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Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

Author

Listed:
  • Ivan Makhotin

    (Petroleum Engineering Department, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205 Moscow, Russia)

  • Denis Orlov

    (Petroleum Engineering Department, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205 Moscow, Russia)

  • Dmitry Koroteev

    (Petroleum Engineering Department, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205 Moscow, Russia)

Abstract

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding.

Suggested Citation

  • Ivan Makhotin & Denis Orlov & Dmitry Koroteev, 2022. "Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production," Energies, MDPI, vol. 15(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1199-:d:743522
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    Citations

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    Cited by:

    1. Yongchao Xue & Chong Cao & Qingshuang Jin & Qianyu Wang, 2023. "Re-Evaluation of Oil Bearing for Wells with Long Production Histories in Low Permeability Reservoirs Using Data-Driven Models," Energies, MDPI, vol. 16(2), pages 1-21, January.
    2. Lun Zhao & Jincai Wang & Libing Fu & Li Chen & Zhihao Jia, 2023. "Improve Oil Recovery Mechanism of Multi-Layer Cyclic Alternate Injection and Production for Mature Oilfield at Extra-High Water Cut Stage Using Visual Physical Simulation Experiment," Energies, MDPI, vol. 16(3), pages 1-13, February.
    3. Eman H. Alkhammash, 2022. "An Optimized Gradient Boosting Model by Genetic Algorithm for Forecasting Crude Oil Production," Energies, MDPI, vol. 15(17), pages 1-13, September.

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