GEOMIN Exchange

Fulfilling environmental and social obligations in order to secure funding and obtain the social licence to operate (SLO – Social Licence to Operate) is only the first step; maintaining a mining operation within a social context conducive to its activities requires the project’s ongoing integration into the local context, rigorous risk management and genuine acceptance by local communities.

This one-day short course provides a strategic and practical roadmap for navigating the E&S lifecycle of a project. Through four structured modules and concrete case studies, we will explore how to align international standards with local realities. The course will highlight critical milestones, common pitfalls, and practical strategies to avoid them—balancing stakeholder expectations, realistic timelines, and project developer constraints.

By the end of this course, participants have learnt the basic principles for:

• Navigating the bridge between national regulatory compliance and international E&S standards (such as IFC Performance Standards) to secure project financing.
• Translating baseline social studies and contextual intelligence into actionable mine design and project planning.
• Anticipating and managing high-risk social impacts, specifically around land access, compensation, and Resettlement Action Plans (RAPs).
• Designing and implementing effective stakeholder engagement strategies and Grievance Redress Mechanisms (GRMs) to mitigate operational risks and maintain the SLO.

E&S as a prerequisite for project approval and financing

International standards vs. local regulatory requirements

Case Study: The cost of E&S failure

From ESIA compliance to contextual intelligence

Social baseline studies and mine design integration

Cultural heritage and local identity management

Land access, compensation, and resettlement principles

Developing effective Resettlement Action Plans (RAPs)

Livelihood restoration and relocation challenges

Stakeholder mapping and engagement strategies

Grievance Redress Mechanisms (GRMs) as risk management tools

Case Study: E&S success and community support

Held as part of GEOMIN 2026, GEOMIN AI Labs is hosted by the Ministry of Mining Industry and Geology of the Republic of Uzbekistan and organized by SLB in partnership with SEG.

Designed for invited students, this hands-on AI challenge brings together geoscience, data, and digital innovation to tackle real-world challenges across the mining and energy sectors.

Participants will work with industry-relevant datasets and apply machine learning to challenges spanning mineral exploration, resource evaluation, geothermal energy, and environmental risk. Through a combination of online challenge development and an in-person final, participants will turn data into predictive models and demonstrate how AI-driven insights can support smarter technical and business decisions.

GEOMIN AI Labs will follow a hybrid competition format, beginning online and culminating in a live final during GEOMIN 2026.

Invited participants can register online to take part in GEOMIN AI Labs 2026.

Participants will join an introductory online session covering the AI Labs format, datasets, challenge requirements, and submission process. They will then work independently on their assigned challenge and submit their solution online by the specified deadline.

All eligible submissions will be evaluated against the defined selection criteria. The top 10 participants will be shortlisted to progress to the live final.

The top 10 finalists will present their solutions in person before the AI Labs jury. Finalists will demonstrate their approach, results, and the potential technical and business impact of their solution, followed by questions from the jury.

Winners will be announced and recognized during the GEOMIN 2026 Closing Ceremony on 16 September.

Participants will be assigned a challenge that applies machine learning to a real-world problem across exploration, resource evaluation, energy, or environmental risk.

Exploration (C1): Find Zinc Before Drilling
Drilling is highly capital-intensive. Build a binary classification model to support early-stage screening and identify high-potential zinc targets using low-cost surface geochemical and magnetic data.

Resources (C2): Estimate Gold Without Direct Assay
Gold can represent a high-value byproduct. Develop a regression model to estimate gold grades using depth and multi-element assay data, supporting the valuation of intervals where direct measurements are unavailable or delayed.

Energy (C3): Map Subsurface Heat for the Future
Deep thermal measurement wells can represent a significant cost in geothermal development. Train a regression model using complete well logs to predict subsurface temperatures in unmeasured wells, helping reduce uncertainty and inform completion decisions.

ESG (C4): Prevent Acid Mine Drainage
Effective management of acid-generating waste rock is critical to environmental protection. Build a binary classification model using multi-element assay data to proactively identify potentially acid-generating material and support appropriate waste-management decisions.

The Geological Survey Round Table serves as a strategic forum for leaders of national geological surveys to exchange perspectives on emerging priorities in mineral exploration, critical minerals, geological data management, and cross-border collaboration across the Africa-Middle East-Central Asia Super Region. Participation is by invitation only.