纠正现象学模型的可解释科学机器学习方法:在 ESP 原型上进行方法验证

IF 3.9 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Carine Menezes Rebello*, Erbet Almeida Costa, Marcio Fontana, Leizer Schnitman and Idelfonso B. R. Nogueira*, 
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引用次数: 0

摘要

电潜泵(ESP)被广泛应用于石油开采过程中,其作为石油行业人工提升技术的有效性已得到公认。开发和改进这些系统的预测模型有助于优化运行效率和最大限度地提高石油产量。这些改进有助于对静电除尘器的性能进行控制和监测,从而有可能使开采过程更具经济和环境可持续性。本文旨在开发一个框架,利用实验数据为 ESP 现象模型创建一个可解释的修正模型。采用神经网络来分析过程的复杂性和非线性,然后通过符号回归生成一个简化的可解释方程,以提高模型的预测能力。使用马尔科夫链蒙特卡罗(MCMC)对模型参数进行了不确定性评估,并将其传播到模型预测中。此外,为了进行比较,还直接从过程数据中创建了一个回归模型。比较分析表明,采用神经网络生成合成数据,然后进行符号回归的方法提高了模型预测关键变量(如进气压力、阻塞压力和生产流量)的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretable Scientific Machine Learning Approach for Correcting Phenomenological Models: Methodology Validation on an ESP Prototype

The electric submersible pump (ESP) is widely used in oil extraction processes and is recognized for its effectiveness as an artificial lift technique in the petroleum sector. Developing and improving predictive models for these systems can contribute to optimizing operational efficiency and maximizing oil production. These improvements facilitate ESP performance control and monitoring, potentially making the extraction process more economically and environmentally sustainable. This paper aims to develop a framework for creating an interpretable corrective model for the ESP phenomenological model using experimental data. Neural networks were employed to analyze the complexities and nonlinearities of the processes, followed by symbolic regression to generate a simplified and interpretable equation to improve the model’s predictive capacity. An uncertainty assessment of model parameters was performed using Markov chain Monte Carlo (MCMC) and propagated to the model prediction. Additionally, a regression model was created directly from the process data for comparison purposes. The comparative analysis indicated that the approach incorporating neural networks to generate synthetic data, followed by symbolic regression, improved the model’s ability to predict key variables such as intake pressure, choke pressure, and production flow.

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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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