Porous-Rock Mineralization Simulation with Stochastic Nucleation
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Solution Overview
Problem
Existing computer simulators for fluid-rock processes in rock porous media lack the ability to accurately predict mineralization and storage of carbon dioxide due to their reliance on deterministic models that do not account for the probabilistic nature of nucleation and the influence of local fluid flow and geometry conditions in complex geometries.
Innovation Solution
A stochastic simulation method that statistically samples nucleation sites within a rock capillary network, incorporating fluid flow vectors, identifying hotspots of nucleation, and iteratively adjusting pore geometry to simulate mineral precipitation, considering the probabilistic nature of nucleation and local flow conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic models are used for physical property changes and chemical reactions, then the simulation process is simplified and computationally efficient, but the accuracy of predicting mineralization and CO2 storage is compromised due to inability to account for probabilistic nucleation and local flow conditions
Solution Approach 1:
The patent applies local quality by implementing stochastic sampling specifically at nucleation sites within the pore network, rather than applying deterministic models uniformly throughout. The simulation identifies specific locations (nucleation sites) where probabilistic mineralization processes occur, while other regions may use simplified approaches. This allows accurate modeling of mineralization at critical locations without requiring complex stochastic models everywhere, thus resolving the contradiction between prediction accuracy and overall model complexity.
Solution Approach 2:
The patent segments the simulation approach by dividing the pore network into distinct regions: nucleation sites where stochastic processes are applied, and other regions where deterministic models suffice. The method separates the complex mineralization process into identifiable stages (nucleation, growth, accumulation) and applies appropriate modeling techniques to each segment, enabling accurate mineralization prediction without requiring the entire system to use computationally intensive stochastic models.
2Reliability
If deterministic models are used for chemical reactions, then computational efficiency is maintained, but the influence of local fluid flow and geometry conditions on nucleation cannot be accurately captured
Solution Approach 1:
The patent applies local quality by implementing stochastic sampling specifically at nucleation sites within the pore network, rather than applying deterministic models uniformly throughout. The simulation identifies specific locations (nucleation sites) where probabilistic mineralization processes occur, while other regions may use simplified approaches. This allows accurate modeling of mineralization at critical locations without requiring complex stochastic models everywhere, thus resolving the contradiction between prediction accuracy and overall model complexity.
Solution Approach 2:
The patent applies partial action by using stochastic sampling only where necessary (at nucleation sites and hotspots) rather than throughout the entire domain. The method performs targeted probabilistic analysis at specific locations where mineralization is most likely to occur, based on local flow and geometry conditions, while avoiding the computational cost of applying stochastic models to the entire pore network. This selective approach maintains computational efficiency while capturing essential probabilistic effects.
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
Enables accurate prediction of mineralization and storage of carbon dioxide at a large scale by modeling the probabilistic onset of nucleation, improving the design of carbon dioxide sequestration equipment and enhancing the simulation of mineralization processes in complex rock structures.
Implementation Method 1
identifying one or more hotspots of nucleation in the rock capillary network; starting a mineral precipitation analysis and estimating a mineral accumulation
Implementation Method 2
starting a mineral precipitation analysis and estimating a mineral accumulation over a given time interval for at least the identified hotspots of nucleation
Data Source
AI summary
A representation of a rock capillary network is obtained and initial and boundary conditions of fluid flow and mineral precipitation process simulations for the rock capillary network are set. One or more instances of a geometry evolution simulation are performed, each geometry evolution simulation comprising obtaining fluid flow vectors for the rock capillary network, identifying one or more hotspots of nucleation in the rock capillary network, starting a mineral precipitation analysis and estimating a mineral accumulation over a given time interval for at least the identified hotspots of nucleation, adjusting a pore geometry to an effect of mineral precipitation for the rock capillary network, and iteratively repeating each of the one or more geometry evolution simulations until corresponding stop criteria are met for each geometry evolution simulation. A resulting rock under analysis property is computed from an aggregate of results of the one or more geometry evolution simulations.