Capillary Network Model for Porous Media Fluid Flow Simulation
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Solution Overview
Problem
Current digital rock analysis methods for enhanced oil recovery (EOR) fail to accurately model fluid flow in porous media due to oversimplification of geometry, neglecting spatial variations and dynamics, which leads to inaccurate predictions and inability to account for nanoscale effects.
Innovation Solution
A method is developed to generate a capillary network model from a three-dimensional physical representation of a porous rock sample, extracting geometrical parameters to simulate fluid flow with a fluid additive, using a system that includes sensors and software for detailed spatial and dynamic analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simplified balls-and-sticks representation is used to model porous media, then the computational complexity is reduced and the model is easier to operate, but the geometrical accuracy and manufacturing precision of the fluid flow simulation deteriorates
Solution Approach 1:
The patent segments the complex porous media geometry into discrete network elements (nodes representing pores and edges representing throats), creating a simplified graph structure that maintains essential flow pathways while reducing computational complexity. This segmentation allows the model to capture connectivity and flow dynamics without requiring full geometric detail.
Solution Approach 2:
The patent creates a topological copy of the porous media structure using graph theory, where the essential connectivity and flow characteristics are replicated in a simplified network representation. This copying approach preserves the functional behavior of fluid flow while eliminating unnecessary geometrical complexity.
2Productivity
If a low-dimensional representation is used to reduce computational complexity, then the processing speed improves, but the ability to capture spatial variations and nanoscale effects deteriorates
Solution Approach 1:
The patent transitions from continuous three-dimensional spatial coordinates to a discrete graph dimensionality where nodes and edges represent pore spaces and flow pathways. This dimensional transformation reduces computational complexity while preserving essential flow characteristics through topological relationships rather than geometric coordinates.
Solution Approach 2:
The patent changes the fundamental parameters from continuous spatial coordinates to discrete topological properties (node connections, edge conductances, pore volumes). This parameter transformation enables efficient computation while capturing the essential physics of fluid flow through modified governing equations that work on the discrete network structure.
3Productivity
If static heuristics and physical approximations are applied to simplify the model, then the computational efficiency improves, but the dynamic accuracy and reliability of fluid flow predictions deteriorates
Solution Approach 1:
The patent implements dynamic flow simulation on the pore network model, where fluid properties, pressures, and flow rates are updated iteratively based on conservation laws and constitutive relationships. This dynamic approach replaces static heuristics with physics-based calculations that adapt to changing flow conditions, improving prediction reliability while maintaining computational efficiency through the simplified network structure.
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 provides a computationally efficient and accurate simulation of fluid flow, enabling faster decision-making in oil and gas operations by predicting enhanced fluid recovery efficiency without the need for expensive field trials.
Implementation Method 1
generating a capillary network model of the porous rock sample based at least on the at least one geometrical parameter for simulating fluid flow inside the porous rock sample
Implementation Method 2
performing at least one simulation of a flow of the fluid through the capillary network model of the porous rock sample with a fluid additive to provide a predicted enhanced fluid recovery efficiency
Data Source
AI summary
A method is provided including receiving data corresponding to a three-dimensional physical representation of a porous rock sample; calculating a low-dimensional representation of a pore network in the porous rock sample based on the three-dimensional physical representation; extracting one or more geometrical parameter from the low-dimension representation; generating a capillary network model of the porous rock sample based at least on the at least one geometrical parameter for simulating fluid flow inside the porous rock sample; and performing at least one simulation of a flow of the fluid through the capillary network model of the porous rock sample with a fluid additive to provide a predicted enhanced fluid recovery efficiency.