Oil Production-Energy Consumption Relationship Modeling Using Generalized Linear Model

This study investigates the relationship between energy consumption and oil production in petroleum extraction using a Generalized Linear Model (GLM). By analyzing actual production data from a Daqing Oilfield production plant (2021–2024), key energy consumption features such as electricity usage, mechanical extraction, water injection, and transportation were selected to construct a GLM predictive model. Comparisons with Generalized Least Squares (GLS) and linear regression models demonstrated the superior performance of GLM, achieving a coefficient of determination (R2) of 0.884, with mean absolute error (MAE) and mean squared error (MSE) of 0.095 and 0.012, respectively. The study highlights the nonlinear impacts of energy consumption on oil production, offering theoretical insights for optimizing energy use and supporting low-carbon transitions in oilfield operations. Future research will explore interaction effects to enhance model generalizability.

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Publication Details

Journal
Energies
Published
2026-02-03
DOI
https://doi.org/10.3390/en19030795
Primary Topic
Global Energy and Sustainability Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Oil Production-Energy Consumption Relationship Modeling Using Generalized Linear Model

Jun Mao, Dengyun Ma, Wei Qian, Wei Wang et al.
Energies
Global Energy and Sustainability Research
article

Oil Production-Energy Consumption Relationship Modeling Using Generalized Linear Model

Jun Mao, Dengyun Ma, Wei Qian, Wei Wang, Kailai Hu, Junru Luo, Chao Chi
article en

Abstract

This study investigates the relationship between energy consumption and oil production in petroleum extraction using a Generalized Linear Model (GLM). By analyzing actual production data from a Daqing Oilfield production plant (2021–2024), key energy consumption features such as electricity usage, mechanical extraction, water injection, and transportation were selected to construct a GLM predictive model. Comparisons with Generalized Least Squares (GLS) and linear regression models demonstrated the superior performance of GLM, achieving a coefficient of determination (R2) of 0.884, with mean absolute error (MAE) and mean squared error (MSE) of 0.095 and 0.012, respectively. The study highlights the nonlinear impacts of energy consumption on oil production, offering theoretical insights for optimizing energy use and supporting low-carbon transitions in oilfield operations. Future research will explore interaction effects to enhance model generalizability.

EnergiesVol. 19(3)
Digital China Health (China) (CN), Daqing Oilfield General Hospital (CN), Changzhou University (CN)
China National Petroleum Corporation
Affordable and clean energy
Openalex Percentile: Top 69%
Global Energy and Sustainability Research
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