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<p>Exploring hidden oil and gas reservoirs in complex multi-layer structural basins is critical yet highly challenging, due to the limitations of conventional methods such as reliance on indirect evidence, high costs, and low success rates. As shallow structural reservoirs are gradually depleted, the difficulty of exploring concealed reservoirs has intensified, making them the primary focus of exploration. To address this, a comprehensive framework was established: Sampling points were arranged in a grid at 1–1.5&#xa0;km intervals, combining microbial gene quantification with cumulative probability curve analysis, and leveraging hydrocarbon-responsive markers. These markers include <i>pmoA</i> for methane, <i>prmA</i> for light oil and <i>AlkB</i> for heavy oil, which were used to quantify surface soil anomalies in the structurally intricate Liuxi area. High-throughput quantitative PCR (qPCR) enabled precise detection, while regression-based cumulative probability curves stratified anomalies into tiers. This curve was validated against 3D seismic data, and Kriging interpolation was used to map favorable zones. Results revealed four favorable zones: <i>AlkB</i> dominated T2/T5 layers, and this phenomenon indicates heavy oil in stable traps; <i>pmoA</i> aligned with T3 gas reservoirs; <i>prmA</i> highlighted light oil migration in T3-T4 lithologic-structural traps. The method also verified no gas reservoirs in disrupted T2 zones and no mixed-phase oils in T4. This framework resolves fluid composition, migration pathways, and structural stability, offering a practical alternative to traditional methods. Its novelty lies in standardizing anomaly classification for microbial gene quantification and adapting cumulative probability analysis to complex multi-layer basins, validating it as a robust tool for unconventional reservoir characterization in heterogeneous settings and advancing microbial geochemistry in high-risk or data-poor exploration regions.</p>

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Gene quantification and exploration in identifying potential oil and gas areas in North China: a comprehensive study on subtle reservoir exploration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Delu Cao,
  • Ze He,
  • Min Zhang,
  • Jinjin Ti,
  • Shuaiwei Wang,
  • Weichao Sun,
  • Zhuo Ning

摘要

Exploring hidden oil and gas reservoirs in complex multi-layer structural basins is critical yet highly challenging, due to the limitations of conventional methods such as reliance on indirect evidence, high costs, and low success rates. As shallow structural reservoirs are gradually depleted, the difficulty of exploring concealed reservoirs has intensified, making them the primary focus of exploration. To address this, a comprehensive framework was established: Sampling points were arranged in a grid at 1–1.5 km intervals, combining microbial gene quantification with cumulative probability curve analysis, and leveraging hydrocarbon-responsive markers. These markers include pmoA for methane, prmA for light oil and AlkB for heavy oil, which were used to quantify surface soil anomalies in the structurally intricate Liuxi area. High-throughput quantitative PCR (qPCR) enabled precise detection, while regression-based cumulative probability curves stratified anomalies into tiers. This curve was validated against 3D seismic data, and Kriging interpolation was used to map favorable zones. Results revealed four favorable zones: AlkB dominated T2/T5 layers, and this phenomenon indicates heavy oil in stable traps; pmoA aligned with T3 gas reservoirs; prmA highlighted light oil migration in T3-T4 lithologic-structural traps. The method also verified no gas reservoirs in disrupted T2 zones and no mixed-phase oils in T4. This framework resolves fluid composition, migration pathways, and structural stability, offering a practical alternative to traditional methods. Its novelty lies in standardizing anomaly classification for microbial gene quantification and adapting cumulative probability analysis to complex multi-layer basins, validating it as a robust tool for unconventional reservoir characterization in heterogeneous settings and advancing microbial geochemistry in high-risk or data-poor exploration regions.