In large-scale reservoir numerical simulations involving millions of nodes and development area-level complexity, traditional history matching methods have proven inadequate in addressing big data requirements, necessitating a shift from well-by-well adjustments to block-level, regionally-focused approaches. To this end, we have introduced the concepts and technical tools of Geographic Information Systems (GIS), devising a three-step workflow: First, GIS heatmap Analysis for Error Visualization: Employing GIS heatmap functionality, we visually represent errors in key parameters such as water cut, pressure, and injection rates across the entire simulated block, pinpointing areas of significant parameter deviation with ease; Second, GIS Regional Selective for Efficient Grid Modifications: Utilizing GIS selection capabilities, we batch-select and modify relevant attribute parameters within specific reservoir regions, greatly enhancing the efficiency of model grid alterations; Third, GIS-Enabled Data Integration for Targeted Parameter Refinement: Leveraging GIS geographical attributes, we link reservoir fluid experimental historical data to the simulation grid, enabling informed, targeted adjustments to attribute values. Technologically, these features have been realized through the use of Python programming language in conjunction with the open-source QGIS geographic information system software. Application results demonstrate that the regional parameter adjustment strategy effectively addresses the limitations of traditional well-centric methods, proving suitable for large-scale reservoir simulations. It has led to a marked improvement in history matching efficiency and a strengthening of the causality inherent in the fitted parameters. This approach successfully tackles the complexities inherent in large-scale reservoir numerical simulations, fostering spatio-temporal dynamic analyses of reservoir behavior, and laying a robust foundation for the future development of intelligent history matching technologies.