基于机器学习的预测,对井头的井性能参数进行井头流量优化.
Ali Akbari1, Fatemeh Ghazi2, Yousef Kazemzadeh3
1Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran. aliakbaripetroleum@gmail.com.
Scientific reports
|December 19, 2025
概括
机器学习模型可以准确地预测碳化合物井的性能. 多层感知器 (MLP) 模型显示出流量和井头压力的卓越预测准确性,优于其他算法.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
- 流体动力学 流体动力学
背景情况:
- 准确的流体流速预测对于碳化合物回收和生产优化至关重要.
- 井头堵塞对于稳定的下游压力和控制井内压力下降至关重要.
- 现有的多相流模型在全球适用性和准确性方面存在局限性.
研究的目的:
- 评估用于预测井性能参数的机器学习算法.
- 为了比较卷积神经网络 (CNN),多层感知器 (MLP) 和辐射基函数网络 (RBFN) 的预测精度.
主要方法:
- 采用了三个机器学习算法:CNN,MLP和RBFN.
- 使用了五个输入参数的数据集:液体生产率,井头压力,窒息尺寸,BS&W和GLR.
- 使用R平方,RMSE,MSE,MAPE和MAE指标评估的模型.
主要成果:
- MLP显示了最高的预测性能,R平方值高达0.9985.5.
- MLP实现了0.0024 (训练) 和0.0057 (测试) 的低根平均平方误差 (RMSE) 值.
- 数据集被分为70:30,用于培训和测试.
结论:
- MLP是一种高效的模型,用于预测碳化合物生产中的井性能参数.
- 机器学习为井流量预测提供了比传统模型更准确的替代方案.
- 该研究强调了人工智能在优化石油和天然气生产方面的潜力.
相关概念视频
Uniform Depth Channel Flow
517
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
517
Design Example: Creating a Hydraulic Model of a Dam Spillway
641
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
641
Pipe Flowrate Measurement: Problem Solving
785
A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is achieved...
785
Rapidly Varying Flow
402
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
402
Gradually Varying Flow
381
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
381
Weir: Problem Solving
418
Water flow in open channels is often measured using hydraulic structures such as weirs, which allow precise calculation of discharge. In a rectangular channel, flow rates are measured using three types of weirs: rectangular sharp-crested, triangular sharp-crested, and broad-crested. The weir head is set at a fixed height above the channel bottom, simplifying calculations and enabling the relationship between depth and flow rate to be analyzed.For the rectangular sharp-crested weir, the flow...
418


