Application of artificial intelligence and machine learning in optimizing oil and gas extraction
Keywords:
Artificial intelligence, machine learning, production forecastingAbstract
The oil and gas industry faces numerous challenges, including price fluctuations, operational complexities, and the need to optimize production processes. In the meantime, the use of new technologies such as artificial intelligence and machine learning can be considered as a solution to improve the accuracy of forecasts and increase productivity. The aim of this study is to investigate the effectiveness of artificial intelligence models in forecasting production and optimizing operations in oil fields. Also, the economic evaluation of the use of these technologies in reducing costs and increasing profitability is another objective of this study. In this study, various artificial intelligence and machine learning models have been used to simulate and predict production in oil fields. Data related to different fields have been collected and analyzed, and the models have been evaluated using various accuracy criteria. The results show that artificial intelligence models have high accuracy in predicting production with an error rate of less than 3%. Also, the use of these models has led to a 25% reduction in operating costs and a 21.4% increase in production efficiency. The use of artificial intelligence in the oil and gas industry has not only increased the accuracy of forecasts, but also contributed to economic and operational improvements. These results indicate the high potential of these technologies to transform the oil and gas industry.
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References
1. Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and
artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391.
2. Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E., El-Husseiny, A., ... & Abdulraheem,
A. (2021). A systematic review of data science and machine learning applications to the oil and gas
industry. Journal of Petroleum Exploration and Production Technology, 1-36.
3. Li, H., Yu, H., Cao, N., Tian, H., & Cheng, S. (2021). Applications of artificial intelligence in oil and gas
development. Archives of Computational Methods in Engineering, 28, 937-949.
4. Giuliani, M., Cadei, L., Montini, M., et al. (2018). Hybrid artificial intelligence techniques for automatic
simulation models matching with field data. In: Abu Dhabi international petroleum exhibition &
conference, 12–15 November. Society of Petroleum Engineers, Abu Dhabi, pp 1–11.
5. Agwu, O. E., Akpabio, J. U., Alabi, S. B., et al. (2018). Artificial intelligence techniques and their
applications in drilling fluid engineering: a review. Journal of Petroleum Science and Engineering, 167,
300–315.
6. Khan, M. R., Tariq, Z., & Abdulraheem, A. (2018). Machine learning derived correlation to determine
water saturation in complex lithologies. In: SPE Kingdom of Saudi Arabia annual technical symposium
and exhibition, 23–26 April. Society of Petroleum Engineers, Dammam, pp 1–10.
7. Salem, K. G., Abdulaziz, A. A. M., Abdel Sattar, A., & Dahab, A. S. D. (2018). Prediction of hydraulic
properties in carbonate reservoirs using artificial neural network. In: Abu Dhabi international
petroleum exhibition & conference, 12–15 November. Society of Petroleum Engineers, Abu Dhabi, pp
1–18.
8. Ghahfarokhi, P. K., Carr, T., Bhattacharya, S., Elliott, J., Shahkarami, A., & Martin, K. (2018). A fiberoptic assisted multilayer perceptron reservoir production modeling: a machine learning approach in
prediction of gas production from the Marcellus shale. In: SPE/AAPG/SEG unconventional resources
technology conference, 23–25 July, Houston, Texas, USA, pp 1–10.
9. Anderson, R. N., Xie, B., Wu, L., Kressner, A. A., Frantz, J. H., Ockree, M. A., & McLane, M. A. (2016).
Using machine learning to identify the highest wet gas producing mix of hydraulic fracture classes and
technology improvements in the Marcellus shale. In: Unconventional resources technology conference,
pp 1–13.
10. Ahmed, S. A., Elkatatny, S., Ali, A. Z., & Abdulraheem, A., Mahmoud, M. (2019). Artificial neural
network ANN approach to predict fracture pressure. In: SPE middle east oil and gas show and
conference, 18–21 March. Society of Petroleum Engineers, Manama, pp 1–9.
11. Wang, X., He, Y., Li, F., Dou, X., Wang, Z., Xu, H., & Fu, L. (2019). A working condition diagnosis
model of sucker rod pumping wells based on big data deep learning. In: International petroleum
technology conference, 26–28 March, Beijing, China, pp 1–10.
12. Ni, H. M., Liu, Y. J., Fan, Y. C., et al. (2014). Optimization of steam flooding injection and production
based on improved particle swarm optimization. Acta Petrolei Sinica, 35(1), 114–117.
13. Shi, S. Z., Yu, H. Y., Sun, Z. L., et al. (2014). Forecast of fracturing effect based on gray correlation
analysis and BP neural network. Journal of Changjiang University (Self Publ Ed), 31, 154–156.
14. Feng, G. Q., Pan, L. Y., Kong, B., et al. (2018). Hierarchical optimization research based on fuzzy
clustering analysis. Evaluation and Development of Oil and Gas Reservoirs, 3, 30–39.
15. Denney, D. (2000). Artificial neural networks identify restimulation candidates. SPE, 52(02), 44–45.
