Pore Network Model for Hydrocarbon Extraction Mass Transfer
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Solution Overview
Problem
Current computer models for hydrocarbon extraction from subterranean reservoirs struggle to accurately model mass transfer between solvents and hydrocarbons due to the complexity of the mixing zone at the solvent-hydrocarbon interface, particularly in solvent-based extraction processes, where large-scale numerical models fail to capture the nonlinear relationships and microstructural effects.
Innovation Solution
A dynamic pore network model is employed to simulate hydrocarbon extraction, representing the subterranean reservoir as a network of interconnected pores and throats, allowing for detailed simulation of phase conditions, molar balances, and mass transfer over time, with operational parameters adjusted to optimize extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If large-scale numerical models are used for reservoir modeling, then the entire subterranean reservoir can be represented, but the mixing zone between solvent and hydrocarbons cannot be effectively modeled due to its thin scale
Solution Approach 1:
The patent divides the reservoir modeling into two distinct segments: a pore-scale model that resolves the mixing zone in detail at the millimeter scale, and a reservoir-scale model that covers the entire reservoir at the meter scale. These segments are coupled through interface conditions, allowing each to operate at its appropriate resolution without compromising the other.
Solution Approach 2:
The pore-scale model is nested within the reservoir-scale model, with the pore-scale domain embedded in the larger reservoir domain. The pore-scale mixing zone model provides detailed mass transfer information that is then used as input for the broader reservoir-scale simulation, creating a hierarchical modeling structure.
2Productivity
If generalized Darcy's law is applied for two-phase flow modeling, then reservoir-scale flow can be predicted, but nonlinear relationships in the mixing zone are not captured
Solution Approach 1:
The patent applies different modeling approaches to different spatial regions: generalized Darcy's law is used in the bulk reservoir regions where linear assumptions hold, while a detailed pore-scale model with full nonlinear mass transfer equations is applied specifically in the mixing zone where nonlinear effects dominate. This local differentiation ensures accuracy where needed while maintaining computational efficiency elsewhere.
Solution Approach 2:
The model transitions between different parameter regimes by switching between Darcy-scale parameters (permeability, viscosity at reservoir scale) and pore-scale parameters (capillary pressure, interfacial tension, concentration gradients at mixing zone scale). This allows the system to capture nonlinear behavior in the mixing zone while using simplified linear relationships in the bulk reservoir.
3Measurement precision
If pore-scale modeling is used for mixing zone, then mass transfer can be accurately captured, but computational complexity increases significantly
Solution Approach 1:
Instead of applying full pore-scale modeling to the entire reservoir, the patent applies pore-scale detail only to the necessary extent - specifically within the mixing zone where mass transfer accuracy is critical. The bulk reservoir continues to use simplified Darcy-scale modeling, thus achieving sufficient accuracy with reduced computational complexity.
Solution Approach 2:
The patent introduces an intermediary coupling layer that connects the pore-scale mixing zone model with the reservoir-scale Darcy model. This intermediary layer translates between the different scales and physics, managing the complexity by providing a standardized interface rather than requiring direct coupling of all pore-scale details throughout the entire reservoir model.
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 enhances the accuracy of hydrocarbon extraction modeling by capturing transient pore-scale mechanisms and optimizing operational parameters, leading to improved extraction efficiency and operational parameters for solvent injection.
Implementation Method 1
mass transfer between a vapor chamber formed by an injected solvent and the hydrocarbons
Implementation Method 2
deriving, for each of the plurality of pores based on phase conditions in the pore, a second set of characteristics for the pore from the first set of characteristics for the pore
Implementation Method 3
computer modelling of the extraction process is often based on an application of Darcy's law in a reservoir model
Data Source
AI summary
A computer system models a hydrocarbon extraction process using a dynamic pore network model that is generated to represent a subterranean reservoir containing hydrocarbons as pores connected by throats. Solvent is injected into the subterranean reservoir to mobilize the hydrocarbons for extraction thereof. An iterative process may be repeated over time to determine hydrocarbon extraction based on changes in the molar balance of the components over time. A first set of characteristics for each pore is defined from which a second set of characteristics can be derived for two-phase pores. The molar balance of the components is determined based on the first and second sets of characteristics. The first set of characteristics is updated based on the molar balance of the components and the process can be repeated for subsequent times. Parameters for injection of the solvent may be adjusted and the iterative process repeated over time to identify preferred parameters.