Skip to main content

Advertisement

A Physically-Constrained Ensemble Learning Rate of Penetration Prediction Model based on Multi-Source Data Fusion

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The rate of penetration (ROP) is a key indicator of drilling efficiency in oil and gas extraction. Accurate ROP prediction is crucial for optimizing drilling design and reducing costs. However, current ROP prediction models rarely consider the impact of geological parameters on ROP. Additionally, data-driven models often lack the constraints of physical laws governing the relationships between parameters, leading to poor interpretability of the results. To address these issues, this paper proposes a Correlation-Constrained Ensemble (CCE) model for ROP prediction that integrates both geological and engineering data. The CCE model first uses the Gradient Boosting Decision Tree (GBDT) algorithm to combine four models: Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Support Vector Regression (SVR). The model then applies correlation constraints to optimize predictions that do not meet the constraint conditions. By incorporating geological data and applying physical constraints, the mean absolute percentage error (MAPE) of the predictions on the test dataset was reduced from 13.67% to 8.87%. Finally, error evaluation methods more suitable for engineering applications were defined, namely error satisfaction and regional accuracy. Using these methods, the impact of different formations and lithologies on prediction results was analyzed. It was found that the prediction errors are larger at lithological transition interfaces and in formations with high clay content. For the regions with larger prediction errors identified in this study, corresponding solutions for engineering applications have been provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

References

  1. Gan C, Wang X, Wang LZ, Cao WH, Liu KZ, Gao H, Wu M (2023) Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process. Geoenergy Sci Eng 230:212187

    Article  Google Scholar 

  2. Hegde C, Millwater H, Pyrcz M, Daigle H, Gray K (2019) Rate of penetration (ROP) optimization in drilling with vibration control. J Nat Gas Sci Eng 67:71–78

    Article  Google Scholar 

  3. Zhang H, Ni H, Wang Z, Liu S, Liang H (2020) Optimization and application study on targeted formation rop enhancement with impact drilling modes based on clustering characteristics of logging. Energy Rep 6:2903–2912

    Article  Google Scholar 

  4. Alali AM, Abughaban MF, Aman BM, Revela S (2021) Hybrid data driven drilling and rate of penetration optimization. J Petrol Sci Eng 200:108075

    Article  Google Scholar 

  5. Brenjkar E, Delijani EB (2022) Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models. J Petrol Sci Eng 210:110033

    Article  Google Scholar 

  6. Maurer WC (1962) The “perfect-cleaning” theory of rotary drilling. J Petrol Technol 14(11):1270–1274

    Article  Google Scholar 

  7. Bourgoyne AT, Young FS (1974) A multiple regression approach to optimal drilling and abnormal pressure detection. Soc Petrol Eng J 14(04):371–384

    Article  Google Scholar 

  8. Warren TM (1987) Penetration rate performance of roller cone bits. SPE Drill Eng 2(01):9–18

    Article  Google Scholar 

  9. Maidla EE, Ohara S (1991) Field verification of drilling models and computerized selection of drill bit, WOB, and drill string rotation. SPE Drill Eng 6(03):189–195

    Article  Google Scholar 

  10. Dye WM, Daugereau K, Hansen NA, Otto MJ, Shoults L, Leaper R, Clapper D, Xiang T (2006) New water-based mud balances high-performance drilling and environmental compliance. SPE Drill Complet 21(04):255–267

    Article  Google Scholar 

  11. Mazen AZ, Rahmanian N, Mujtaba I, Hassanpour A (2021) Prediction of Penetration Rate for PDC Bits Using Indices of Rock Drillability, Cuttings Removal, and Bit Wear. SPE Drill Compl 36(02):320–337

    Article  Google Scholar 

  12. Gan C, Cao WH, Liu KZ, Wu M, Wang FW, Zhang SB (2020) A new hybrid bat algorithm and its application to the ROP optimization in drilling processes. IEEE Trans Ind Inform 16:7338–7348

    Article  Google Scholar 

  13. Ahmed A, Salaheldin E, Hany G (2022) Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model. J Petrol Sci Eng 208:109335

    Article  Google Scholar 

  14. Gan C, Cao WH, Liu KZ, Wu M (2022) A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process. J Process Control 109:83–92

    Article  Google Scholar 

  15. Pei Z, Song X, Ji Y, Yin T, Tian S, Li G (2023) Wide and deep cross network for the rate of penetration prediction. Geoenergy Sci Eng 229:212066

    Article  Google Scholar 

  16. Melvin BD, Kim KY, Shin HS, Li Z (2019) Predicting rate of penetration during drilling of deep geothermal well in Korea using artificial neural networks and real-time data collection. J Nat Gas Sci Eng 67:225–232

    Article  Google Scholar 

  17. Oyedere M, Gray K (2020) ROP and TOB optimization using machine learning classification algorithms. J Nat Gas Sci Eng 77:103230

    Article  Google Scholar 

  18. Chen X, Weng C, Du X, Yang J, Gao D, Wang R (2023) Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques. Ocean Eng 285:115404

    Article  Google Scholar 

  19. Su K, Da W, Li M, Li H, Wei J (2024) Research on a drilling rate of penetration prediction model based on the improved chaos whale optimization and back propagation algorithm. Geoenergy Sci Eng 240:213017

    Article  Google Scholar 

  20. Lawal AI, Kwon S, Onifade M (2021) Prediction of rock penetration rate using a novel antlion optimized ANN and statistical modelling. J Afr Earth Sci 182:104287

    Article  Google Scholar 

  21. Bizhani M, Kuru E (2022) Towards drilling rate of penetration prediction: Bayesian neural networks for uncertainty quantification. J Petrol Sci Eng 219:111068

    Article  Google Scholar 

  22. Alsaihati A, Elkatatny S, Gamal H (2022) Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model. J Petrol Sci Eng 208:109335

    Article  Google Scholar 

  23. Hegde C, Daigle H, Millwater H, Gray K (2017) Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models. J Petrol Sci Eng 159:295–306

    Article  Google Scholar 

  24. Ren J, Jiang J, Zhou C, Li Q, Xu Z (2024) Research on adaptive feature optimization and drilling rate prediction based on real-time data. Geoenergy Sci Eng 242:213247

    Article  Google Scholar 

  25. Damine Y, Bessous N, Megherbi AC, Sbaa S (2023) Early Bearing Fault Detection Using EEMD and Three-Sigma Rule Denoising Method. Mechanika 29:302–308

    Article  Google Scholar 

  26. Tao W, Sun Z, Wang G, Xiao S, Liang B, Zhang M, Song S (2024) Broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction. Comput Electron Agric 221:108948

    Article  Google Scholar 

  27. Rahadian H, Bandong S, Widyotriatmo A, Joelianto E (2023) Image encoding selection based on Pearson correlation coefficient for time series anomaly detection. Alex Eng J 82:304–322

    Article  Google Scholar 

  28. Nogueira SCL, Och SH, Moura LM, Domingues E, Coelho LS, Mariani VC (2023) Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering. Energy 208:128066

    Article  Google Scholar 

  29. Liu M, Zheng D, Li J, Hu Z, Liu L, Ding Y (2024) An ensemble learning framework for click-through rate prediction based on areinforcement learning algorithm with parameterized actions. Knowledge-Based Syst 283:111152

    Article  Google Scholar 

  30. Ribeiro MHDM, Silva RG, Ribeiro GT, Mariani VC, Coelho LS (2023) Cooperative ensemble learning model improves electric short-term load forecasting. Chaos, Solitons Fract 166:112982

    Article  MathSciNet  Google Scholar 

  31. Junior MY, Freire RZ, Seman LO, Stefenon SF, Mariani VC, Coelho LS (2024) Optimized hybrid ensemble learning approaches applied to very short-term load forecasting. Int J Electr Power Energy Syst 155:109579

    Article  Google Scholar 

  32. Moreno SR, Coelho LS, Ayala HVH, Mariani VC (2020) Wind turbines anomaly detection based on power curves and ensemble learning. IET Renew Power Gener 19:4086–4093

    Article  Google Scholar 

  33. Loreti D, Visani G (2024) Parallel approaches for a decision tree-based explainability algorithm. Future Gener Comput Syst 158:308–322

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge gratefully the financially supported by the National Natural Science Foundation of China (Grant No. 52104013), Project funded by China Postdoctoral Science Foundation (Grant No. 2022T150724), and National Key R&D Program of China(Grant No. 2019YFA0708300) .

Author information

Authors and Affiliations

Authors

Contributions

Yongdong Fan: Methodology, Program code, Writing- Original draft preparation Yan Jin: Data collection, Article structure determination, Overall control Huiwen Pang: Literature research, Writing- Reviewing and Editing Yunhu Lu: Practical application analysis Shiguo Wang: Practical application supplement and analysis.

Corresponding authors

Correspondence to Yongdong Fan or Yan Jin.

Ethics declarations

This manuscript has not been published or presented elsewhere and is not under consideration by another journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these. All authors have checked the manuscript and agreed to the submission. There are no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, Y., Jin, Y., Pang, H. et al. A Physically-Constrained Ensemble Learning Rate of Penetration Prediction Model based on Multi-Source Data Fusion. Appl Intell 55, 226 (2025). https://doi.org/10.1007/s10489-024-05922-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-05922-z

Keywords