AI / Computer Vision Digital Twin for Oil & Gas FUG Component Counting

We are developing a digital-twin workflow for an oil & gas production facility using drone imagery, ground photography, video, LiDAR/RTK where available, and AI-assisted component identification. We need a freelancer with experience in computer vision, 3D reconstruction/digital twins, Python, Power BI, photogrammetry, LiDAR, or oil & gas process equipment to evaluate the existing methodology and recommend/build the most practical workflow to: reconstruct or organize the facility as a digital twin; identify and count fugitive-emission components such as valves, flanges/connectors, open-ended lines, instruments, gauges, relief valves, pump/compressor seals, sight glasses, etc.; assign each component a unique ID and avoid duplicate counting across multiple images; associate components with process service such as Gas/Vapor, Light Oil, or Water/Light Oil; create an auditable component database with image/location references and confidence level; generate summary tables, component counts, QA/QC views, and reports using Power BI, Python, or another suitable platform; recommend where existing AI models, object detection/segmentation, photogrammetry, or manual validation should be used. Deliverable: a concise technical recommendation plus a working prototype using a sample facility dataset, showing the proposed digital-twin/component-count workflow and final reporting format. Oil & gas production-facility experience is strongly preferred. Knowledge of YOLO/object detection, OpenCV, COLMAP/photogrammetry, point clouds/LiDAR, Python, Power BI, or GIS is a plus. The objective is not simply object detection; it is to create a repeatable, auditable methodology for regulatory fugitive-component inventory and counting.