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Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique

Yıl 2025, , 212 - 222, 30.06.2025

Gülüzar Çit , Azhar Alyahya

https://doi.org/10.35377/saucis...1579599

Öz

Planning a strategy throughout the oil and gas sector depends on production forecasting. Precise projections aid in estimating future output rates, streamlining processes, and effectively allocating resources. Techniques like “ Decline Curve Analysis (DCA) and Numerical Reservoir Simulation (NRS) ” have been used in the past, but they have drawbacks such reliance on static models and time consumption. A stacked generalization ensemble learning method for predicting oil and gas production is presented in this work. Using Python and data from wells in the state of “New York State”, the model contains four machine learning techniques: “ Random Forest Regressor (RFR), Extremely Randomized Trees Regressor (ETR), K-Nearest Neighbors (KNN), and Gradient Boosting Regressor (GBR) ”. The stacked model works better than separate models, according to the results of experiments, via R2 scores of 0.9709 per oil and 0.9998 per gas.

Anahtar Kelimeler

Machine learning models Random Forest regressor Extremely Randomized Trees Regressor K- Nearest Neighbors Gradient boosting regressor Stacking model

Destekleyen Kurum

Sakarya University

Kaynakça

  • British Petroleum, "Statistical Review of World Energy," BP Global, 2021. [Online]. Available: https://www.bp.com
  • J. G. Speight, Handbook of Petroleum Refining. 2014. [Online]. Available: https://www.academia.edu/63659108/Handbook_of_Petroleum_Refining
  • D. Orodu, O. F. Aworinde, and A. F. Alayande, "A hybrid machine learning framework for enhanced reservoir characterization," J. Petroleum Sci. Eng., vol. 207, p. 109114, 2021, doi: 10.1016/j.petrol.2021.109114.
  • A. F. Khan and S. R. Alam, "Adaptive Neuro-Fuzzy Inference System with metaheuristic tuning for petroleum production forecasting," Applied Soft Computing, vol. 114, p. 108050, 2022, doi: 10.1016/j.asoc.2021.108050.
  • M. A. Ullah, S. M. Khaleque, and S. Sikder, "Prediction of oil production using optimized machine learning models," Energies, vol. 14, no. 16, p. 4923, 2021, doi: 10.3390/en14164923.
  • M. J. Fetkovich, "Decline Curve Analysis Using Type Curves," J. Petroleum Technol., vol. 32, no. 6, pp. 1065-1077, 1980.
  • M. J. Abhishek and V. Kumar, "Gradient boosting regression tree model for enhanced oil production prediction," Processes, vol. 10, no. 2, p. 234, 2022, doi: 10.3390/pr10020234.
  • K. M. Ali and J. Zhang, "Application of metaheuristic optimization algorithms for predictive analysis in petroleum engineering," J. Petroleum Exploration Production Technol., vol. 12, no. 5, pp. 1325–1335, 2022, doi: 10.1007/s13202-021-01402-w.
  • C. S. W. Ng, A. J. Ghahfarokhi, and M. N. Amar, "Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm," J. Petroleum Sci. Eng., vol. 208, p. 109468, 2022, doi: 10.1016/j.petrol.2021.109468.
  • S. D. Mohaghegh, "Machine Learning Applications in Reservoir Engineering: Part 1," J. Petroleum Technol., vol. 69, no. 6, pp. 70-77, 2017, doi: 10.2118/0617-0070-JPT.
  • J. X. Chen, H. L. Wang, and K. Zhao, "Comparative evaluation of machine learning techniques for hydrocarbon reservoir prediction," Energies, vol. 14, no. 3, p. 806, 2021, doi: 10.3390/en14030806.
  • A. S. Abou-Sayed, "AI in the Petroleum Industry," Society of Petroleum Engineers AI Newsletter, 2021. [Online]. Available: https://www.spe.org
  • S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
  • M. Kim, "Deep Learning-Based Prediction of the Cumulative Gas Production of the Montney Formation, Canada," GeoConvention, 2020. [Online]. Available: https://geoconvention.com/wp-content/uploads/abstracts/2020/57980- deep-learning-based-prediction-of-the-cumulative-g.pdf
  • M. S. Zanjani, M. A. Salam, and O. Kandara, "Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques," Int. J. Comput. Sci. Inf. Security, vol. 18, no. 6, pp. 65–72, 2020.
  • C. Tan et al., "Fracturing productivity prediction model and optimization of the operation parameters of shale gas well based on machine learning," Lithosphere, vol. 2021, no. Special 4, p. 2884679, 2021, doi: 10.2113/2021/2884679.
  • G. Hui, S. Chen, Y. He, H. Wang, and F. Gu, "Machine learning-based production forecast for shale gas in unconventional reservoirs via integration of geological and operational factors," J. Natural Gas Sci. Eng., vol. 94, p. 104045, 2021, doi: 10.1016/j.jngse.2021.104045.
  • N. M. Ibrahim et al., "Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production," Sensors, vol. 22, no. 14, p. 5326, 2022, doi: 10.3390/s22145326.
  • S. Hosseini and T. Akilan, "Advanced Deep Regression Models for Forecasting Time Series Oil Production," arXiv preprint arXiv:2308.16105, 2023.
  • L. Song, C. Wang, C. Lu, S. Yang, and C. Tan, "Machine Learning Model of Oilfield Productivity Prediction and Performance Evaluation," J. Physics: Conference Series, vol. 2468, no. 1, p. 012084, 2022, doi: 10.1088/1742- 6596/2468/1/012084.
  • N. Liu, H. Gao, Z. Zhao, Y. Hu, and L. Duan, "A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang," J. Petroleum Exploration Production Technol., vol. 11, pp. 3533-3546, 2021, doi: 10.1007/s13202-021-01402-z.
  • F. Ye, X. Li, N. Zhang, and F. Xu, "Prediction of Single-Well Production Rate after Hydraulic Fracturing in Unconventional Gas Reservoirs Based on Ensemble Learning Model," Processes, vol. 12, no. 6, p. 1194, 2024, doi: 10.3390/pr12061194.
  • S. Ray, "A quick review of machine learning algorithms," in Proc. Int. Conf. Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 35-39, doi: 10.1109/comitcon.2019.8862451.
  • M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015, doi: 10.1126/science.aaa8415.
  • L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
  • M. Y. Khan, "Automated prediction of Good Dictionary EXamples (GDEX): a comprehensive experiment with distant supervision, machine learning, and word embedding-based deep learning techniques," Complexity, vol. 2021, pp. 1- 18, 2021, doi: 10.1155/2021/2553199.
  • L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, no. 2, pp. 123–140, 1996, doi: 10.1007/BF00058655.
  • A. K. Ali and A. M. Abdullah, "Fake accounts detection on social media using stack ensemble system," Int. J. Electrical Comput. Eng., vol. 12, no. 3, pp. 3013-3022, 2022.
  • S. P. Rao and A. V. K. Shetty, "Random forest-based predictive models for enhanced fluid flow estimation in pipelines," J. Petroleum Sci. Eng., vol. 199, p. 108382, 2021, doi: 10.1016/j.petrol.2021.108382.
  • P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. 63, no. 1, pp. 3–42, 2006, doi: 10.1007/s10994-006-6226-1.
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, 2009.
  • T. Aziz and M. R. Camana, "REM-Based Indoor Localization with an Extra-Trees Regressor," Electronics, vol. 12, no. 20, p. 4350, 2023, doi: 10.3390/electronics12204350.
  • R. K. Halder, "Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications," J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00973-y.
  • T. Timbers, T. Campbell, and M. Lee, "Chapter 7 Regression I: K-nearest neighbors," in Data Science: A First Introduction, CRC Press, 2022. [Online]. Available: https://datasciencebook.ca/regression1.html
  • C. Gkerekos, I. Lazakis, and G. Theotokatos, "Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study," Ocean Eng., vol. 188, p. 106282, 2019, doi: 10.1016/j.oceaneng.2019.106282.
  • J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting Machine," The Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001.
  • A. Ali, "Gradient Boosting Machine Learning Algorithm," Dec. 2023, doi: 10.13140/RG.2.2.31609.65123.
  • M. Kalirane, "Ensemble Learning in Machine Learning: Bagging, Boosting and Stacking," Analytics Vidhya, Jan. 2024. [Online]. Available: https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging- boosting-and-stacking/
  • "Oil and Gas Annual Production: Beginning 2001," Data.gov. [Online]. Available: https://catalog.data.gov/dataset/oil- and-gas-annual-production-beginning-2001

Yıl 2025, , 212 - 222, 30.06.2025

Gülüzar Çit , Azhar Alyahya

https://doi.org/10.35377/saucis...1579599

Öz

Kaynakça

  • British Petroleum, "Statistical Review of World Energy," BP Global, 2021. [Online]. Available: https://www.bp.com
  • J. G. Speight, Handbook of Petroleum Refining. 2014. [Online]. Available: https://www.academia.edu/63659108/Handbook_of_Petroleum_Refining
  • D. Orodu, O. F. Aworinde, and A. F. Alayande, "A hybrid machine learning framework for enhanced reservoir characterization," J. Petroleum Sci. Eng., vol. 207, p. 109114, 2021, doi: 10.1016/j.petrol.2021.109114.
  • A. F. Khan and S. R. Alam, "Adaptive Neuro-Fuzzy Inference System with metaheuristic tuning for petroleum production forecasting," Applied Soft Computing, vol. 114, p. 108050, 2022, doi: 10.1016/j.asoc.2021.108050.
  • M. A. Ullah, S. M. Khaleque, and S. Sikder, "Prediction of oil production using optimized machine learning models," Energies, vol. 14, no. 16, p. 4923, 2021, doi: 10.3390/en14164923.
  • M. J. Fetkovich, "Decline Curve Analysis Using Type Curves," J. Petroleum Technol., vol. 32, no. 6, pp. 1065-1077, 1980.
  • M. J. Abhishek and V. Kumar, "Gradient boosting regression tree model for enhanced oil production prediction," Processes, vol. 10, no. 2, p. 234, 2022, doi: 10.3390/pr10020234.
  • K. M. Ali and J. Zhang, "Application of metaheuristic optimization algorithms for predictive analysis in petroleum engineering," J. Petroleum Exploration Production Technol., vol. 12, no. 5, pp. 1325–1335, 2022, doi: 10.1007/s13202-021-01402-w.
  • C. S. W. Ng, A. J. Ghahfarokhi, and M. N. Amar, "Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm," J. Petroleum Sci. Eng., vol. 208, p. 109468, 2022, doi: 10.1016/j.petrol.2021.109468.
  • S. D. Mohaghegh, "Machine Learning Applications in Reservoir Engineering: Part 1," J. Petroleum Technol., vol. 69, no. 6, pp. 70-77, 2017, doi: 10.2118/0617-0070-JPT.
  • J. X. Chen, H. L. Wang, and K. Zhao, "Comparative evaluation of machine learning techniques for hydrocarbon reservoir prediction," Energies, vol. 14, no. 3, p. 806, 2021, doi: 10.3390/en14030806.
  • A. S. Abou-Sayed, "AI in the Petroleum Industry," Society of Petroleum Engineers AI Newsletter, 2021. [Online]. Available: https://www.spe.org
  • S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
  • M. Kim, "Deep Learning-Based Prediction of the Cumulative Gas Production of the Montney Formation, Canada," GeoConvention, 2020. [Online]. Available: https://geoconvention.com/wp-content/uploads/abstracts/2020/57980- deep-learning-based-prediction-of-the-cumulative-g.pdf
  • M. S. Zanjani, M. A. Salam, and O. Kandara, "Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques," Int. J. Comput. Sci. Inf. Security, vol. 18, no. 6, pp. 65–72, 2020.
  • C. Tan et al., "Fracturing productivity prediction model and optimization of the operation parameters of shale gas well based on machine learning," Lithosphere, vol. 2021, no. Special 4, p. 2884679, 2021, doi: 10.2113/2021/2884679.
  • G. Hui, S. Chen, Y. He, H. Wang, and F. Gu, "Machine learning-based production forecast for shale gas in unconventional reservoirs via integration of geological and operational factors," J. Natural Gas Sci. Eng., vol. 94, p. 104045, 2021, doi: 10.1016/j.jngse.2021.104045.
  • N. M. Ibrahim et al., "Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production," Sensors, vol. 22, no. 14, p. 5326, 2022, doi: 10.3390/s22145326.
  • S. Hosseini and T. Akilan, "Advanced Deep Regression Models for Forecasting Time Series Oil Production," arXiv preprint arXiv:2308.16105, 2023.
  • L. Song, C. Wang, C. Lu, S. Yang, and C. Tan, "Machine Learning Model of Oilfield Productivity Prediction and Performance Evaluation," J. Physics: Conference Series, vol. 2468, no. 1, p. 012084, 2022, doi: 10.1088/1742- 6596/2468/1/012084.
  • N. Liu, H. Gao, Z. Zhao, Y. Hu, and L. Duan, "A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang," J. Petroleum Exploration Production Technol., vol. 11, pp. 3533-3546, 2021, doi: 10.1007/s13202-021-01402-z.
  • F. Ye, X. Li, N. Zhang, and F. Xu, "Prediction of Single-Well Production Rate after Hydraulic Fracturing in Unconventional Gas Reservoirs Based on Ensemble Learning Model," Processes, vol. 12, no. 6, p. 1194, 2024, doi: 10.3390/pr12061194.
  • S. Ray, "A quick review of machine learning algorithms," in Proc. Int. Conf. Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 35-39, doi: 10.1109/comitcon.2019.8862451.
  • M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015, doi: 10.1126/science.aaa8415.
  • L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
  • M. Y. Khan, "Automated prediction of Good Dictionary EXamples (GDEX): a comprehensive experiment with distant supervision, machine learning, and word embedding-based deep learning techniques," Complexity, vol. 2021, pp. 1- 18, 2021, doi: 10.1155/2021/2553199.
  • L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, no. 2, pp. 123–140, 1996, doi: 10.1007/BF00058655.
  • A. K. Ali and A. M. Abdullah, "Fake accounts detection on social media using stack ensemble system," Int. J. Electrical Comput. Eng., vol. 12, no. 3, pp. 3013-3022, 2022.
  • S. P. Rao and A. V. K. Shetty, "Random forest-based predictive models for enhanced fluid flow estimation in pipelines," J. Petroleum Sci. Eng., vol. 199, p. 108382, 2021, doi: 10.1016/j.petrol.2021.108382.
  • P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. 63, no. 1, pp. 3–42, 2006, doi: 10.1007/s10994-006-6226-1.
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, 2009.
  • T. Aziz and M. R. Camana, "REM-Based Indoor Localization with an Extra-Trees Regressor," Electronics, vol. 12, no. 20, p. 4350, 2023, doi: 10.3390/electronics12204350.
  • R. K. Halder, "Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications," J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00973-y.
  • T. Timbers, T. Campbell, and M. Lee, "Chapter 7 Regression I: K-nearest neighbors," in Data Science: A First Introduction, CRC Press, 2022. [Online]. Available: https://datasciencebook.ca/regression1.html
  • C. Gkerekos, I. Lazakis, and G. Theotokatos, "Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study," Ocean Eng., vol. 188, p. 106282, 2019, doi: 10.1016/j.oceaneng.2019.106282.
  • J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting Machine," The Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001.
  • A. Ali, "Gradient Boosting Machine Learning Algorithm," Dec. 2023, doi: 10.13140/RG.2.2.31609.65123.
  • M. Kalirane, "Ensemble Learning in Machine Learning: Bagging, Boosting and Stacking," Analytics Vidhya, Jan. 2024. [Online]. Available: https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging- boosting-and-stacking/
  • "Oil and Gas Annual Production: Beginning 2001," Data.gov. [Online]. Available: https://catalog.data.gov/dataset/oil- and-gas-annual-production-beginning-2001
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Research Article
Yazarlar

Gülüzar Çit 0000-0002-1220-0558

Azhar Alyahya 0009-0002-6214-5179

Erken Görünüm Tarihi 13 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 5 Kasım 2024
Kabul Tarihi 21 Nisan 2025
Yayımlandığı Sayı Yıl 2025

Kaynak Göster

APA Çit, G., & Alyahya, A. (2025). Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. Sakarya University Journal of Computer and Information Sciences, 8(2), 212-222. https://doi.org/10.35377/saucis...1579599
AMA Çit G, Alyahya A. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. Haziran 2025;8(2):212-222. doi:10.35377/saucis.1579599
Chicago Çit, Gülüzar, ve Azhar Alyahya. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences 8, sy. 2 (Haziran 2025): 212-22. https://doi.org/10.35377/saucis. 1579599.
EndNote Çit G, Alyahya A (01 Haziran 2025) Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. Sakarya University Journal of Computer and Information Sciences 8 2 212–222.
IEEE G. Çit ve A. Alyahya, “Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique”, SAUCIS, c. 8, sy. 2, ss. 212–222, 2025, doi: 10.35377/saucis...1579599.
ISNAD Çit, Gülüzar - Alyahya, Azhar. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences 8/2 (Haziran 2025), 212-222. https://doi.org/10.35377/saucis. 1579599.
JAMA Çit G, Alyahya A. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. 2025;8:212–222.
MLA Çit, Gülüzar ve Azhar Alyahya. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences, c. 8, sy. 2, 2025, ss. 212-2, doi:10.35377/saucis. 1579599.
Vancouver Çit G, Alyahya A. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. 2025;8(2):212-2.

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