Research on application of machine learning algorithm to forecast production for fracture basement - Bach Ho field

  • Tran Dang Tu
  • Dinh Duc Huy
  • Pham Truong Giang
  • Le Quang Duyen
  • Tran Xuan Quy
  • Le The Hung
  • Luu Dinh Tung
Keywords: Artificial neural network, machine learning, forecasting production, Bach Ho field, logistic growth


Conventional tools that are currently used to forecast production for fracture basement (such as simulation model and decline curve analysis) are still not highly reliable and their forecasting performance is still short-term, affecting the plan for field development, field operation and optimisation of oil recovery.
The paper introduces the applicability of machine learning algorithm to predict oil production for basement reservoirs of Bach Ho field. The research results show that the artificial neural network (ANN) model using reverse propagation algorithm and the logistic growth model (LGM) using optimisation algorithm have improved the ability to predict production with high accuracy


Q. Cao, R. Banerjee, S. Gupta, J. Li, W. Zhou, and B. Jeyachandra, “Data driven production forecasting using machine learning”, SPE Argentina Exploration and Production of Unconventional Resources Symposium, Buenos Aires, Argentina, 1 - 3 June 2016. DOI: 10.2118/180984-MS.

Yanan Li and Yifu Han, “Decline curve analysis for production forecasting based on machine learning”, SPE Symposium: Production Enhancement and Cost Optimisation, Kuala Lumpur, Malaysia, 7 - 8 November 2017. DOI: 10.2118/189205-MS.

A. Mirzaei-Paiamna and S. Salavati, “The application of artificial neural networks for the prediction of oil production flow rate”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Vol. 34, No. 19, pp. 1834 - 1843, 2012. DOI: 10.1080/15567036.2010.492386.

David Fulford, “Machine learning for Production forecasting: Accuracy through uncertainty”, 12th Annual Ryder Scott Reservoir Conference, Houston, TX, 14 September 2016.

Trần Văn Hồi, Nguyễn Văn Đức và Phạm Xuân Sơn, “Tìm kiếm thăm dò và phát triển dầu trong đá móng mỏ Bạch Hổ: Tư liệu, sự kiện và bài học kinh nghiệm”, Hội nghị khoa học kỷ niệm 30 năm khai thác dầu từ đá móng mỏ Bạch Hổ, Vũng Tàu, 6/9/2018.

Trần Đăng Tú, Đinh Đức Huy, Trần Xuân Quý, Phạm Trường Giang, Lê Vũ Quân, Lê Thế Hùng, Lê Quốc Trung và Trần Nguyên Long, “Nghiên cứu ứng dụng mô hình tăng trưởng logistic để dự báo khai thác cho tầng Miocene dưới mỏ Bạch Hổ”, Tạp chí Dầu khí, số 9, tr. 16 - 22, 2019.

Pierre-François Verhulst, “Notice sur la loi que la population poursuit dans son accroissement”, Correspondance Mathématique et Physique, Vol. 10, pp. 113 - 121, 1838.

Thomas Robert Malthus, An essay on the principle of population: or, a view of its past and present effects on human happiness; with an inquiry into our prospects respecting the future removal or mitigation of the evils which it occasions. Biodiversity Heritage Library (BHL), 1872. DOI: 10.5962/bhl.title.49216.

A. Tsoularis and J. Wallace, “Analysis of logistic growth models”, Mathematical Biosciences, Vol. 179, No. 1, pp. 21 - 55, 2002. DOI: 10.1016/S0025-5564(02)00096-2.

M.King Hubbert, Nuclear energy and the fossil fuel. Drilling and Production Practice, New York. 1956.

How to Cite
Tran Dang Tu, Dinh Duc Huy, Pham Truong Giang, Le Quang Duyen, Tran Xuan Quy, Le The Hung, & Luu Dinh Tung. (2020). Research on application of machine learning algorithm to forecast production for fracture basement - Bach Ho field. Petrovietnam Journal, 12, 37 - 46.

Most read articles by the same author(s)

1 2 > >>