Case studies
Geothermal Reservoir Detection Using AI
Applying deep learning to seismic data for accurate identification of geothermal energy reservoirs
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Summary
This project applied cutting-edge deep learning techniques to the challenge of geothermal reservoir identification. Traditional exploration methods are expensive and time-consuming, often requiring extensive drilling campaigns with uncertain outcomes. Our AI-driven approach analyzes existing seismic surveys, geological maps, and geochemical data to predict the location and characteristics of subsurface geothermal reservoirs, dramatically reducing exploration risk and cost.
Duration
10 months
Team Size
6
01
Challenges
The main challenges included working with sparse and heterogeneous geological datasets spanning decades of collection methods, modeling complex 3D subsurface structures from 2D survey data, quantifying prediction uncertainty to guide drilling decisions, and validating model predictions against known reservoir locations while maintaining generalization to new regions.
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Innovation
We developed a novel 3D convolutional neural network architecture specifically designed for volumetric seismic data interpretation. The model incorporates geological priors through physics-informed loss functions that enforce thermodynamic constraints. A Bayesian uncertainty quantification layer provides confidence intervals for each prediction, enabling risk-aware decision making for drilling campaigns.
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Impact
The AI system identified three previously unknown geothermal prospects in test regions, two of which were confirmed through subsequent drilling. Exploration costs were reduced by 60% compared to traditional methods, and the time from initial survey to prospect identification was cut from 18 months to 3 months. The technology is now being deployed across multiple geothermal exploration programs.
Impact metrics
Our impact in the vertical
Quantifying our success to showcase how we bring transformative AI solutions to healthcare
85%
Detection Accuracy
60%
Exploration Cost Reduction
83%
Time-to-Identification Reduction
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