首页|An artificial intelligence approach for particle transport velocity prediction in horizontal flows

An artificial intelligence approach for particle transport velocity prediction in horizontal flows

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Particle entrainment is an inevitable phenomenon in pipeline systems,especially during the develop-ment and extraction phases of oil and gas wells.Accurately predicting the critical velocity for particle transport is a key focus for implementing effective sand control management.This work presents a semi-supervised learning-deep hybrid kernel extreme learning machine(SSL-DHKELM)model for predicting the critical velocity,which integrates multiple machine learning theories including the deep learning approach,which is adept at advanced feature extraction.Meanwhile,the SSL framework enhances the model's capabilities when data availability is limited.An improved slime mould algorithm is also employed to optimize the model's hyperparameters.The proposed model has high accuracy on both the sample dataset and out-of-sample data.When trained with only 10％of the data,the model's error still did not increase significantly.Additionally,this model achieves superior predictive accuracy compared to existing mechanistic models,demonstrating its impressive performance and robustness.

Particle transportCritical velocityDeep learningSemi-supervised learningExtreme learning machine

Haoyu Chen、Zhiguo Wang、Hai Huang、Jun Zhang

展开 >

College of Petroleum Engineering,Xi'an Shiyou University,Xi'an,710065,China

Shaanxi Key Laboratory of Carbon Dioxide Sequestration and Enhanced Oil Recovery,Xi'an,710065,China

College of New Energy,Xi'an Shiyou University,Xi'an,710065,China

Engineering Research Center of Smart Energy and Carbon Neutral in Oil&Gas Field,Universities of Shaanxi Province,China

The Erosion/Corrosion Research Center,The University of Tulsa,OK,74104,United States

展开 >

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Basic Research Program of Shaanxi ProvinceNatural Science Basic Research Program of Shaanxi ProvinceNatural Science Basic Research Program of Shaanxi ProvinceChina Postdoctoral Science FoundationYouth Innovation Team of Shaanxi UniversitiesOpen Foundation of Shaanxi Key Laboratory of Carbon Dioxide Sequestration and Enhanced Oil Recovery

52074220523040082022JC-372023-JC-QN-04032024JC-YBQN-03812023MD734223

2024

10.1016/j.partic.2024.05.011

颗粒学报(英文版)
中国颗粒学会　中国科学院过程工程研究所

颗粒学报(英文版)

CSTPCD
影响因子：0.632
ISSN：1674-2001
年,卷(期)：2024.92(9)