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関連する概念動画

Uniform Depth Channel Flow01:27

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 Spillway01:21

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 Solving01:28

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 Flow01:24

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 Flow01:29

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 Solving01:26

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

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Mechanical Expansion of Steel Tubing as a Solution to Leaky Wellbores
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炭化水素井戸の性能パラメータの機械学習ベース予測とウェルヘッドチョークフロー最適化

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
PubMed
まとめ

機械学習モデルは炭化水素井戸の性能を正確に予測します。多層パーセプトロン（MLP）モデルは、他のアルゴリズムを上回る流量とウェルヘッド圧力の予測精度において優れた性能を示しました。

キーワード:
チョークサイズ進化的最適化アルゴリズム流量予測液体生産量機械学習

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Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches
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科学分野:

  • 石油工学
  • 機械学習の応用
  • 流体力学

背景:

  • 正確な流体流量予測は、炭化水素回収と生産最適化にとって極めて重要です。
  • ウェルヘッドチョークは、下流の安定した圧力と井戸内圧力降下の制御に不可欠です。
  • 既存の多相流モデルは、グローバルな適用性と精度に限界があります。

研究 の 目的:

  • ウェル性能パラメータを予測するための機械学習アルゴリズムを評価すること。
  • 畳み込みニューラルネットワーク（CNN）、多層パーセプトロン（MLP）、および放射基底関数ネットワーク（RBFN）の予測精度を比較すること。

主な方法:

  • 畳み込みニューラルネットワーク（CNN）、多層パーセプトロン（MLP）、放射基底関数ネットワーク（RBFN）の3つの機械学習アルゴリズムを採用しました。
  • 液体生産量、ウェルヘッド圧力、チョークサイズ、BS&W、GLRの5つの入力パラメータを持つデータセットを使用しました。
  • 決定係数（R二乗）、RMSE、MSE、MAPE、MAEの指標を用いてモデルを評価しました。

主要な成果:

  • MLPは決定係数（R二乗）が最大0.9985で、最高の予測性能を示しました。
  • MLPは、トレーニングで0.0024、テストで0.0057という低い二乗平均平方根誤差（RMSE）値を達成しました。
  • データセットはトレーニングとテストのために70:30に分割されました。

結論:

  • MLPは、炭化水素生産におけるウェル性能パラメータを予測するための非常に効果的なモデルです。
  • 機械学習は、ウェルフローレート予測のための従来のモデルよりも正確な代替手段を提供します。
  • この研究は、石油・ガス生産の最適化におけるAIの可能性を強調しています。