Tree-Based Ensembles for Predicting the Bottomhole Pressure of Oil and Gas Well Flows
In this paper, we develop a predictive model for the multi-phase wellbore flows based on ensembles of decision trees like Random Forest or XGBoost. The tree-based ensembles are trained on the time series of different physical parameters generated using the numerical simulator of the full-scale transient wellbore flows. Once the training is completed, the ensemble is used to predict one of the key parameters of the wellbore flow, namely, the bottomhole pressure. According to our recent experiments with complex wellbore configurations and flows, the normalized root mean squared error (NRMSE) of prediction below 5% can be achieved and beaten by ensembles of decision trees in comparison to artificial neural networks. Moreover, the obtained solution is more scalable and demonstrate good noise-tolerance properties. The error analysis shows that the prediction becomes particularly challenging in the case of highly transient slug flows. Some hints for overcoming these challenges and research prospects are provided.
KeywordsMulti-phase flow Tree-based regression Time series Bottomhole pressure
The paper was partially prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’. The authors are grateful to Schlumberger and to Marina Bulova, Dean Willberg, Bertrand Theuveny in particular for supporting this work. Dmitry Ignatov was supported by the Russian Foundation for Basic Research, grants no. 16-01-00583 and 16-29-12982.
- 2.Theuveny, B., et al.: Integrated approach to simulation of near-wellbore and wellbore cleanup. In: SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers (2013)Google Scholar
- 3.Osman, E.A., Ayoub, M.A., Aggour, M.A.: An artificial neural network model for predicting bottomhole flowing pressure in vertical multiphase flow. In: SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers (2005)Google Scholar
- 12.Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York (2016)Google Scholar
- 13.Yu, H., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. In: Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5–10 December 2016, vol. 29, pp. 847–855 (2016)Google Scholar