Volume 44 Issue 1
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CHEN Shuangling,YU Jingdu,ZHENG Min,et al. An improved method for determining the coefficient of resource scale variation (k) in reservoir size sequential analysis and its application case[J]. Bulletin of Geological Science and Technology,2025,44(1):15-24 doi: 10.19509/j.cnki.dzkq.tb20230371
Citation: CHEN Shuangling,YU Jingdu,ZHENG Min,et al. An improved method for determining the coefficient of resource scale variation (k) in reservoir size sequential analysis and its application case[J]. Bulletin of Geological Science and Technology,2025,44(1):15-24 doi: 10.19509/j.cnki.dzkq.tb20230371

An improved method for determining the coefficient of resource scale variation (k) in reservoir size sequential analysis and its application case

doi: 10.19509/j.cnki.dzkq.tb20230371
More Information
  • Objective

    Sequential reservoir size analysis based on the Pareto principle encounters challenges in accurately determining the coefficient 'k', which quantifies the gradient of resource scale variation, thereby limiting the approach's effectiveness.

    Methods

    This study proposes an optimized methodology for calculating 'k' by analyzing the scale ratio of discovered resources and employing possible sequential numbers. This approach involves establishing a cross-plot with sequential numbers and k-axis values based on calculated ratios, locating combinations of data points from various ratios that form approximately straight vertical lines against the k-axis, and identifying the intersection points on the k-axis as solutions for k. Further optimization principles are suggested to enhance result selection to meet resource assessment requirements.

    Results

    Reanalysis of classic datasets from academic literature validated the methodology's capability in accurately determining the coefficient (k). A case study of tight gas reservoirs, specifically the 6th group of Jurassic Shaximiao formations in the Yanting Block of the Jinqiu gas-producing area in the central Sichuan Basin, demonstrated favorable linear fitting results between forecasted and actual data. The calculated resource scale is in strong alignment with established tight gas exploration outcomes in the Sichuan Basin.

    Conclusion

    The proposed methodology reduces reliance on geological experience, eliminates the need for complex determinant models or matrix manipulations, and minimizes subjectivity and computational complexity in parameter selection. Additionally, the algorithm is available as a coded computer program, enhancing its practical efficiency and applicability in sequential reservoir size methods.

     

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