Conservation Tracking

RCoE Action

Topic

Policy

Target

Title

This dataset maps regions across Africa with high geothermal energy exploration potential, identifying areas where further research and investment are most likely to yield significant returns. The mapping represents a synthesis of the Indicator Analysis performed by TNO (Hofstra, 2023), specifically selecting the most and least favorable technical outcomes under the assumption of a clay-poor sandstone (CP) compaction model.

To ensure structural robustness and account for geological uncertainty, the indicator results have been projected onto four distinct geological basin models:

BGS TARGET Model: Projecting indicator values onto basin shapefiles defined by the British Geological Survey.
Evenick Model: Utilizing sedimentary basin structures characterized by surface porosity and thickness according to Evenick’s framework.
Getech Model: Integrating regional basin delineations to refine spatial prospectivity based on gravity and crustal structure data.
Robertson CGG Model: Applying indicator analysis to high-resolution basin geometries to evaluate technical performance.

Key Indicators & Methodology

The favorable results displayed in these maps are derived from a multi-criteria evaluation which includes:

Thermal Performance: Integration of the 3D conductive thermal model to identify viable reservoir temperatures at depth.
Reservoir Quality: Assessment of potential hydraulic transmissivity based on depth-dependent porosity and permeability for sedimentary aquifers.
Economic Viability: Calculations involving the Levelized Cost of Energy (LCOE) to distinguish areas where energy extraction is most cost-effective.

This layer is part of the Geothermal Atlas for Africa developed within the LEAP-RE project.

These layers present the application of the Degree of Urbanisation stage I methodology recommended by UN Statistical Commission to the global population grid generated by the JRC in the epochs 1975-2030 (5 years timestep).

They derive from the GHS-SMOD - R2023A.

The layers have been generated by integration of built-up surface extracted from Landsat and Sentinel-2 image data processing (GHS-BUILT-S R2023), and population data derived from the CIESIN GPW v4.11 (GHS-POP R2023).

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

"This map shows, for each grid cell, the environmental impact in terms of CO₂-equivalent emissions per kilowatt-hour [g CO2 eq/kWh] generated by by the following systems:

Cooling Absorption System;
Refrigeration Absorption System;
Binary ORC Power Plant;
Flash Power Plant;
Heat for Direct Use;
Heat for High Temperature Heat Pump;

The maps refer to:

base case scenario;
upside scenario, modelling a better fluid flow;
up up side, modelling both better fluid flow and higher underground temperatures.

This layer is part of the Geothermal Atlas for Africa developed within the LEAP-RE project"

Modern energy services are crucial to human well-being and to a country’s economic development; and yet 1.2 billion people are without access to electricity. It is recognized that the central grid is unlikely to reach many remote areas in the near future: many of these communities will have low electricity consumption, making the costs of extending the grid unaffordable. Given the evident potential of solar energy for African countries, using stand-alone and mini-grid photovoltaic (PV) systems could be an alternative approach to meet the objective of universal electrification.

This dataset presents the ratio between the optimized battery size (kWh) and PV array size (kWp) for PV mini-grids using Li-ion batteries to store electricity (instead of traditional lead-acid batteries). It includes layers modeling two distinct usage behaviors:

Low energy consumption patterns: where the majority of energy is used during the night-time.
High energy consumption patterns: where the majority of energy is used during the day-time.

A higher ratio means that the battery size needed to satisfy the same electricity demand produced by the PV system is larger. Used in combination with other sources, these data can help governments, local authorities, and non-governmental organisations to investigate the suitability of PV mini-grids for the electrification of regions where access to electricity is lacking.

This dataset maps the critical structural and tectonic features across Africa that serve as key indicators for geothermal resource potential. These layers delineate "fault/fracture zones" which are considered a primary geothermal resource play type for both magmatic and non-magmatic settings.

The layers in the dataset are:

Active faults (BGS) – derived from the Trans-African Regional Geo-data for the Energy Transition (TARGET) model of the British Geological Survey;
Active Faults GEM) – compilled as part of the GEM's global Probablistic Seismic Hazard modeling efforts;
Major faults – derived from the Trans-African Regional Geo-data for the Energy Transition (TARGET) model of the British Geological Survey;
Major faults - East-African Rift System - showing the location of major faults in the East-African Rift System, derived from Chrowicz (2005), part of the East African Rift Temperature and Heat flow model (EARTH).

These fault layers are crucial for characterizing subsurface hydraulic properties. Specifically, the dataset's features are utilized to:

Determine Permeability and Transmissivity: Active fault zones are modeled to have high fault/fracture permeability, accounting for a hydraulic transmissivity of 20 Darcy-meters up to a depth of 3 km.
Define Reservoir Thickness: For performance calculations, the corresponding "aquifer" permeability within these active fault zones is set to 10,000 mDarcy with an assumed thickness of 20 meters.
Model Geothermal Production: These structural features are integrated into the "resource fast model" as thin reservoirs for geothermal doublet systems (systems with at least one production and one injection well) situated in fractured basement or sedimentary rock.
Support Spatial Analysis: The locations of these active faults are rasterized and combined with 3D temperature grids (where magmatic areas and active volcanoes exhibit elevated heat production) to calculate local geothermal energy conversion scenarios across the continent.

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project"

This dataset comprises a collection of shapefiles detailing the geographical locations and attributes of various wells across Africa, with a strong focus on geothermal exploration and assessment. Together, these layers offer a valuable resource for geothermal energy planning, subsurface geological analysis, and infrastructure mapping. The collection includes the following spatial data layers:

Wells: Maps the locations of various existing well types and provides critical geothermal metrics where available, including well names, temperature correction states, thermal gradients, heat flow, and heat production data.
BGS Well: A compiled spatial layer derived from scientific literature that categorizes well locations and types.
Planned wells: Highlights the prospective locations of future geothermal wells across the continent.

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project"