在泡凝聚-破裂驱动的流量下,在无管道中切片运输的多层次模拟和机器学习优化
Hengfu Xiang1, Guilin Zhang1, Sen Zhang1
1College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao 266580, China.
ACS omega
|February 16, 2026
概括
通过了解泡动态,可以改善深水无钻井中低效的切片运输. 一个整合实验,模拟和机器学习的新框架提高了切片运输预测和效率.
科学领域:
- 石油工程是石油工程中的一个.
- 多相流动力学 多相流动力学
- 计算流体动力学的流体动力学.
背景情况:
- 深水无式钻井面临着切片运输的挑战,导致运营问题,如堵塞的管道.
- 现有的模型往往忽略了气体-液体-固体系统中的泡动力学,限制了高压,高粘度条件下的准确性.
研究的目的:
- 开发一个综合框架,以了解无钻井中的气泡动力学和切片运输.
- 通过使用先进的建模和机器学习,提高切片度和运输速率的预测准确度.
主要方法:
- 综合框架结合了可视化实验,CFD-DEM与人口平衡模型 (PBM) 模拟以及机器学习.
- 使用CFD-DEM-PBM模型,对泡动态和切片相互作用进行高准确度量化.
- 训练逆向传播 (BP) 和辐射基函数 (RBF) 神经网络,使用实验数据进行基于物理学的预测.
主要成果:
- 泡凝聚抑制了沉积,而泡破裂促进了粒子再悬浮,它们的协同作用决定了运输效率.
- 结合的CFD-DEM-PBM模型准确地捕捉了泡动态和与切片的交叉规模相互作用.
- RBF神经网络实现了卓越的预测准确性 (R2 = 0.91182),超过了BP网络和经验模型.
结论:
- 综合框架为无钻井的实时参数优化和决策提供了可靠的基础.
- 这项研究为分析复杂的多相运输现象建立了新的范式.
- 了解气泡凝聚和分解对于优化切片运输效率至关重要.
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