Digital Rock Model for Nanoscale Fluid-Solid Interaction Simulation
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Solution Overview
Problem
Current digital rock analysis (DRA) methods fail to accurately model fluid-fluid and fluid-solid interactions at the nanoscale, particularly in porous media, limiting the prediction of enhanced oil recovery (EOR) efficiency and neglecting surface chemistry and interactions crucial for optimizing oil recovery processes.
Innovation Solution
A system and method that construct a digital model of a porous rock sample, incorporating voxel-scale fluid-solid interactions and using advanced computational fluid dynamics to simulate fluid flow with additives, such as chemicals and nanoparticles, to predict enhanced oil recovery efficiency by calibrating fluid flow models to match established figures-of-merit for full-sample dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital rock analysis methods are used, then the modeling process is simple, but the accuracy of fluid-fluid and fluid-solid interactions at the nanoscale is insufficient
Solution Approach 1:
The patent segments the digital rock model into multiple scales (micrometer and nanoscale voxels) to capture fluid-solid interactions at different levels. The rock sample is divided into a fine-grid digital core model with nanoscale resolution for critical regions, while coarser resolution is used for larger-scale flow patterns, enabling accurate nanoscale interaction modeling without requiring uniform ultra-fine resolution throughout the entire model.
Solution Approach 2:
The patent implements a nested modeling approach where nanoscale digital twin models are embedded within the larger micrometer-scale digital rock model. The nanoscale voxels contain detailed surface chemistry and fluid-solid interaction information, while being nested within the broader micrometer-scale pore network structure, allowing multi-scale phenomena to be captured hierarchically.
2Measurement precision
If field trials are conducted to optimize oil recovery enhancers, then accurate EOR efficiency data is obtained, but expensive and time-consuming operations are required
Solution Approach 1:
The patent creates a virtual digital twin copy of the actual rock sample that replicates its pore structure, surface chemistry, and fluid flow characteristics. This digital copy serves as a virtual laboratory where numerous EOR scenarios can be tested without physical field trials. The digital twin is calibrated against limited experimental data and then used to predict EOR efficiency for various additives and conditions, replacing expensive and time-consuming field experiments.
Solution Approach 2:
The patent performs preliminary screening and optimization of EOR additives using the calibrated digital rock model before conducting actual field trials. By pre-testing various chemicals and nanoparticles in the virtual environment, the most promising candidates are identified in advance, reducing the number and cost of subsequent field trials and accelerating the overall optimization process.
3Productivity
If surface chemistry and nanoscale interactions are neglected, then the modeling process is simpler, but the optimization of oil recovery processes is limited
Solution Approach 1:
The patent applies local quality by assigning different levels of chemical and physical property detail to different regions and scales of the model. At the nanoscale voxel level, detailed surface chemistry parameters (contact angles, surface energy, wettability) are incorporated for fluid-solid interactions. At the micrometer scale, these are upscaled to effective pore-level properties. This allows comprehensive surface chemistry modeling only where and when needed, rather than uniformly throughout the entire model.
Solution Approach 2:
The patent incorporates surface chemistry effects by dynamically adjusting fluid-solid interaction parameters (such as contact angles, adhesion forces, and wettability coefficients) based on the presence of EOR additives. The model can modify these parameters to reflect chemical interactions between additives and rock surfaces, capturing phenomena like surfactant adsorption, nanoparticle attachment, and wettability alteration without requiring a completely separate chemical simulation framework.
Data Source
AI summary
A method includes constructing a digital model of a porous rock sample using input data and establishing for the digital model of a porous rock sample and for a fluid of interest figures-of-merit that are established for full-sample dimensions. For a selected fluid flow model, the method performs a calibration so as to match parameters of the selected fluid flow model to the established figures-of-merit and, based on the calibrated fluid flow model, performs at least one simulation of a flow of the fluid through the digital model of a porous rock sample with a fluid additive to provide a predicted enhanced fluid recovery efficiency. Also disclosed is a system as well as a computer program product configured to implement the method.