To address the complex layouts and dense inspection points of oilfield metering and water⁃injection facilities, where the conventional A* algorithm exhibits low search efficiency, this study proposes a path⁃planning method for oilfield inspection robots based on an improved weighted A* algorithm. First, metering rooms, water⁃injection rooms, stairways, doors, and various outdoor obstacles were modeled using the actual layout of the Shenyang Oil Production Plant of Liaohe Oilfield Company, and a regular 60×60 grid map was constructed. Multiple inspection targets were then defined according to the actual locations of on⁃site valves. A goal⁃distance⁃dependent dynamic weight function was introduced into the weighted A* framework to strengthen directional guidance during long⁃range planning while yielding smoother and more stable local paths near the target. An adaptive node projection mechanism and eight⁃neighborhood constraints were also incorporated to improve path feasibility and robustness in complex environments. In addition, a lightweight traveling salesman problem (TSP) heuristic based on the nearest⁃neighbor strategy was used to optimize the inspection sequence for indoor valves. Turning⁃point extraction was subsequently applied to simplify the planned paths and eliminate redundant nodes. The proposed method was validated in a three⁃dimensional simulation environment built using ROS2 and RViz. The results show that the improved weighted A* algorithm outperforms the conventional A* algorithm significantly in terms of path length, number of turning points, and trajectory smoothness, thereby enhancing the path⁃planning performance of oilfield inspection robots.