Lessons Learned from Applying Quantitative Interpretation in Geothermal Projects

Quantitative seismic interpretation (QI) plays an increasingly important role in geothermal reservoir characterization, offering a pathway to reduce uncertainty and improve decision-making throughout the project lifecycle. However, applying QI to geothermal settings requires careful adaptation due to common challenges such as poor petrophysical data quality, sparse well coverage, legacy seismic datasets, and the need for uncertainty quantification.

This paper presents a set of practical solutions developed through real-world geothermal projects in the Paris Basin (France) and the Central Netherlands Basin (the Netherlands). Key workflows include rock physics–guided machine learning for direct prediction of elastic and petrophysical properties, and integration of seismic datasets of different vintages. We also demonstrate the use of neural networks and statistical sampling (Jackknife method) to generate multiple realizations and quantify prediction uncertainty.

The lessons learned from these case studies provide a flexible framework for adapting QI techniques to geothermal projects with variable data quality and project maturity. This approach enhances the reliability of reservoir property predictions and supports better-informed decisions in geothermal exploration and development.