20240036231. STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM simplified abstract (BEYOND LIMITS, INC.)
Contents
STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM
Organization Name
Inventor(s)
Azarang Golmohammadizangabad of Los Angeles CA (US)
Shahram Farhadi Nia of Glendale CA (US)
STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240036231 titled 'STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM
Simplified Explanation
The patent application describes systems and methods for developing a reservoir using observed data points and well logs. Here is a simplified explanation of the abstract:
- Observed data points and well logs are received at a neural network.
- Feature vectors are generated based on the distance between observed data points and randomly generated points in the volume.
- A 3D populated log is generated by propagating well log values of the feature vectors across the volume.
- Uncertainty is quantified by generating multiple realizations of the 3D populated log, each equally probable but different.
- Core values are generated from the realizations.
- A static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.
Potential applications of this technology:
- Reservoir development and management in the oil and gas industry.
- Geological modeling and analysis for mining operations.
- Environmental monitoring and assessment of underground water resources.
Problems solved by this technology:
- Improved accuracy and efficiency in reservoir characterization and modeling.
- Better understanding of subsurface geological formations.
- Enhanced decision-making in resource extraction and management.
Benefits of this technology:
- More accurate prediction of reservoir behavior and performance.
- Reduced uncertainty in reservoir modeling and simulation.
- Improved resource allocation and optimization.
- Enhanced risk assessment and mitigation strategies.
Original Abstract Submitted
implementations described and claimed herein provide systems and methods for developing a reservoir. in one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. feature vectors are generated using the neural network. the feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. a 3d populated log is generated by propagating well log values of the feature vectors across the volume. uncertainty is quantified by generating a plurality of realizations including the 3d populated log. each of the realizations is different and equally probable. core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.