DARLCOLLINS-GEOTECH/Nigeria-Underground-Hydrogen-Storage-Suitability-Analysis

A GIS-based Multi-Criteria Decision Analysis (MCDA) project for identifying and prioritising potential underground hydrogen storage sites in Nigeria. The study integrates geological, infrastructural, environmental, and spatial datasets using Analytical Hierarchy Process (AHP) and weighted overlay analysis to support energy transition planning. # Identification and Prioritisation of Underground Hydrogen Storage Sites in Nigeria ## Overview This project presents a GIS-based Multi-Criteria Decision Analysis (MCDA) framework for identifying and prioritising suitable underground hydrogen storage (UHS) locations across Nigeria. Underground hydrogen storage is considered a critical technology for enabling large-scale renewable energy integration by providing long-duration energy storage capabilities. This project investigates Nigeria's geological and infrastructural potential for UHS deployment by integrating multiple spatial datasets and decision-support techniques. The analysis combines Geographic Information Systems (GIS), Analytical Hierarchy Process (AHP), and Weighted Linear Combination techniques to generate suitability maps for potential hydrogen storage development. ## Project Objectives The main objectives of this study were to: - Identify key geological, technical, infrastructural, and environmental factors influencing underground hydrogen storage suitability. - Develop a GIS-based MCDA framework for spatial site assessment. - Apply AHP to determine relative importance weights of evaluation criteria. - Generate suitability maps identifying priority areas for future hydrogen storage investigation. ## Methodology The workflow consisted of five major stages: ### 1. Data Collection Multiple spatial datasets were collected, including: - Geological data - Lithology - Hydrogeological characteristics - Fault density - Pipeline infrastructure - Energy hubs - Road networks - Digital Elevation Models (DEM) - Land Use/Land Cover (LULC) - Protected areas ### 2. Data Processing and Harmonisation Spatial datasets were processed using: - Coordinate system standardisation - Raster conversion - Spatial interpolation - Distance analysis - Terrain analysis - Reclassification All datasets were harmonised using WGS 84 UTM Zone 32N at a 100 m spatial resolution. ### 3. Multi-Criteria Decision Analysis Ni …