基于 MDS-SSA-GNN 的新型抽水井系统混合效率预测模型

IF 3.5 3区 工程技术 Q3 ENERGY & FUELS
Biao Ma, Shimin Dong
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引用次数: 0

摘要

预测油井抽油系统的效率对于优化这些系统的能效参数具有重要作用。目前,油井抽油机系统效率的预测主要依靠机理模型,但这些模型在预测效率时往往过于复杂。一些研究人员尝试利用深度学习来预测系统效率,但由于没有充分考虑影响因素以及这些因素与系统效率之间的因果关系,往往将无关变量作为影响因素,导致预测模型的准确性较低。本文提出了一种用于预测抽水井系统效率的混合模型(MDS-SSA-GNN)。该模型由六个部分组成:皮尔逊积矩相关系数(PPMCC)、多维标度(MDS)变换、最大最小归一化、麻雀优化算法(SSA)、图神经网络(GNN)和最大最小反归一化。首先,利用 PPMCC 定量计算各影响因素与系统效率之间的相关系数大小。其次,利用 MDS 对主要影响因素进行降尺度处理,并根据最大-最小归一化原则进行归一化处理。第三,利用 GNN 算法对抽水机组系统效率进行预测,并利用 SSA 算法对网络参数的初始值进行优化。最后,通过最大最小反规范化得到预测结果。为了验证模型的准确性,本研究随机选取了 100 口实际油井进行对比分析,并分析了混合算法的结构参数对系统效率预测精度的影响。分析结果表明,所提出的模型能有效预测系统效率,对提高油井抽油机系统效率预测的准确性有一定作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A novel hybrid efficiency prediction model for pumping well system based on MDS–SSA–GNN

A novel hybrid efficiency prediction model for pumping well system based on MDS–SSA–GNN

The prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions.

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来源期刊
Energy Science & Engineering
Energy Science & Engineering Engineering-Safety, Risk, Reliability and Quality
CiteScore
6.80
自引率
7.90%
发文量
298
审稿时长
11 weeks
期刊介绍: Energy Science & Engineering is a peer reviewed, open access journal dedicated to fundamental and applied research on energy and supply and use. Published as a co-operative venture of Wiley and SCI (Society of Chemical Industry), the journal offers authors a fast route to publication and the ability to share their research with the widest possible audience of scientists, professionals and other interested people across the globe. Securing an affordable and low carbon energy supply is a critical challenge of the 21st century and the solutions will require collaboration between scientists and engineers worldwide. This new journal aims to facilitate collaboration and spark innovation in energy research and development. Due to the importance of this topic to society and economic development the journal will give priority to quality research papers that are accessible to a broad readership and discuss sustainable, state-of-the art approaches to shaping the future of energy. This multidisciplinary journal will appeal to all researchers and professionals working in any area of energy in academia, industry or government, including scientists, engineers, consultants, policy-makers, government officials, economists and corporate organisations.
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