20240036231. STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM simplified abstract (BEYOND LIMITS, INC.)

STATIC ENGINE AND NEURAL NETWORK FOR A COGNITIVE RESERVOIR SYSTEM

Organization Name

BEYOND LIMITS, INC.

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.