Wireless Hydrogen Subsurface Sensing for Leaner Sensor Deployment

The patent replaces physical sensor deployment with machine learning-based virtual sensing. The ML model predicts subsurface conditions (temperature, pressure, gas composition) by processing data from existing wells and surface measurements, eliminating the need for numerous physical sensors in the reservoir. This substitution directly resolves the contradiction by maintaining measurement precision through computational methods while avoiding the complexity and cost of deploying large sensor networks.

The patent creates virtual copies of sensor measurements through machine learning predictions. Instead of deploying physical sensors throughout the reservoir, the system generates virtual sensor data by training ML models on available well data and using them to predict conditions at unsampled locations. This copying approach provides comprehensive monitoring coverage without the physical complexity of deploying actual sensors everywhere.

The patent introduces machine learning models as intermediary systems that process and interpret data from existing reliable sources (well logs, surface measurements) to infer subsurface conditions. Rather than relying on wireless sensors that struggle with communication reliability in harsh environments, the ML intermediary synthesizes accurate subsurface information from more reliable data sources, effectively bypassing the communication reliability problem while maintaining data acquisition capability.

The patent substitutes wireless sensor communication systems with machine learning-based data processing. Instead of using wireless sensors that face communication reliability issues in subsurface environments, the system uses ML models to process data from established communication-reliable sources (well testing equipment, surface sensors), thereby eliminating the communication reliability problem while achieving the same monitoring objectives.

The patent replaces physical measurement systems with machine learning-based virtual sensing. The ML models are trained on comprehensive datasets including well logs, production data, and surface measurements to learn complex subsurface relationships. Once trained, these models can accurately predict conditions throughout the reservoir using minimal input data, thereby maintaining measurement precision while dramatically reducing the need for physical sensors and associated system complexity.

The patent transforms the measurement approach by changing from direct physical measurement to computational prediction. The ML models process multiple input parameters (well logs, production rates, surface temperatures) to generate predictions of subsurface conditions. This parameter transformation allows the system to maintain high measurement precision through sophisticated data processing while using far fewer physical measurement points, thus reducing device complexity.