Hydrogen Prospect Fracture Network Prediction From Fault Data
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Solution Overview
Problem
Current hydrogen exploration technologies are inefficient and environmentally costly, hindering the widespread adoption of hydrogen-based energy solutions due to high costs and negative environmental impact.
Innovation Solution
A method and system utilizing fault data, including earthquake data, to identify and predict hydrogen generation potential in geological formations by analyzing fracture networks and hydration reactions, facilitating the identification of active fault zones as conduits for water influx and hydrogen migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current hydrogen exploration technologies are used, then hydrogen production can be achieved, but the cost is high and environmental impact is negative
Solution Approach 1:
The patent replaces traditional mechanical drilling and extraction methods with a chemical reaction-based system. Water is injected into ultramafic rock formations to induce serpentinization reactions that naturally produce hydrogen, eliminating the need for high-energy mechanical processing and reducing operational costs while improving environmental sustainability
Solution Approach 2:
The patent changes the fundamental approach from extracting existing hydrogen to generating hydrogen in situ through controlled chemical reactions. By altering parameters such as water injection pressure, temperature conditions, and rock permeability, the system creates optimal conditions for serpentinization, thereby reducing exploration costs and environmental disturbance
2Productivity
If traditional hydrogen exploration methods are used, then hydrogen can be produced, but efficiency is low
Solution Approach 1:
The patent performs preliminary characterization of geological formations using machine learning models to identify suitable ultramafic rock targets before injection begins. This preliminary action includes analyzing rock composition, permeability, and structural features, thereby reducing exploration time and increasing production efficiency by targeting only viable formations
Solution Approach 2:
The patent uses machine learning models trained on existing geological data and successful serpentinization cases to predict outcomes and optimize injection parameters. This copying of knowledge from past successes accelerates the exploration process and improves efficiency by avoiding trial-and-error approaches
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the efficiency and reduces the cost of hydrogen production by accurately predicting hydrogen generation in geological formations, thereby improving the economic and environmental viability of hydrogen exploration.
Implementation Method 1
water influx and hydrogen migration... analyzing fracture networks and hydration reactions
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for hydrogen production. Systems and methods provided herein may include obtaining one or more hydrogen prospects, the one or more hydrogen prospects selected based on one or more parameters. Systems and methods provided herein may include constructing one or more fracture networks based on the one or more hydrogen prospects, the one or more fracture networks each having a source rock area value. Systems and methods provided herein may include, based on the source rock area value for each of the one or more hydrogen prospects, generating a source rock exposure value for each of the one or more prospects. Systems and methods provided herein may include outputting a production prediction based on the source rock exposure value for each of the one or more hydrogen prospects.