Academic Journal
变工况集输管道胶凝淤积特征智能预测方法.
| Τίτλος: | 变工况集输管道胶凝淤积特征智能预测方法. |
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| Alternate Title: | Intelligent prediction methods for gelling deposit characteristics in gathering pipelines under variable operating conditions. |
| Συγγραφείς: | 王志华1 zhihua_wang@126.com, 洪家骏1 hongcation@163.com, 祝 愿2, 姜 怀2, 杨 恒3, 卜凡熙1 |
| Πηγή: | Journal of China University of Petroleum. Aug2026, Vol. 50 Issue 4, p112-124. 13p. |
| Θεματικοί όροι: | *Pipeline transportation, *Petroleum industry, Gelation, Prediction models, Fluid dynamics, Pipeline maintenance & repair, Oil wells |
| Abstract (English): | In response to the " dual carbon" actions of oil fields and the utilization of distributed energy, the application scenarios of oil collection without water blending in high water-cut oil wells have been continuously expanded. However, in highwater-content produced fluid systems, complex gelling deposits inevitably form within gathering pipelines without hot water mixing. This study employed gelling deposit thickness to characterize gelling deposits behavior in high-water-cut produced fluid systems. A test system and methodology were designed for loop simulation tests to simulate the distribution of gelling deposit thickness within loop channels. Based on experimental data, an intelligent predictive model for gelling deposit characteristics was established, thereby revealing the influencing patterns on gelling deposit thickness within gathering pipelines. Results indicate that under the combined effects of gravitational differences, turbulent disturbances and shear-induced detachment, the thickness of gelling deposits in the upper pipeline section is markedly lower than that in the bottom section. Furthermore, different installation methods significantly influence the gelling deposition process, with deposit thickness increasing rapidly particularly at ascending curved sections, the leading edge of ascending bends, and the trailing end of descending bends. The relative error for the test set in the intelligent prediction model for gelling deposits characteristics within gathering pipelines was controlled within 20%, enabling accurate forecasting of inhomogeneous gelling deposit characteristics. The thicknesses reference value exerts the most pronounced effect on the growth rate of gelling deposits thickness. When the reference value increases, constrained by concentration gradients, the formation rate of gelling particles significantly diminishes, consequently reducing the rate of decrease. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 面向油田 "双碳" 行动及分布式能源利用, 高含水油井不掺水集油已不断扩大推广应用场景, 然而采出液体系 在停掺集输时, 管道中不可避免会形成复杂胶凝淤积层。 采用胶凝淤积厚度描述高含水采出液体系的胶凝淤积特 征, 设计模拟环道胶凝淤积厚度分布测试试验系统及方法, 并基于试验数据, 构建胶凝淤积特征智能预测模型, 揭示 集输管道胶凝淤积厚度的影响规律。 结果表明: 在重力差异、湍流扰动、剪切剥离效应影响下, 管道顶部区域的胶凝 淤积厚度明显小于底部区域, 且不同敷设方式对胶凝淤积过程影响显著, 尤其在上行弧型段、上行弯管段前端、下行 弯管段末端处胶凝淤积厚度迅速增大; 构建的集输管道胶凝淤积特征智能预测模型中测试集对应的相对误差控制 在 20% 以内, 可用于准确预测集输管道非均一化胶凝淤积特征; 厚度基准值对胶凝淤积厚度增长速率的影响最大, 当基准值增大后, 受浓度差限制, 胶凝颗粒形成速度会大幅降低, 速率削减幅度也随之降低。 [ABSTRACT FROM AUTHOR] |
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