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<p>Reservoir uncertainty poses a significant challenge for the development and management of edge-water gas reservoirs, causing issues such as unexpected flow capacity, uneven pressure depletion, and unclear water invasion. Therefore, it is essential to integrate dynamic responses into static parameters for a reliable reservoir model. This study aims to improve history matching accuracy in edge-water gas reservoirs by developing a hierarchical workflow that updates multiscale parameters progressively. The proposed method consists of three calibration stages: (1) global tuning of zone-level pore volume and aquifer strength via genetic algorithm (GA), (2) regional transmissibility adjustment for flow connectivity, and (3) local grid-scale permeability refinement using a streamline-based inversion. Each stage effectively reduces mismatch at corresponding scales—shut-in bottom hole pressure (SBHP) at field level, flowing bottom hole pressure (FBHP), and cumulative water production (WPT) at well level, resulting in improved well-level response matching, accurate water breakthrough prediction, and minimal disruption to prior geological models. This approach not only enhances model reliability but also provides valuable insights into residual gas distribution, gas drainage areas and water invasion paths using dynamic streamline diagnostics. The power and utility of the proposed workflow is demonstrated by a field application of edge-water gas model at the Sichuan Basin, China. The unique multiscale calibration strategy integrates evolutionary and streamline-based methods, enabling efficient convergence while preserving geological realism.</p>

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A novel hierarchical multiscale calibration approach for complex edge-water gas reservoirs

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Ruxin Zhang,
  • Xiaohua Liu,
  • Jinchuan Hu,
  • ChouChou Zhang

摘要

Reservoir uncertainty poses a significant challenge for the development and management of edge-water gas reservoirs, causing issues such as unexpected flow capacity, uneven pressure depletion, and unclear water invasion. Therefore, it is essential to integrate dynamic responses into static parameters for a reliable reservoir model. This study aims to improve history matching accuracy in edge-water gas reservoirs by developing a hierarchical workflow that updates multiscale parameters progressively. The proposed method consists of three calibration stages: (1) global tuning of zone-level pore volume and aquifer strength via genetic algorithm (GA), (2) regional transmissibility adjustment for flow connectivity, and (3) local grid-scale permeability refinement using a streamline-based inversion. Each stage effectively reduces mismatch at corresponding scales—shut-in bottom hole pressure (SBHP) at field level, flowing bottom hole pressure (FBHP), and cumulative water production (WPT) at well level, resulting in improved well-level response matching, accurate water breakthrough prediction, and minimal disruption to prior geological models. This approach not only enhances model reliability but also provides valuable insights into residual gas distribution, gas drainage areas and water invasion paths using dynamic streamline diagnostics. The power and utility of the proposed workflow is demonstrated by a field application of edge-water gas model at the Sichuan Basin, China. The unique multiscale calibration strategy integrates evolutionary and streamline-based methods, enabling efficient convergence while preserving geological realism.