P. (2014). Bit Selection Using Drilling Data By Artificial Neural Networks. May. http://utpedia.utp.edu.my/id/eprint/14233
[3]	Bilgesu, H. I., Al-Rashidi, A. F., Aminian, K., and Ameri, S. (2001). An Unconventional Approach for Drill-Bit Selection. Proceedings of the Middle East Oil Show, 198–203. https://doi.org/10.2523/68089-ms
[4]	Fasheum, M. (1997). THE UNIVERSITY OF NOTTINGHAM DEPARTMENT OF MINERAL RESOURCES ENGINEERING by. April.
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[6]	Jamshidi, E., and Mostafavi, H. (2013). Soft computation application to optimize drilling bit selection utilizing virtual inteligence and genetic algorithms. Society of Petroleum Engineers - International Petroleum Technology Conference 2013, IPTC 2013: Challenging Technology and Economic Limits to Meet the Global Energy Demand, 1(October), 357–371. https://doi.org/10.2523/16446-ms
[7]	Brandon, B. D., Cerkovnik, J., Koskie, E., Bayoud, B. B., Colston, F., Clayton, R. I., Anderson, M. E., Hollister, K. T., Senger, J., and Niemi, R. (1992). Development of a New IADC Fixed Cutter Drill Bit Classification System. https://doi.org/10.2118/23940-MS
[8]	Rabia, H., Farrelly, M., and Barr, M. V. (1986). A New Approach to Drill Bit Selection. Society of Petroleum Engineers of AIME, (Paper) SPE, 421–428. https://doi.org/10.2118/15894-MS
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[11]	Onyia, E. C. (1988). Relationships Between Formation Strength, Drilling Strength, and Electric Log Properties. Society of Petroleum Engineers of AIME, (Paper) SPE, OMEGA. https://doi.org/10.2118/18166-MS
[12]	Igemhokhai, S., Bello, K., and Adejumo, A. (2025). A machine learning approach to Intelligent Artificial lift method selection. International Journal of Frontiers in Engineering and Technology Research, 8(2), 001–015. https://doi.org/10.53294/IJFETR.2025.8.2.0030
[13]	M. Ali, R. Prasad, Y. Xiang, Z. M. Yaseen, Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts, J. Hydrol. 584 (2020) 124647, https://doi.org/10.1016/J.JHYDROL.2020.124647
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APA Style

Igemhokhai, S., Bello, K., Olaoye, A., Momodu, E., Adejumo, A., et al. (2025). Application of Machine Learning for Bit-formation Matching in Drilling Operations. Petroleum Science and Engineering, 9(1), 38-47. https://doi.org/10.11648/j.pse.20250901.14

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ACS Style

Igemhokhai, S.; Bello, K.; Olaoye, A.; Momodu, E.; Adejumo, A., et al. Application of Machine Learning for Bit-formation Matching in Drilling Operations. Pet. Sci. Eng. 2025, 9(1), 38-47. doi: 10.11648/j.pse.20250901.14

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AMA Style

Igemhokhai S, Bello K, Olaoye A, Momodu E, Adejumo A, et al. Application of Machine Learning for Bit-formation Matching in Drilling Operations. Pet Sci Eng. 2025;9(1):38-47. doi: 10.11648/j.pse.20250901.14

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@article{10.11648/j.pse.20250901.14,
  author = {Shedrach Igemhokhai and Kelani Bello and Abiodun Olaoye and Ehigie Momodu and Abayomi Adejumo and Oladele Akindayini},
  title = {Application of Machine Learning for Bit-formation Matching in Drilling Operations 
},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {1},
  pages = {38-47},
  doi = {10.11648/j.pse.20250901.14},
  url = {https://doi.org/10.11648/j.pse.20250901.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250901.14},
  abstract = {Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.
},
 year = {2025}
}

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TY  - JOUR
T1  - Application of Machine Learning for Bit-formation Matching in Drilling Operations 

AU  - Shedrach Igemhokhai
AU  - Kelani Bello
AU  - Abiodun Olaoye
AU  - Ehigie Momodu
AU  - Abayomi Adejumo
AU  - Oladele Akindayini
Y1  - 2025/05/29
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250901.14
DO  - 10.11648/j.pse.20250901.14
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 38
EP  - 47
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250901.14
AB  - Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.

VL  - 9
IS  - 1
ER  -

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Author Information

Shedrach Igemhokhai

Department of Petroleum Engineering, Faculty of Engineering, University of Benin, Benin, Nigeria

Contact Email

http://orcid.org/0009-0001-9119-1783
Kelani Bello

Department of Petroleum Engineering, Faculty of Engineering, University of Benin, Benin, Nigeria

Contact Email

http://orcid.org/0000-0002-1240-9992
Abiodun Olaoye

Gankco Energy Services Limited, Abuja, Nigeria

Contact Email

http://orcid.org/0000-0002-2037-2501
Ehigie Momodu

Gankco Energy Services Limited, Abuja, Nigeria

Contact Email

http://orcid.org/0000-0003-3517-3879
Abayomi Adejumo

Oriental Energy Resources Limited, Lagos, Nigeria

Contact Email

http://orcid.org/0009-0005-7027-1847
Oladele Akindayini

Sterling Global Exploration and Production Company Limited, Lagos, Nigeria

Contact Email

http://orcid.org/0009-0007-6495-7580

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Plain Text BibTeX RIS

APA Style

Igemhokhai, S., Bello, K., Olaoye, A., Momodu, E., Adejumo, A., et al. (2025). Application of Machine Learning for Bit-formation Matching in Drilling Operations. Petroleum Science and Engineering, 9(1), 38-47. https://doi.org/10.11648/j.pse.20250901.14

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ACS Style

Igemhokhai, S.; Bello, K.; Olaoye, A.; Momodu, E.; Adejumo, A., et al. Application of Machine Learning for Bit-formation Matching in Drilling Operations. Pet. Sci. Eng. 2025, 9(1), 38-47. doi: 10.11648/j.pse.20250901.14

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AMA Style

Igemhokhai S, Bello K, Olaoye A, Momodu E, Adejumo A, et al. Application of Machine Learning for Bit-formation Matching in Drilling Operations. Pet Sci Eng. 2025;9(1):38-47. doi: 10.11648/j.pse.20250901.14

Copy | Download

@article{10.11648/j.pse.20250901.14,
  author = {Shedrach Igemhokhai and Kelani Bello and Abiodun Olaoye and Ehigie Momodu and Abayomi Adejumo and Oladele Akindayini},
  title = {Application of Machine Learning for Bit-formation Matching in Drilling Operations 
},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {1},
  pages = {38-47},
  doi = {10.11648/j.pse.20250901.14},
  url = {https://doi.org/10.11648/j.pse.20250901.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250901.14},
  abstract = {Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Application of Machine Learning for Bit-formation Matching in Drilling Operations 

AU  - Shedrach Igemhokhai
AU  - Kelani Bello
AU  - Abiodun Olaoye
AU  - Ehigie Momodu
AU  - Abayomi Adejumo
AU  - Oladele Akindayini
Y1  - 2025/05/29
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250901.14
DO  - 10.11648/j.pse.20250901.14
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 38
EP  - 47
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250901.14
AB  - Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.

VL  - 9
IS  - 1
ER  -

Copy | Download