Abstract
Oil production prediction is the main focus of scientific management. During the process of oil exploitation, the production data can be considered to have time series characteristics, which are affected by production plans and geologic conditions, making this time series data complex. To resolve this, this paper tries to make full use of the advantages of different prediction models and proposes model fusion based approach (called TN-Fusion) for production prediction. This approach can effectively extract the temporal and non-temporal features affecting the production, to improve the prediction accuracy through the effective fusion of time series model and non-time series model. Compared with those single model based approach, and non-time series model fusion methods, TN-Fusion has better accuracy and reliability.
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Acknowledgement
“This research was supported in part by the supporting project from China Petroleum Group (2018D-5010-16) for Big Data Industry Development Pilot Demonstration Project from Ministry of Industry and Information Technology, the National Major Science and Technology Project (2017ZX05013-002), the China Petroleum Group Science and Technology Research Institute Co., Ltd. Innovation Project (Grant No. 2017ycq02) and the Fundamental Research Funds for the Central Universities (2015020031)”.
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Zeng, X., Zhang, W., Xu, L., Wang, X., Yuan, J., Zhou, J. (2020). Model Fusion Based Oilfield Production Prediction. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_62
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DOI: https://doi.org/10.1007/978-981-15-2810-1_62
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