Rock Structure Simulation via Grain-Scale Non-Linearity Modeling
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Solution Overview
Problem
Current models simulating rock structures fail to accurately account for non-linear grain interactions, leading to inadequate representation of stress behavior, pore pressure, and acoustic wave propagation due to the omission of structural non-linearity.
Innovation Solution
A system that models substance characteristics based on structural non-linearity using electronic storage and processors to obtain and analyze structure information, including resonance frequencies, to generate a structure model that simulates grain-scale interactions and characteristics such as pore pressure, stress distribution, and force chains within rock structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional models are used to simulate rock structures, then the model complexity is low and ease of manufacture is improved, but the manufacturing precision and reliability of the simulation are insufficient due to omission of structural non-linearity
Solution Approach 1:
The patent segments the rock structure into discrete grain-scale elements, allowing the model to capture non-linear grain interactions while maintaining computational tractability. This segmentation enables accurate representation of stress distribution, pore pressure, and force chains without requiring overly complex continuous media models.
Solution Approach 2:
The patent transitions from traditional continuum mechanics to a grain-scale discrete element approach, adding a new dimensional perspective to the simulation. This dimensional change allows the model to explicitly represent individual grain interactions and their non-linear effects, significantly improving simulation accuracy for rock structure behavior.
2Reliability
If grain-scale interactions are incorporated into the model, then the reliability and measurement precision of stress behavior prediction are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary characterization of grain-scale properties and contact mechanics before conducting full stress behavior simulations. This preliminary action allows the model to use pre-defined grain interaction rules and material properties, reducing computational complexity during the main simulation while maintaining high prediction reliability for stress responses and pore pressure.
Solution Approach 2:
The patent creates simplified representative grain-scale models that replicate the essential non-linear interaction mechanisms of real rock structures. These copied grain models capture the critical force chain formations and stress transfer patterns without requiring exhaustive detail of every grain, thus balancing computational complexity with prediction reliability.
3Measurement precision
If structural non-linearity is accounted for in the model, then the measurement precision of acoustic wave propagation is improved, but the difficulty of detecting and measuring the parameters increases
Solution Approach 1:
The patent uses acoustic wave propagation through the grain-scale model to probe and detect structural non-linearity in rock formations. By analyzing how acoustic waves interact with individual grains and their non-linear contacts, the model can precisely predict acoustic wave behavior and infer subsurface rock properties, improving measurement precision while managing the complexity of parameter detection.
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
This approach enables more accurate simulation of rock structure behavior, improving the prediction of stress responses, pore pressure, and acoustic wave propagation by accounting for non-linear grain interactions, enhancing drilling operations and resource identification.
Implementation Method 1
transmission of one or more acoustic waves through the substance within the volume
Implementation Method 2
The structure information may be determined based on analysis of the resonance frequencies of the substance within the volume
Data Source
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AI summary
Structure information for a substance within a volume may be obtained. The structure information may characterize structural non-linearity of the substance within the volume. A structure model for the substance within the volume may be generated based on the structure information and/or other information. The structure model may simulate one or more characteristics of the substance within the volume. Presentation of information on the characteristic(s) of the substance within the volume may be effectuated based on the structure model and/or other information.