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Modeling oil production based on symbolic regression

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  • Yang, Guangfei
  • Li, Xianneng
  • Wang, Jianliang
  • Lian, Lian
  • Ma, Tieju

Abstract

Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans.

Suggested Citation

  • Yang, Guangfei & Li, Xianneng & Wang, Jianliang & Lian, Lian & Ma, Tieju, 2015. "Modeling oil production based on symbolic regression," Energy Policy, Elsevier, vol. 82(C), pages 48-61.
    • Handle: RePEc:eee:enepol:v:82:y:2015:i:c:p:48-61
    DOI: 10.1016/j.enpol.2015.02.016
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    2. Lian Lian & Wen Tian & Hongfeng Xu & Menglan Zheng, 2018. "Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "Tracking economic growth by evolving expectations via genetic programming: A two-step approach," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    5. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    8. Bünning, Felix & Sangi, Roozbeh & Müller, Dirk, 2017. "A Modelica library for the agent-based control of building energy systems," Applied Energy, Elsevier, vol. 193(C), pages 52-59.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    10. Huan-Niemi, Ellen & Niskanen, Olli & Rikkonen, Pasi & Rintamaki, Heidi, 2015. "Forecasting mitigation measures for agricultural greenhouse gas emissions in Finland," 2015 Conference, August 9-14, 2015, Milan, Italy 211751, International Association of Agricultural Economists.
    11. Gómez-Castro, F.I. & Gutiérrez-Antonio, C. & Romero-Izquierdo, A.G. & May-Vázquez, M.M. & Hernández, S., 2023. "Intensified technologies for the production of triglyceride-based biofuels: Current status and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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