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<p>The accurate prediction of crude oil production is crucial for effective management of oil reservoir operations. This paper leverages recent advancements in machine learning techniques and metaheuristic optimization algorithms, specifically deep learning (DL) and metaheuristic (MH) approaches, to construct a robust and efficient oil production prediction model. Real-world datasets from two diverse countries, Yemen and China, are employed in model development. The study focuses on optimizing a multilayer perceptron (MLP) using the Runge–Kutta optimizer (RUN). The primary goal is to enhance the MLP parameters through the application of the RUN algorithm. Rigorous evaluation experiments gauge the efficacy of the resulting prediction model (RUN-MLP), demonstrating impressive performance across three widely recognized evaluation metrics: root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10115_2025_2415_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation>). Comparative analyses involve multiple MLP-modified models employing various MH algorithms, with the RUN-MLP consistently exhibiting competitive performance. The findings underscore the computational efficiency of the RUN optimization algorithm. Additionally, the study employs the Friedman test as a statistical tool to elucidate differences between RUN and its competitors.</p>

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Optimized neural networks for efficient modeling of crude oil production

  • 期刊论文

  • 发表日期：

    2025年4月18日

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  • Ahmed A. Ewees,
  • Mohammed A. A. Al-qaness,
  • Hung Vo Thanh,
  • Ayman Mutahar AlRassas,
  • Mohamed Abd Elaziz

摘要

The accurate prediction of crude oil production is crucial for effective management of oil reservoir operations. This paper leverages recent advancements in machine learning techniques and metaheuristic optimization algorithms, specifically deep learning (DL) and metaheuristic (MH) approaches, to construct a robust and efficient oil production prediction model. Real-world datasets from two diverse countries, Yemen and China, are employed in model development. The study focuses on optimizing a multilayer perceptron (MLP) using the Runge–Kutta optimizer (RUN). The primary goal is to enhance the MLP parameters through the application of the RUN algorithm. Rigorous evaluation experiments gauge the efficacy of the resulting prediction model (RUN-MLP), demonstrating impressive performance across three widely recognized evaluation metrics: root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination ( \(R^2\) R 2 ). Comparative analyses involve multiple MLP-modified models employing various MH algorithms, with the RUN-MLP consistently exhibiting competitive performance. The findings underscore the computational efficiency of the RUN optimization algorithm. Additionally, the study employs the Friedman test as a statistical tool to elucidate differences between RUN and its competitors.