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<p>This study uses advanced deep learning techniques to present an efficient and data-driven method for predicting the Hydrocarbon Reservoir Quality Index (HRQI) in deepwater carbonate systems. Traditional approaches like core sampling and Nuclear Magnetic Resonance logging are often costly and limited in data coverage, particularly in older wells. To overcome these limitations, the study utilizes 5112 petrophysical log samples from three wells located in a Middle Eastern deepwater carbonate reservoir. Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. The Adam-LSTM model outperformed the others, achieving a Root Mean Square Error of 0.009 and a correlation coefficient (R<sup>2</sup>) of 0.9995, indicating excellent predictive performance. Feature analysis revealed RHOB, DT, NPHI, and Vp as the most significant inputs for predicting HRQI. The model enhances reservoir characterization by capturing complex lithological patterns, supporting better zonation and well placement. Its scalability and cost-effectiveness make it especially useful in settings with limited data availability. This research highlights the practical value of integrating artificial intelligence into reservoir evaluation, offering a powerful tool for improving decision-making in deepwater hydrocarbon exploration and development.</p>

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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Xiao Hu,
  • Jun Xie,
  • Xiwei Li,
  • Junzheng Han,
  • Zhengquan Zhao,
  • Hamzeh Ghorbani

摘要

This study uses advanced deep learning techniques to present an efficient and data-driven method for predicting the Hydrocarbon Reservoir Quality Index (HRQI) in deepwater carbonate systems. Traditional approaches like core sampling and Nuclear Magnetic Resonance logging are often costly and limited in data coverage, particularly in older wells. To overcome these limitations, the study utilizes 5112 petrophysical log samples from three wells located in a Middle Eastern deepwater carbonate reservoir. Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. The Adam-LSTM model outperformed the others, achieving a Root Mean Square Error of 0.009 and a correlation coefficient (R2) of 0.9995, indicating excellent predictive performance. Feature analysis revealed RHOB, DT, NPHI, and Vp as the most significant inputs for predicting HRQI. The model enhances reservoir characterization by capturing complex lithological patterns, supporting better zonation and well placement. Its scalability and cost-effectiveness make it especially useful in settings with limited data availability. This research highlights the practical value of integrating artificial intelligence into reservoir evaluation, offering a powerful tool for improving decision-making in deepwater hydrocarbon exploration and development.