Predicting oil field production using the Random Forest algorithm

  • Isabel F. A. Gonçalves PUC-Rio
  • Thiago M. D. Silva PUC-Rio
  • Abelardo B. Barreto PUC-Rio
  • Sinesio Pesco PUC-Rio


Precisely forecasting oil field performance is essential in oil reservoir planning and management. Nevertheless, forecasting oil production is a complex nonlinear problem due to all geophysical and petrophysical properties that may result in different effects with a bit of change. All decisions to be made during an exploitation project needs to be made considering different efficient algorithms to simulate data, providing robust scenarios to lead to the best deductions. To reduce the uncertainty in the simulation process, researchers have efficiently introduced machine learning algorithms for solving reservoir engineering problems because they can extract the maximum information from the dataset. Accordingly, this paper proposes using a Random Forest model to predict the daily oil production of an offshore reservoir. In this study, the oil rate production is considered a time series and was pre-processed and restructured to fit a supervised learning problem. We use the Random Forest model to forecast a one-time step, which is an extension of decision tree learning, widely used in regression and classification problems for supervised machine learning. For testing the robustness of the proposed model, we use the Volve oil field dataset as a case study to conduct the experiments. The results indicate that the Random Forest model could adequately estimate the one-time step of the oil field production.


M. A. Ahmadi, M. Ebadi, A. Yazdanpanah, "Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization", Journal of Petroleum Science and Engineering, Vol. 123, November 2014, Pages 7-19, DOI: 10.1016/j.petrol.2014.05.023

H. Altinçop, A. B. Oktay, "Air Pollution Forecasting with Random Forest Time Series Analysis", 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1-5, 2019, doi: 10.1109/IDAP.2018.8620768

L. Breiman, "Bagging predictors", Machine Learning, vol. 24, pp. 123-140, aug. 1996, doi: 10.1007/BF00058655.

L. Breiman, "Random Forests", Machine Learning, vol. 45, pp. 5-32, oct. 2001, doi: 10.1023/A:1010933404324.

L. A. N. Costa, C. Maschio, D. J. Schiozer, "Application of artificial neural networks in a history matching process", Journal of Petroleum Science and Engineering, Vol 123, November 2014, Pages 30-45, DOI: 10.1016/j.petrol.2014.06.004

Equinor, Volve dataset,, 2018.Accessed: 2022-04-13.

R. Feng, D. Grana, N. Balling, "Imputation of missing well log data by random forest and its uncertainty analysis", Computers & Geosciences, vol. 152, 104763, mar. 2021, doi: 10.1016/j.cageo.2021.104763

I. Gupta, N. Tran, D. Devegowda, V. Jayaram, C. Rai, C. Sondergeld, H. Karami. "Looking Ahead of the Bit Using Surface Drilling and Petrophysical Data: Machine-Learning-Based Real-Time Geosteering in Volve Field", SPE Journal, vol. 25, pp. 990-1006, 2020, doi: 10.2118/199882-PA

M. A. Marins, B. D. Barros, I. H. Santos, D. C. Barrionuevo, R. E.V. Vargas, T. M. Prego, A. A. Lima, M. L.R. Campos, E A. B. Silva, S. L. Netto, "Fault detection and classification in oil wells and production/service lines using random forest", Journal of Petroleum Science and Engineering, vol. 197, 107879, 2021, doi: 10.1016/j.petrol.2020.107879.

S. Mohaghegh, R. Arefi, S. Ameri, M. H. Hefner, "A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks", SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 1994, SPE-28394-MS

J. Nocedal, S. Wright, "Numerical Optimization", Springer-Verlag New York, 2006

D. S. Oliver, A. C. Reynolds, N. Liu, "Inverse Theory for Petroleum Reservoir Characterization and History Matching", Cambridge: Cambridge University Press, 2008

G. A. Papacharalampous, H. Tyralis, "Evaluation of random forests and Prophet for daily streamflow forecasting", Adv. Geosci., vol. 45, pp. 201-208, 2018, doi: 10.5194/adgeo-45-201-2018

X. Qiu, L. Zhang, P. N. Suganthan, G. A. J. Amaratunga, "Oblique Random Forest Ensemble via Least Square Estimation for Time Series Forecasting", Information Sciences, vol. 420, pp. 249-262, dec. 2017, doi: 10.1016/j.ins.2017.08.060

M. Rahimi, M. A. Riahi, "Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield ", Journal of Applied Geophysics, vol. 201, 104640, april, 2022, doi: 10.1016/j.jappgeo.2022.104640.

M. Ravasi, I. Vasconcelos, A. Kritski, A. Curtis, C. A. C. Filho, G. A. Meles, "Geophysical Journal International", 55th U.S. Rock Mechanics/Geomechanics Symposium, vol. 205, pp. 99-104, 2016, doi: 10.1093/gji/ggv528.

M. G. Shiranji, A. A. Emerick, "An Improved TSVD-Based Levenberg- Marquardt Algorithm for History Matching and Comparison with Gauss- Newton", Journal of Petroleum Science and Engineering, Vol. 143, July 2016, Pages 258-271, DOI: 10.1016/j.petrol.2016.02.026

T. M. D. Silva, R. V. Bela, S. Pesco, A. B. Barreto, "ES-MDA applied to estimate skin zone properties from injectivity tests data in multilayer reservoirs", Computers & Geosciences, Vol. 146, January 2021, 104635, DOI: 10.1016/j.cageo.2020.104635

T. M. D. Silva, S. Pesco, A. B. Barreto, "Influences of the inflation factors generation in the main parameters of the ensemble smoother with multiple data assimilation", Journal of Petroleum Science and Engineering, Vol. 203, August 2021, 108648, DOI: 10.1016/j.petrol.2021.108648

Z. Sun, A. Garza, R. Salazar-Tio, A. Fager, B. Crouse, "A Novel 3D Mechanical Earth Modeling of the Volve Field and Its Application to Fault Stability Analysis", 55th U.S. Rock Mechanics/Geomechanics Symposium, 2021.

A. T. Tunkiel, T. Wiktorski, D. Sui, "Drilling Dataset Exploration, Processing and Interpretation Using Volve Field Data", ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering, 2020, doi: 10.1115/OMAE2020-18151

B. Wang, J. Sharma, J. Chen, P. Persaud, "Ensemble Machine Learning Assisted Reservoir Characterization using Field Production Data-An Offshore Field Case Study", Energies, vol. 14, 1052, feb. 2021, doi: 10.3390/en14041052

H. Wu, Y. Cai, Y. Wu, R. Zhong, Q. Li, J. Zheng, D. Lin, Y. Li, "Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression", BioScience Trends, vol. 11, 3, 2017, doi: 10.5582/bst.2017.01035

N. Zhang, M. Wei, J. Fan, M. Aldhaheri, Y. Zhang, B. Bai, "Development of a hybrid scoring system for EOR screening by combining conventional screening guidelines and random forest algorithm", Energies, vol. 256, 115915, aug. 2019, doi: 10.1016/j.fuel.2019.115915

C. D. Zhou, X. Wu, J. Cheng, "Determining Reservoir Properties in Reservoir Studies Using a Fuzzy Neural Network", SPE Annual Technical Conference and Exhibition, Houston, Texas, October 1993, SPE-26430-MS
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GONÇALVES, Isabel F. A.; SILVA, Thiago M. D.; BARRETO, Abelardo B.; PESCO, Sinesio. Predicting oil field production using the Random Forest algorithm. In: WORKSHOP DE APLICAÇÕES INDUSTRIAIS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 134-139. DOI: