Application of machine learning algorithm to forecast production for fracture basement formation, Central arch, Bach Ho field

  • Tran Dang Tu Vietnam Petroleum Institute
  • Le The Hung Vietnam Petroleum Institute
  • Tran Xuan Quy Vietnam Petroleum Institute
  • Doan Huy Hien Vietnam Petroleum Institute
  • Pham Truong Giang Vietnam Petroleum Institute
  • Luu Dinh Tung Vietnam Petroleum Institute
Keywords: Machine learning, random forest model, production forecast, fracture basement, Bach Ho field


Oil production forecast is a big challenge in the oil and gas industry. Simulation model and prediction results play an important role in field operation and management. Currently, dynamic simulation model, decline curve analysis are popular tools applied to forecast production. The dynamic simulation model shows a remarkable effect for sedimentary objects. However, production forecasting by this method for fracture basement formation sometimes gives unreliable results because the fracture basement formation is a complex of geological objects, which causes difficulties in predicting the geological characteristics. The decline curve analysis (DCA) method uses simple extrapolated mathematical functions to forecast oil production, therefore the results do not reflect the production operations such as opening/closing production interval.
To avoid the disadvantages of these traditional methods, Vietnam Petroleum Institute (VPI) has studied the applicability of machine learning to forecast oil production for fracture basement formation of Bach Ho field. The study results show that the random forest model has improved the production forecast with low relative error (4%).  


Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay, “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, Vol. 12, pp. 2825 - 2830, 2011.

Tran Dang Tu, Nguyen The Duc, Le Quang Duyen, Pham Truong Giang, Le Vu Quan, Le Quoc Trung, Tran Xuan Quy, and Pham Chi Duc “An applied machine learning approach to production forecast for basement formation - Bach Ho field”, Petrovietnam Journal, Vol. 6, pp. 48 - 57, 2019.

Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization”, 3rd International Conference for Learning Representations, San Diego, USA, 7 - 9 May 2015. DOI: 10.48550/arXiv.1412.6980.

Robert E Schapire, “The strength of weak learnability”, Machine Learning, Vol. 5, pp. 197 - 227, 1990. DOI: 10.1007/BF00116037.

Oliver Kramer, “Scikit-learn”, Machine learning for evolution strategies. Springer, 2016, pp. 45 - 53. DOI: 10.1007/978-3-319-33383-0.

Tianqi Chen and Carlos Guestrin, “XGBoost: A scalable tree boosting system”, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 13 - 17 August 2016. DOI: 10.1145/2939672.2939785.

How to Cite
Tran , D. T., Le , T. H., Tran , X. Q., Doan, H. H., Pham , T. G., & Luu , D. T. (2022). Application of machine learning algorithm to forecast production for fracture basement formation, Central arch, Bach Ho field. Petrovietnam Journal, 9, 16 - 23.

Most read articles by the same author(s)

1 2 > >>