一种地质封存注入性的评估与参数确定方法和系统
By combining machine learning models and sensor data, the high cost and low efficiency of geological storage injection assessment are solved, achieving low-cost and high-efficiency injection assessment and parameter determination, which is applicable to fluid migration simulation under various geological conditions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- PEKING UNIV
- Filing Date
- 2026-03-18
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for assessing geological repositories and injections suffer from high computational costs, low efficiency, and difficulty in achieving accurate quantitative evaluation.
A surrogate prediction model based on machine learning is adopted. The target injection parameters are obtained by training the model using sample formation parameters and sample injection parameters. Combined with sensor data and high-performance computing units, low-cost and efficient injection assessment and parameter determination are achieved.
It achieves low-cost and efficient injection assessment and parameter determination, improves the accuracy and efficiency of formation injection assessment, and is applicable to fluid transport simulation under various geological conditions.
Smart Images
Figure CN121919581B_ABST