Online Monitoring of Inner Deposits in Crude Oil Pipelines

R. Giro, G. Bernasconi, G. Giunta, Simone Cesari
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Abstract

The formation of deposits is a very common issue in oil and gas pipeline transportation systems. Such sediments, mainly wax and paraffine for crude oil, or hydrates and water for gas, progressively reduce the free cross-sectional area of the pipe, leading in some cases to the complete occlusion of the conduit. The overall result is a decrease in the transportation performance, with negative economic, environmental, and safety consequences. To prevent this issue, the amount of inner deposits must be continuously and accurately monitored, such that the corresponding cleaning procedures can be performed when necessary. Currently, the former operation is still dictated by best-practice rules pertaining to preventive or reactive approaches, yet the demand from the industry is for predictive solutions that can be deployed online for real-time monitoring applications. The paper moves toward this direction by presenting a machine learning methodology that leverages pressure measurements to perform online monitoring of the inner deposits in crude oil trunklines. The key point is that the attenuation of pressure transients within the fluid is dependent on the free cross-sectional area of the pipe. Pressure signals, collected from two or more distinct locations along a pipeline, can therefore be exploited to estimate and track in real time the presence and thickness of the deposits. Several statistical indicators, derived from the attenuation of such pressure transients between adjacent acquisition points, are fed to a data-driven regression algorithm that automatically outputs a numeric indicator representing the amount of inner pipe debris. The procedure is applied to the pressure measurements collected for one and a half years on discrete points at a relative distance of 40 and 60 km along an oil pipeline in Italy (100 km length, 16-in. inner diameter pipes). The availability of historical data prepipe and postpipe cleaning campaigns further enriches the proposed data-driven approach. Experimental results demonstrate that the proposed predictive monitoring strategy is capable of tracking the conditions of the entire conduit and of individual pipeline sections, thus determining which portion of the line is subject to the highest occlusion levels. In addition, our methodology allows for real-time acquisition and processing of data, thus enabling the opportunity for online monitoring. Prediction accuracy is assessed by evaluating the typical metrics used in the statistical analysis of regression problems.
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原油管道内沉积物在线监测
在油气管道输送系统中,沉积物的形成是一个非常普遍的问题。这些沉积物,主要是原油中的蜡和石蜡,或天然气中的水合物和水,逐渐减少管道的自由横截面积,在某些情况下导致管道完全闭塞。总体结果是运输性能下降,带来负面的经济、环境和安全后果。为了防止这个问题,必须连续准确地监测内部沉积物的数量,以便在必要时执行相应的清洗程序。目前,前一种操作仍然是由与预防性或反应性方法相关的最佳实践规则所决定的,但行业的需求是可以在线部署用于实时监控应用的预测解决方案。本文提出了一种机器学习方法,利用压力测量对原油干线内部沉积物进行在线监测,朝着这个方向迈进。关键是流体内压力瞬态的衰减取决于管道的自由横截面积。因此,可以利用从管道沿线两个或多个不同位置收集的压力信号来实时估计和跟踪沉积物的存在和厚度。从相邻采集点之间的压力瞬变衰减中得出的几个统计指标被输入到数据驱动的回归算法中,该算法自动输出一个代表内管碎屑数量的数字指标。该程序适用于在意大利的一条石油管道(长100公里,16英寸)上相对距离为40公里和60公里的离散点上收集的一年半的压力测量数据。内径管)。历史数据管道前和管道后清理活动的可用性进一步丰富了所提出的数据驱动方法。实验结果表明,所提出的预测监测策略能够跟踪整个管道和单个管道段的状况,从而确定管道的哪一部分受到最高的遮挡水平。此外,我们的方法允许实时采集和处理数据,从而使在线监测成为可能。通过评估回归问题统计分析中使用的典型度量来评估预测准确性。
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