Abstract
Drilling optimization is essential in constructing an enhanced geothermal system (EGS) and can be facilitated through predicting the rate of penetration (ROP). The ROP evolution along the depth was forecasted by considering the current and previous ROP values as input to a gated recurrent unit (GRU)-based deep learning model. Drilling data was obtained from two geothermal wells in Pohang, South Korea. Multiple data configurations for training and testing were designed from both wells. The proximity of the training section to the target results in improved accuracy in prediction (MAPE smaller than ~ 3%). Furthermore, larger depth spans of ROPs used for training resulted in better prediction outcomes. The model trained with the entire dataset from an adjacent well exhibited well-predicted ROP values for a new drilling hole (MAPE smaller than 5–10%). From the multiple-step forecasting analysis, the error tended to sharply increase as the number of predicted ROP values increased despite a large number of the input sequence (MAPE larger than 20%). Incorporating other drilling data besides ROP evolution did not improve the prediction. By predicting ROP evolution along the depth, the GRU-based model can assist operators in optimizing drilling processes and preparing for upcoming scenarios. The model can serve as a valuable tool for enhancing drilling efficiency and effectively managing operational uncertainties.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
References
Abdel Azim R (2020) Application of artificial neural network in optimizing the drilling rate of penetration of western desert Egyptian wells. SN Appl Sci 2:1177. https://doi.org/10.1007/s42452-020-2993-8
Ahmed A, Ali A, Elkatatny S, Abdulraheem A (2019) New artificial neural networks model for predicting rate of penetration in deep shale formation. Sustainability 11:6527. https://doi.org/10.3390/su11226527
Al-Abduljabbar A, Gamal H, Elkatatny S (2020) Application of artificial neural network to predict the rate of penetration for S-shape well profile. Arab J Geosci 13:784. https://doi.org/10.1007/s12517-020-05821-w
Al-AbdulJabbar A, Elkatatny S, Mahmoud M, Abdulraheem A (2018) Predicting rate of penetration using artificial intelligence techniques. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. SPE
Alali AM, Abughaban MF, Aman BM, Ravela S (2021) Hybrid data driven drilling and rate of penetration optimization. J Pet Sci Eng 200:108075. https://doi.org/10.1016/j.petrol.2020.108075
Alkinani HH, Al-Hameedi AT, Dunn-Norman S, et al (2019) Dynamic neural network model to predict the rate of penetration prior to drilling. 53rd US Rock Mech Symp
Alsaihati A, Elkatatny S, Gamal H (2022) Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model. J Pet Sci Eng 208:109335. https://doi.org/10.1016/j.petrol.2021.109335
Augustine C, Tester JW, Anderson B, et al (2006) A comparison of geothermal with oil and gas well drilling costs. In: Proceedings of the 31st Workshop on Geothermal Reservoir Engineering. Curran Associates Inc New York, New York, pp 5–19
Barbosa LFFM, Nascimento A, Mathias MH, de Carvalho JA (2019) Machine learning methods applied to drilling rate of penetration prediction and optimization - A review. J Pet Sci Eng 183:106332. https://doi.org/10.1016/j.petrol.2019.106332
Brenjkar E, Biniaz Delijani E, Karroubi K (2021) Prediction of penetration rate in drilling operations: a comparative study of three neural network forecast methods. J Pet Explor Prod 11:805–818. https://doi.org/10.1007/s13202-020-01066-1
Cho K, Van Merriënboer B, Gulcehre C, et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv Prepr arXiv14061078. https://doi.org/10.48550/arXiv.1406.1078
Dashti A, Raji M, Alivand MS, Mohammadi AH (2020) Estimation of CO2 equilibrium absorption in aqueous solutions of commonly used amines using different computational schemes. Fuel 264:116616. https://doi.org/10.1016/j.fuel.2019.116616
Diaz MB, Kim KY (2020) Improving rate of penetration prediction by combining data from an adjacent well in a geothermal project. Renew Energy 155:1394–1400. https://doi.org/10.1016/j.renene.2020.04.029
Diaz MB, Kim KY, Kang T-H, Shin H-S (2018) Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting improvement. Geothermics 72:348–357. https://doi.org/10.1016/j.geothermics.2017.12.007
Diaz MB, Kim KY, Shin H-S, Zhuang L (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. https://doi.org/10.1016/j.jngse.2019.05.004
Elkatatny S (2018) New approach to optimize the rate of penetration using artificial neural network. Arab J Sci Eng 43:6297–6304. https://doi.org/10.1007/s13369-017-3022-0
Elkatatny S (2021) Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques. Ain Shams Eng J 12:917–926. https://doi.org/10.1016/j.asej.2020.05.014
Elkatatny S, Al-AbdulJabbar A, Abdelgawad K (2020) A new model for predicting rate of penetration using an artificial neural network. Sensors 20:2058. https://doi.org/10.3390/s20072058
Elkatatny SM, Tariq Z, Mahmoud MA, Al-AbdulJabbar A (2017) Optimization of rate of penetration using artificial intelligent techniques. In: 51st U.S. Rock Mechanics/Geomechanics Symposium. p ARMA-2017–0429
Eskandarian S, Bahrami P, Kazemi P (2017) A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking. J Pet Sci Eng 156:605–615. https://doi.org/10.1016/j.petrol.2017.06.039
Han J, Sun Y, Zhang S (2019) A data driven approach of ROP prediction and drilling performance estimation. In: International Petroleum Technology Conference. International Petroleum Technology Conference
Hashemizadeh A, Bahonar E, Chahardowli M et al (2022) Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement. Earth Sci Informatics 15:2133–2153. https://doi.org/10.1007/s12145-022-00860-1
Hassan A, Elkatatny S, Al-Majed A (2020) Coupling rate of penetration and mechanical specific energy to improve the efficiency of drilling gas wells. J Nat Gas Sci Eng 83:103558. https://doi.org/10.1016/j.jngse.2020.103558
Hegde C, Wallace S, Gray K (2015) Using trees, Bagging, and random forests to predict rate of penetration during drilling. In: SPE Middle East Intelligent Oil and Gas Conference and Exhibition. SPE
Hegde C, Gray KE (2017) Use of machine learning and data analytics to increase drilling efficiency for nearby wells. J Nat Gas Sci Eng 40:327–335. https://doi.org/10.1016/j.jngse.2017.02.019
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Li C, Cheng C (2020) Prediction and optimization of rate of penetration using a hybrid artificial intelligence method based on an improved genetic algorithm and artificial neural network. In: Abu Dhabi International Petroleum Exhibition & Conference. SPE, p D021S031R001
Mantha B, Samuel R (2016) ROP optimization using artificial intelligence techniques with statistical regression coupling. In: SPE annual technical conference and exhibition. SPE
Pyle D (1999) Data preparation for data mining. morgan kaufmann
Sabah M, Talebkeikhah M, Wood DA et al (2019) A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Sci Informatics 12:319–339. https://doi.org/10.1007/s12145-019-00381-4
Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639
Shi X, Liu G, Gong X et al (2016) An efficient approach for real-time prediction of rate of penetration in offshore drilling. Math Probl Eng 2016:1–13. https://doi.org/10.1155/2016/3575380
Soares C, Gray K (2019) Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models. J Pet Sci Eng 172:934–959. https://doi.org/10.1016/j.petrol.2018.08.083
Sobhi I, Dobbi A, Hachana O (2022) Prediction and analysis of penetration rate in drilling operation using deterministic and metaheuristic optimization methods. J Pet Explor Prod Technol 12:1341–1352. https://doi.org/10.1007/s13202-021-01394-w
Yan H, Zhang J, Zhou N, Li M (2020) Application of hybrid artificial intelligence model to predict coal strength alteration during CO2 geological sequestration in coal seams. Sci Total Environ 711:135029. https://doi.org/10.1016/j.scitotenv.2019.135029
Youcefi MR, Hadjadj A, Bentriou A, Boukredera FS (2020) Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm. Earth Sci Inform 13:1351–1368. https://doi.org/10.1007/s12145-020-00524-y
Zhao Y, Noorbakhsh A, Koopialipoor M et al (2020) A new methodology for optimization and prediction of rate of penetration during drilling operations. Eng Comput 36:587–595. https://doi.org/10.1007/s00366-019-00715-2
Acknowledgements
This work was based on data from NEXGEO and Pohang EGS project consortium.
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A5A1032433).
Author information
Authors and Affiliations
Contributions
Wanhyuk Seo: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing—Original Draft; Gyung Won Lee: Conceptualization, Resources, Data Curation; Kwang Yeom Kim: Conceptualization, Resources, Writing—Review & Editing; Tae Sup Yun: Conceptualization, Validation, Investigation, Supervision, Writing—Review & Editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Communicated by: H. Babaie
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.
About this article
Cite this article
Seo, W., Lee, G.W., Kim, K.Y. et al. Predicting rate of penetration (ROP) based on a deep learning approach: A case study of an enhanced geothermal system in Pohang, South Korea. Earth Sci Inform 17, 813–824 (2024). https://doi.org/10.1007/s12145-023-01149-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01149-7