Pipe roughness calibration in oil field water injection system

Yuxue Wang; Songyu Bai; Yuxue Wang; Songyu Bai

doi:10.3934/math.2025231

AIMS Mathematics

2025, Volume 10, Issue 3: 5052-5070. doi: 10.3934/math.2025231

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Research article

Pipe roughness calibration in oil field water injection system

Yuxue Wang ^,,
Songyu Bai

College of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, China

Received: 02 January 2025 Revised: 18 February 2025 Accepted: 24 February 2025 Published: 06 March 2025
MSC : 65K10, 86A05, 90C90

The pipe roughness coefficient is a crucial parameter in oil field water injection networks, directly affecting the accuracy of hydraulic calculations and operational optimization. This paper proposed a mathematical model to calibrate the pipe roughness coefficient under a single operating condition, using matrix singular value decomposition to convert the problem into a positive definite quadratic programming model. The interior-point method was employed to solve this problem, yielding a global optimal solution. Simulation results on an actual network showed that the proposed method reduced the average error of the roughness coefficient by 4.9%, from 7.08% to 2.18%, demonstrating its effectiveness.
- water injection pipeline network,
- pipe roughness coefficient,
- correction,
- positive definite quadratic programming,
- interior-point method
Citation: Yuxue Wang, Songyu Bai. Pipe roughness calibration in oil field water injection system[J]. AIMS Mathematics, 2025, 10(3): 5052-5070. doi: 10.3934/math.2025231

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Abstract

The pipe roughness coefficient is a crucial parameter in oil field water injection networks, directly affecting the accuracy of hydraulic calculations and operational optimization. This paper proposed a mathematical model to calibrate the pipe roughness coefficient under a single operating condition, using matrix singular value decomposition to convert the problem into a positive definite quadratic programming model. The interior-point method was employed to solve this problem, yielding a global optimal solution. Simulation results on an actual network showed that the proposed method reduced the average error of the roughness coefficient by 4.9%, from 7.08% to 2.18%, demonstrating its effectiveness.

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