Neural network approach to predict existing and infill oil well performance

Linyu Yang, Zhong He, John Yen, Ching Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we put forward a neural network approach to predict existing and infill oil well performance. Multiple wells' history production data were used to train the neural network and the established neural network can be used to predict future performance of oil wells. No reservoir data is currently involved in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. As both of the static and dynamic data are used in the training, we combine the spatial and time series prediction together in this approach. Primary production of a 9-well area in North Robertson Unit located in west Texas was tested in this paper. The results demonstrate that our approach is powerful in rapid projection of existing wells' future performance, as well as the performance prediction of infill drilling wells. By incorporating the appropriate optimization technique, it can be further extended to use for location optimization of infill drilling wells.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages408-413
Number of pages6
Volume4
StatePublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: Jul 24 2000Jul 27 2000

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period7/24/007/27/00

Fingerprint

Oil wells
Infill drilling
Neural networks
Petroleum reservoirs
Time series

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Yang, L., He, Z., Yen, J., & Wu, C. (2000). Neural network approach to predict existing and infill oil well performance. In Proceedings of the International Joint Conference on Neural Networks (Vol. 4, pp. 408-413). IEEE.
Yang, Linyu ; He, Zhong ; Yen, John ; Wu, Ching. / Neural network approach to predict existing and infill oil well performance. Proceedings of the International Joint Conference on Neural Networks. Vol. 4 IEEE, 2000. pp. 408-413
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title = "Neural network approach to predict existing and infill oil well performance",
abstract = "In this paper, we put forward a neural network approach to predict existing and infill oil well performance. Multiple wells' history production data were used to train the neural network and the established neural network can be used to predict future performance of oil wells. No reservoir data is currently involved in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. As both of the static and dynamic data are used in the training, we combine the spatial and time series prediction together in this approach. Primary production of a 9-well area in North Robertson Unit located in west Texas was tested in this paper. The results demonstrate that our approach is powerful in rapid projection of existing wells' future performance, as well as the performance prediction of infill drilling wells. By incorporating the appropriate optimization technique, it can be further extended to use for location optimization of infill drilling wells.",
author = "Linyu Yang and Zhong He and John Yen and Ching Wu",
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Yang, L, He, Z, Yen, J & Wu, C 2000, Neural network approach to predict existing and infill oil well performance. in Proceedings of the International Joint Conference on Neural Networks. vol. 4, IEEE, pp. 408-413, International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, 7/24/00.

Neural network approach to predict existing and infill oil well performance. / Yang, Linyu; He, Zhong; Yen, John; Wu, Ching.

Proceedings of the International Joint Conference on Neural Networks. Vol. 4 IEEE, 2000. p. 408-413.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, we put forward a neural network approach to predict existing and infill oil well performance. Multiple wells' history production data were used to train the neural network and the established neural network can be used to predict future performance of oil wells. No reservoir data is currently involved in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. As both of the static and dynamic data are used in the training, we combine the spatial and time series prediction together in this approach. Primary production of a 9-well area in North Robertson Unit located in west Texas was tested in this paper. The results demonstrate that our approach is powerful in rapid projection of existing wells' future performance, as well as the performance prediction of infill drilling wells. By incorporating the appropriate optimization technique, it can be further extended to use for location optimization of infill drilling wells.

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Yang L, He Z, Yen J, Wu C. Neural network approach to predict existing and infill oil well performance. In Proceedings of the International Joint Conference on Neural Networks. Vol. 4. IEEE. 2000. p. 408-413