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2024 | OriginalPaper | Buchkapitel

Intelligent Prediction Technology for Production of Tight Oil Based on Data Analysis

verfasst von : Ning Li, Xiang-hong Wu, Xin Li, Zhi-ping Wang, Yue-zhong Wang, Li-ao Zhao, Liang Ren, Hong-liang Wang, Hong-yu Tian, Shu-hang Ren, Si-rui Jiang

Erschienen in: Proceedings of the International Field Exploration and Development Conference 2023

Verlag: Springer Nature Singapore

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Abstract

China is rich in tight oil resources, with a wide distribution range and a large amount of resources, making it one of the key areas for strategic replacement of future oil reserves and production. In response to issues such as strong heterogeneity of terrestrial tight oil reservoirs, difficulty in drilling high-quality oil layers, large production differences, and unclear main control factors for production capacity, a detailed analysis of dynamic and static data of production wells was conducted to analyze production performance and decline patterns. Production wells were classified according to production characteristics, and development indicators at different stages were statistically analyzed based on actual production days. Using a combination of principal component analysis and Pearson correlation coefficient, based on multiple dynamic and static data such as geological factors, fracturing factors, and development factors, and analyzing the correlation between different single and combined factors and cumulative oil production at different stages, the main control factors for different production stages of tight oil were obtained. A production capacity prediction model for tight oil fracturing horizontal wells was established based on machine learning intelligent algorithms, A production capacity evaluation and prediction technology for tight oil fracturing horizontal wells has been developed. By comparing with actual production data, the accuracy of the predicted results can meet production needs, providing a strong technical foundation for precise prediction and guidance of tight oil production in China.

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Literatur
1.
Zurück zum Zitat Rahmanifard, H., Gates, I., Asl, A.S.: Comparison of machine learning and statistical predictive models for production time series forecasting in tight oil reservoirs. In: SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA (2022). https://doi.org/10.15530/urtec-2022-3703284 Rahmanifard, H., Gates, I., Asl, A.S.: Comparison of machine learning and statistical predictive models for production time series forecasting in tight oil reservoirs. In: SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA (2022). https://​doi.​org/​10.​15530/​urtec-2022-3703284
2.
Zurück zum Zitat Zhang, Y., Zheng, Y., Sun, S., et al.: Data driven production prediction of tight sandstone after compression in Changqing Oilfield. Energy Environ. Protect. 43(10), 96–101127 (2021) Zhang, Y., Zheng, Y., Sun, S., et al.: Data driven production prediction of tight sandstone after compression in Changqing Oilfield. Energy Environ. Protect. 43(10), 96–101127 (2021)
3.
Zurück zum Zitat Tao, L., et al.: A new productivity prediction hybrid model for multi fractured horizontal wells in tight oil reservoirs. In: SPE/IATMI Asia Pacific Oil&Gas Conference and Exhibition, Virtual (2021). https://doi.org/10.2118/205620-MS Tao, L., et al.: A new productivity prediction hybrid model for multi fractured horizontal wells in tight oil reservoirs. In: SPE/IATMI Asia Pacific Oil&Gas Conference and Exhibition, Virtual (2021). https://​doi.​org/​10.​2118/​205620-MS
4.
Zurück zum Zitat Al Ali Hussain Al Ali, Z., Horne, R.: Meta learning using deep N-BEATS model for production forecasting with limited history. In: Gas&Oil Technology Showcase and Conference held in Dubai, UAE (2023) Al Ali Hussain Al Ali, Z., Horne, R.: Meta learning using deep N-BEATS model for production forecasting with limited history. In: Gas&Oil Technology Showcase and Conference held in Dubai, UAE (2023)
6.
Zurück zum Zitat Class_Messages_Listing/content/Important_Neural_Network_Technology_Tutorials/Olah/LSTM Neural Network Tutorial-15.pdf Class_Messages_Listing/content/Important_Neural_Network_Technology_Tutorials/Olah/LSTM Neural Network Tutorial-15.pdf
7.
Zurück zum Zitat Wang, Y., Wang, C., Zhang, H., et al.: Automatic ship detection based on RetinaNet using multi resolution Gaofen-3 image. Remote Sens. 11(5), 531 (2019)CrossRef Wang, Y., Wang, C., Zhang, H., et al.: Automatic ship detection based on RetinaNet using multi resolution Gaofen-3 image. Remote Sens. 11(5), 531 (2019)CrossRef
8.
Zurück zum Zitat Chen, L., Wang, Z., Wang, G.: Application of LSTM network in short-term power load forecasting under deep learning framework. Power Inf. Commun. Technol. 15(5), 8–11 (2017)MathSciNet Chen, L., Wang, Z., Wang, G.: Application of LSTM network in short-term power load forecasting under deep learning framework. Power Inf. Commun. Technol. 15(5), 8–11 (2017)MathSciNet
9.
Zurück zum Zitat Li, N., Gong, R., Liu, Z., Mi, L., Liu, L.: Application of artificial intelligence technology in single well production and water cut prediction. In: Lin, J. (ed.) IFEDC 2021. Springer Series in Geomechanics and Geoengineering, pp. 512–528. Springer, Singapore (2021). https://doi.org/10.1007/978-981-19-2149-0_47 Li, N., Gong, R., Liu, Z., Mi, L., Liu, L.: Application of artificial intelligence technology in single well production and water cut prediction. In: Lin, J. (ed.) IFEDC 2021. Springer Series in Geomechanics and Geoengineering, pp. 512–528. Springer, Singapore (2021). https://​doi.​org/​10.​1007/​978-981-19-2149-0_​47
10.
Zurück zum Zitat Ma, Q., Guo, J., Li, N.: Load forecasting methods for urban gas pipeline networks. J. Anshan Univ. Sci. Technol. 27(2), 101–105 (2004) Ma, Q., Guo, J., Li, N.: Load forecasting methods for urban gas pipeline networks. J. Anshan Univ. Sci. Technol. 27(2), 101–105 (2004)
11.
Zurück zum Zitat Li, N.: Research on load forecasting of urban gas pipeline networks. Master’s thesis, Liaoning University of Science and Technology (2004) Li, N.: Research on load forecasting of urban gas pipeline networks. Master’s thesis, Liaoning University of Science and Technology (2004)
12.
Zurück zum Zitat Ojedapo, B., Ikiensikama, S., Wachikwu, V.U.: Elechi petroleum production forecasting using machine learning algorithms. In: SPE Nigeria Annual International Conference and Exhibition held in Lagos, Nigeria (2022). https://doi.org/10.2118/212018-MS Ojedapo, B., Ikiensikama, S., Wachikwu, V.U.: Elechi petroleum production forecasting using machine learning algorithms. In: SPE Nigeria Annual International Conference and Exhibition held in Lagos, Nigeria (2022). https://​doi.​org/​10.​2118/​212018-MS
13.
Zurück zum Zitat Gong, R., Li, X., Li, N., et al.: Artificial Intelligence for Oil and Gas, pp. 9–10. Petroleum Industry Press (2021) Gong, R., Li, X., Li, N., et al.: Artificial Intelligence for Oil and Gas, pp. 9–10. Petroleum Industry Press (2021)
14.
Zurück zum Zitat Li, N., Gong, R., Li, X., Li, W., Wu, B., Ren, S.: Factor analysis of affecting the accuracy for intelligent picking of seismic first arrivals with deep learning model. In: Lin, J. (ed.) IFEDC 2022. Springer Series in Geomechanics and Geoengineering, pp. 7042–7062. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-1964-2_598CrossRef Li, N., Gong, R., Li, X., Li, W., Wu, B., Ren, S.: Factor analysis of affecting the accuracy for intelligent picking of seismic first arrivals with deep learning model. In: Lin, J. (ed.) IFEDC 2022. Springer Series in Geomechanics and Geoengineering, pp. 7042–7062. Springer, Singapore (2023). https://​doi.​org/​10.​1007/​978-981-99-1964-2_​598CrossRef
16.
Zurück zum Zitat Li, N., Ran, Q.Q., Li, J.F., Yuan, J.R., Wang, C., Wu, Y.S.: A multiple-continuum model for simulation of gas production from shale gas reservoirs. SPE165991 (2013) Li, N., Ran, Q.Q., Li, J.F., Yuan, J.R., Wang, C., Wu, Y.S.: A multiple-continuum model for simulation of gas production from shale gas reservoirs. SPE165991 (2013)
18.
Zurück zum Zitat Li, N., Yan, L., Li, L., et al.: Numerical simulation of triple media percolation mechanism of shale gas reservoir. In: 10th National Symposium on Efficient Development Technology of Natural Gas Reservoir, pp. 342–349 (2019) Li, N., Yan, L., Li, L., et al.: Numerical simulation of triple media percolation mechanism of shale gas reservoir. In: 10th National Symposium on Efficient Development Technology of Natural Gas Reservoir, pp. 342–349 (2019)
Metadaten
Titel
Intelligent Prediction Technology for Production of Tight Oil Based on Data Analysis
verfasst von
Ning Li
Xiang-hong Wu
Xin Li
Zhi-ping Wang
Yue-zhong Wang
Li-ao Zhao
Liang Ren
Hong-liang Wang
Hong-yu Tian
Shu-hang Ren
Si-rui Jiang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-0272-5_7