Prediction of Drilling Fluid Temperature Window and Formation of Gas Hydrates During Drilling in Deep Water Using Machine Learning Algorithms

Document Type : Original Article

Authors
Mohammad Dousti 1
Elnaz Khodapanah 2
1 M.Sc., Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, PO. Box: 5331817634, Iran
2 Associate Professor, Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, PO. Box: 5331817634, Iran
20.1001.1/ijge.2024.722820
Abstract
Drilling in gas hydrate formations in deep water poses significant risks and challenges. This research aims to determine the temperature window for drilling fluid in deep water drilling, after examining the relations of predicting the temperature of gas hydrate formation, using two machine learning algorithms, "polynomial regression in Python and multilayer perceptron in MATLAB". The predictions are used to establish the required temperature range for the drilling fluid. Among the examined methods for predicting gas hydrate formation temperatures, the Saharkhizan relationship, with an average relative error of 0.49, demonstrated the highest accuracy. Furthermore, for six gas compounds with varying specific gravities, the gas compound with a specific gravity of 0.61 yielded an R² value of 0.9743 using the polynomial regression algorithm. Similarly, the gas compound with a specific gravity of 0.65 showed the highest accuracy when using the multilayer perceptron algorithm.
Keywords
Natural gas hydrates
Drilling fluid temperature window
Deepwater drilling
Polynomial regression algorithm
Multilayer perceptron algorithm
Subjects
  1. Carroll, J. (2020). Introduction. In Natural Gas Hydrates (pp. 1–26). Elsevier, pp: 12-15. DOI: https://doi.org/10.1016/B978-0-12-821771-9.00001-X.
  2. Englezos, P. (1993). Clathrate Hydrates. In Ind. Eng. Chem. Res (Vol. 32).
  3. Yang, M., Zhao, J., Zheng, J. nan, & Song, Y. (2019). Hydrate reformation characteristics in natural gas hydrate dissociation process: A review. In Applied Energy (Vol. 256). Elsevier Ltd. DOI:  https://doi.org/10.1016/j.apenergy.2019.113878 .
  4. Wang, X., Pan, L., Lau, H. C., Zhang, M., Li, L., & Zhou, Q. (2018). Reservoir volume of gas hydrate stability zones in permafrost regions of China. Applied Energy, 225, 486–500. DOI: https://doi.org/10.1016/j.apenergy.2018.04.125 .
  5. شرکت نفت خزر. (1398). هیدرات‌های گازی در آب‌های عمیق (چاپ اول)، صفحه 1-30.
  6. Li, J. fa, Ye, J. liang, Qin, X. wen, Qiu, H. jun, Wu, N. you, Lu, H. long, Xie, W. wei, Lu, J. An, Peng, F., Xu, Z. qiang, Lu, C., Kuang, Z. gui, Wei, J. gong, Liang, Q. yong, Lu, H. feng, & Kou, B. bei. (2018). the first offshore natural gas hydrate production test in South China Sea. China Geology, 1(1), 5–16. DOI:  https://doi.org/10.31035/cg2018003.
  7. Merey, Ş. (2016). Drilling of gas hydrate reservoirs. In Journal of Natural Gas Science and Engineering (Vol. 35, Issue Part A, pp. 1167–1179). Elsevier B.V. DOI: https://doi.org/10.1016/j.jngse.2016.09.058.
  8. Yakushev, V.S., Collett, T.S., 1992. Gas hydrates in Artic regions: risk to drilling and production. In: Proceedings of the Second (1992) International Offshore and Polar Engineering Conference, San Francisco, USA, 14e19 June 1992.
  9. Collett, T., Bahk, J.J., Baker, R., Boswell, R., Divins, D., Frye, M., Goldberg, D., Husebø, J., Koh, C., Malone, M., Morell, M., Myers, G., Shipp, C., Torres, M., 2015. Methane hydrates in nature-current knowledge and challenges. J. Chem. Eng. Data 60 (2), 319e329.
  10. Wei, N., Pei, J., Zhao, J., Zhang, L., Zhou, S., Luo, P., Li, H., & Wu, J. (2022). A state-of-the-art review and prospect of gas hydrate reservoir drilling techniques. In Frontiers in Earth Science (Vol. 10). Frontiers Media S.A. DOI: https://doi.org/10.3389/feart.2022.997337.
  11. Kozhevnykov, A., Khomenko, V., Liu, B., Kamyshatskyi, O., & Pashchenko, O. (2020). The history of gas hydrates studies: From laboratory curiosity to a new fuel alternative. Key Engineering Materials, 844, 49–64. DOI: https://doi.org/10.4028/www.scientific.net/KEM.844.49.
  12. Hammerschmidt, E.G., 1934. Formation of gas hydrates in natural gas transmission lines. Ind. Eng. Chem. 26, 851.
  13. Makogon, Y., 1981. Hydrates of Natural Gas. Penn Well Publishing Company, Tulsa, p. 237.
  14. Berge, B.K. (Norsk Hydro). Hydrate prediction on a microcomputer. SPE 15306, Presented at the Society of Petroleum Engineers Symposium on Petroleum Industry Applications, Colorado, USA, Silvercreek, 1986, 18e20 June.
  15. Kobayashi, R., Song, K., Sloan, E., 1987. Phase behavior of water/hydrocarbon systems. In: Bradley, H.B., Gipson Fred, W. (Eds.), Petroleum Engineering Handbook. Society of Petroleum Engineers, Richardson, TX, USA.
  16. Motiee, M., 1991. Estimate possibility of hydrate. Hydrocarb. Process. 70 (7), 98-99.
  17. Towler, B.F., Mokhatab, S., 2005. Quickly estimate hydrate formation conditions innatural gases. Hydrocarb. Process. 84 (4), 61e62.
  18. Ghayyem, M. ali, Izadmehr, M., & Tavakoli, R. (2014). Using an eclectic approach, developing a simple and accurate correlation for initial estimation of hydrate formation temperature of sweet natural gases. Journal of Natural Gas Science and Engineering, 21, 184–192. DOI: https://doi.org/10.1016/j.jngse.2014.08.003.
  19. Safamirzaei, M. (n.d.). Predict gas hydrate formation temperature with a simple correlation. www.GulfPub.com.
  20. Runyan Lee, T. (n.d.). A machine learning approach to predicting seafloor properties and their application in estimating a global methane hydrate inventory. 2021 https://scholarsjunction.msstate.edu/td
  21. Menad Nait Amar, Prediction of hydrate formation temperature using gene expression programming, Journal of Natural Gas Science and Engineering, Volume 89, May 2021, 103879, DOI: https://doi.org/10.1016/j.jngse.2021.103879.
  22. El-hoshoudy A. N., Ahmed A., Gomma S., Abdelhady A., An Artificial Neural Network Model for Predicting the Hydrate Formation Temperature, Arabian Journal for Science and Engineering 47(6), 2021, DOI: https://doi.org/10.1007/s13369-021-06340-w.
  23. Zimeng Yu, Hailong Tian, Application of Machine Learning in Predicting Formation Condition of Multi-Gas Hydrate, Energies 2022, 15(13), 4719; DOI: https://doi.org/10.3390/en15134719.
  24. Xinyue D., Jiaqiang Z., Jiadong Li, Yu T., Chuanyong Zhu, Liang G., Prediction of Gas Hydrate Formation in the Wellbore, Energies 2023, 16(14), 5579; DOI: https://doi.org/10.3390/en16145579.
  25. Abdullah Hussein, Essentials of Flow Assurance Solids in Oil and Gas Operations, Chapter 12 - Flow Assurance Solids Prediction and Modeling, pages 503-577, 2023, DOI: https://doi.org/10.1016/B978-0-323-99118-6.00018-6.
  26. PVTsim Nova. A leading PVT software. Available online: https://www.calsep.com/pvtsim-nova/ (accessed on 2021).
  27. CSMHYD. Publicly Available Software. Available online: http://hydrates.mines.edu/CHR/Software.html  (accessed on 2 July 2021).
  28. Rajnauth, J., Barrufet, M., & Falcone, G. (2010). SPE 131663 Hydrate Formation: Considering the effects of Pressure, Temperature, Composition and Water. http://onepetro.org/SPEEURO/proceedings-pdf/10EURO/All-10EURO/SPE-131663-MS/1742567/spe-131663-ms.pdf/1.
  29. Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. In SN Computer Science (Vol. 2, Issue 3). Springer. DOI: https://doi.org/10.1007/s42979-021-00592-x.
  30. حاجی‌آبادی، مرتضی؛ شبکه عصبی چیست: شبکه عصبی مصنوعی چیست، ارائه شده در سایت کنکور کامپیوتر، 1401.
  31. Mahesh, B. (2018). Machine Learning Algorithms-A Review. International Journal of Science and Research. DOI: https://doi.org/10.21275/ART20203995.
  32. Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. In SN Computer Science (Vol. 2, Issue 3). Springer. DOI: https://doi.org/10.1007/s42979-021-00592-x.
  33. Deepak K., Mayukh Ch., Gurmehak K., Suchitra V., Deep Learning for Medical Applications with Unique Data, chapter3- Deep learning applications for disease diagnosis, pages 31-51, 2022, DOI: https://doi.org/10.1016/B978-0-12-824145-5.00005-8.
Iranian Journal of Gas Engineering
Volume 11, Issue 1 - Serial Number 19
September 2024
Pages 71-86

  • Receive Date 05 June 2024
  • Revise Date 20 July 2024
  • Accept Date 10 August 2024