Abstract

This publication presents a comprehensive study on the integration of artificial intelligence with volcanic monitoring systems for Enhanced Quantum Geothermal (EQG) exploration utilizing the GEIOS-KAIGEN technological framework. Our research combines real-time seismic data from geocasing-embedded nanomechanical sensors with advanced machine learning algorithms to optimize nitrogen hybrid nanofoam injection protocols in complex volcanic environments. Through controlled laboratory simulations and extensive field deployment at the Cyprus Troodos ophiolite complex, we demonstrate significant improvements in geothermal resource characterization and extraction efficiency while maintaining comprehensive volcanic stability monitoring protocols.

The study encompasses diverse geological formations including ophiolites, peridotites, gabbros, sheeted dike complexes, and metamorphic derivatives within active and fossil volcanic systems. Our AI-GMS (Artificial Intelligence - Geothermal Management System) platform integrates Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Deep Q-Networks (DQN) for real-time optimization of multi-thread well operations and nanofoam stimulation parameters across heterogeneous lithological units.

Laboratory results demonstrate exceptional performance across all tested lithologies: pillow basalts showed 340% permeability enhancement with 94.2% AI prediction accuracy, sheeted dike complexes achieved 280% permeability improvement with 91.7% prediction accuracy, gabbroic sections exhibited 410% enhancement with 96.1% accuracy, and peridotite sequences demonstrated 520% permeability increase with 89.3% prediction accuracy for serpentine mineral stability. The nitrogen hybrid nanofoam system, incorporating functionalized silica, alumina, and magnetite nanoparticles, achieved optimal stimulation with minimal seismic impact across all geological units.

Field deployment results from the 18-month Cyprus Troodos operation show 39.7% average thermal extraction enhancement across the complete ophiolite sequence, with 847 monitored seismic events (M0.1-M2.3) remaining within acceptable safety parameters. The AI system maintained 99.4% uptime with 97.8% sensor survival rate in extreme temperature environments up to 180°C. Notably, ultramafic sections demonstrated 380% increase in CO₂ mineralization rates, establishing significant carbon sequestration co-benefits with 2.3 Mt CO₂ equivalent storage capacity.

The geocasing-embedded sensor network, featuring 75 sensors per 100m with nanomechanical strain resolution of 10⁻⁹, fiber optic distributed temperature sensing (±0.1°C accuracy), and real-time chemical composition analysis, enabled unprecedented multi-scale process monitoring. AI algorithms achieved lithological boundary detection accuracy of 94.7% and demonstrated successful transfer learning between laboratory and field conditions with 78% performance retention.

Predictive scenario modeling validated the system's capability for volcanic risk assessment, with 96.1% accuracy for structural instability detection at lithological contacts and 91.4% accuracy for serpentinization-induced volume changes. The integrated approach successfully managed complex geological processes from millisecond seismic events to months-long mineral alteration reactions across seven orders of magnitude in temporal scale.

This research establishes the first comprehensive AI-enhanced monitoring framework for EQG operations in volcanic environments, demonstrating safe and efficient geothermal development in complex multi-lithological settings while providing significant environmental co-benefits through enhanced carbon sequestration. The technology enables access to previously challenging geological environments and represents a paradigm shift toward intelligent, adaptive geothermal exploration systems.

Keywords

  • Enhanced Quantum Geothermal
  • AI-GMS optimization
  • nitrogen nanofoam stimulation
  • volcanic monitoring
  • ophiolite complex
  • multi-lithological systems
  • geocasing sensors
  • predictive analytics
  • carbon sequestration
  • GEIOS-KAIGEN technology