储气设施水合物形成预测方法及预防和消除措施的发展

V. Volovetskyi, Ya. Doroshenko, S. Matkivskyi, P. Raiter, O. Shchyrba, S. Stetsiuk, H. Protsiuk
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引用次数: 0

摘要

本工作的目的是研究井和地下储气设施运行过程中水合物的形成过程。制定了一套针对不同地质条件和工艺条件下储气设施井中水合物形成的预测和及时预防措施。利用神经网络进行水合物形成过程的预测,该神经网络是一种软件产品,具有在MATLAB环境中计算权重因子的能力,并且能够使指定的网络参数适应其运行过程中更新和补充的输入数据。因此,在MATLAB软件环境下,首先创建具有随机权重因子集的双层人工神经网络的软件模块。在第二阶段,使用试验场输入/输出数据集、输出数据集对神经网络进行训练。在第三阶段,利用人工神经网络作为预测水合物形成的手段,在其运行过程中,通过获得额外的更新数据作为输入集,对系数进行修改,从而改进人工神经网络的预测算法,从而能够细化权重因子。在没有新的数据用于人工神经网络的额外训练的情况下,它作为一种计算工具,在输入有关管道中流体的当前上述选定工艺参数的数据的基础上,确保输出值在0 ~ 1(或0 ~ 100%)范围内,表示管道控制段形成水合物的概率。这种方法的应用使教学成为可能;另外,,即改进神经网络;因此,这种预测水合物形成的客观手段提高了预测和系统运行过程中所得结果的可靠性。该工作的作者建议采取综合方法,以确保对井和天然气收集点的操作模式进行明确控制。根据实验研究结果,确定了地下储气设施中水合物最有可能沉积的位置,特别是在液体污染物聚集的管道内部空间(较低的管道段)和气体集结点可调节流口处。分析了油井和气田设备中防止和消除水合物形成的现有方法。通过这种分析,可以列出最适合乌克兰天然气储存设施条件的方法清单。提出了一种基于人工神经网络算法的特定管道段水合物生成预测方法。开发的方法基于管道某些部分的温度和压力值的数据,使我们能够高精度地预测水合物形成过程的开始,并采取适当的措施。为了提高解决储气设施水合物形成问题的效率,需要引入新的方法来及时预测并发症,特别是使用神经网络和多种措施。在地下储气设施中应用开发的预测方法、预防和消除井中水合物形成的方法和措施及工艺装备,将提高地下储气设施的运行效率。提出了利用人工智能预测井流线水合物地层和地下储气设施工艺装备的方法。使用这种方法来预测和运行系统作为一个整体,由于系统对指定控制条件的适应,确保了所获得结果的高可靠性。
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Development of methods for predicting hydrate formation in gas storage facilities and measures for their prevention and elimination
The purpose of this work is to study the processes of hydrate formation during the operation of wells and underground gas storage facilities. Development of a set of measures aimed at the prediction and timely prevention of hydrate formation in wells and technological equipment of gas storage facilities under different geological and technological conditions.The prediction of hydrate formation processes was carried out using a neural network that is a software product with weight factors calculated in MATLAB environment and the ability to adapt parameters of the network specified to updated and supplemented input data during its operation. So, within the MATLAB software environment, a software module of a two-layer artificial neural network with a random set of weight factors is created at the first stage. In the second stage, the neural network is trained using experimental field input/output data set, output data. In the third stage, an artificial neural network is used as a means of predicting hydrate formation with the ability to refine weight factors during its operation subject to obtaining additional updated data, as an input set, for modifying the coefficients and, accordingly, improving the algorithm for predicting of an artificial neural network. In the absence of new data for the additional training of an artificial neural network, it is used as a computing tool that, on the basis of input data about the current above-mentioned selected technological parameters of fluid in the pipeline, ensures the output values in the range from 0 to 1 (or from 0 to 100%), that indicates the probability of hydrates formation in the controlled section of the pipeline. Application of such an approach makes it possible to teach; additionally,, that is, to improve the neural network; therefore this means of predicting hydrate formations objectively increases reliability of results obtained in the process of predicting and functioning of the system.The authors of the work recommend to carry out an integrated approach to ensure clear control over the operation mode of wells and gas collection points.According to the results of experimental studies, the places of the most likely deposition of hydrates in underground gas storage facilities were identified, in particular, in the inside space of the flowline in places of accumulation of liquid contaminants (lowered pipeline sections) and an adjustable choke of the gas collection point. The available methods used to prevent and eliminate hydrate formation both in wells and at gas field equipment were analyzed. Such an analysis made it possible to put together a list of methods that are most appropriate for the conditions of gas storage facilities in Ukraine.The method of predicting hydrate formation in certain sections of pipelines based on algorithms of artificial neural networks is proposed. The developed methodology based on data on values of temperatures and pressures in certain sections of pipelines allows us to predict the beginning of the hydrate formation process at certain points with high accuracy and take appropriate measures.To increase the efficiency of solving the problem of hydrate formation in gas storage facilities, it is expedient to introduce new approaches to timely predict complications, in particular, the use of neural networks and diverse measures.Implementation of the developed predicting methodology and methods and measures to prevent and eliminate hydrate formation in wells and technological equipment in underground gas storage facilities will increase the operation efficiency of underground gas storage facilities.The use of artificial intelligence to predict hydrate formations in flowlines of wells and technological equipment of underground gas storage facilities is proposed. Using this approach to predict and functionthe system as a whole ensures high reliability of the results obtained due to adaptation of the system to the specified control conditions.
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来源期刊
Journal of Achievements in Materials and Manufacturing Engineering
Journal of Achievements in Materials and Manufacturing Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
2.10
自引率
0.00%
发文量
15
期刊介绍: The Journal of Achievements in Materials and Manufacturing Engineering has been published by the Association for Computational Materials Science and Surface Engineering in collaboration with the World Academy of Materials and Manufacturing Engineering WAMME and the Section Metallic Materials of the Committee of Materials Science of the Polish Academy of Sciences as a monthly. It has 12 points which was received during the evaluation by the Ministry of Science and Higher Education journals and ICV 2017:100 on the ICI Journals Master list announced by the Index Copernicus. It is a continuation of "Proceedings on Achievements in Mechanical and Materials Engineering" published in 1992-2005. Scope: Materials[...] Properties[...] Methodology of Research[...] Analysis and Modelling[...] Manufacturing and Processingv Biomedical and Dental Engineering and Materials[...] Cleaner Production[...] Industrial Mangement and Organisation [...] Education and Research Trends[...]
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