Planning and optimization of oilfield surface construction engineering scheme based on cyclic neural network

DOI

10.2991/978-94-6463-429-7_17How to use a DOI?

Keywords

Recurrent neural network; intelligent algorithm; oil field ground construction; construction engineering scheme and optimization

Abstract

In order to solve the problems of high oil and gas gathering energy consumption and construction cost and poor efficiency after the application of oilfield surface construction engineering scheme planning, this paper studies an optimization method of oilfield surface construction engineering scheme planning based on cyclic neural network. Firstly, determine the objective function of the optimization of the oilfield surface construction scheme, set the constraint conditions of the optimization model, thus complete the design of the optimization method of the scheme based on the circular neural network. The experimental results show that the design method in this paper can effectively reduce the construction cost of oil field surface engineering and oil and gas gathering and transmission consumption, improve the construction efficiency of oil field surface engineering, the optimized total cost is 104.663 million yuan, the minimum oil and gas gathering and transmission consumption is 6.9 MWh, and the construction time is only 67.2 days, which has certain application value.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wei Zhang
PY  - 2024
DA  - 2024/06/07
TI  - Planning and optimization of oilfield surface construction engineering scheme based on cyclic neural network
BT  - Proceedings of the 2024 7th International Conference on Structural Engineering and Industrial Architecture (ICSEIA 2024)
PB  - Atlantis Press
SP  - 154
EP  - 161
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-429-7_17
DO  - 10.2991/978-94-6463-429-7_17
ID  - Zhang2024
ER  -