Diffusion Flux Inclusion for Reservoir Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current reservoir simulation models for hydrocarbon recovery often underestimate oil recovery due to the neglect of molecular diffusion, especially in fractured reservoirs with low permeability, leading to inefficient gas injection and early breakthrough, as they primarily rely on convection flux without accurately accounting for diffusion flux.
Innovation Solution
The method involves determining a Péclet number to assess the significance of diffusion in hydrocarbon recovery simulations, allowing for the selective inclusion or exclusion of diffusion models based on the flux ratio, thereby optimizing gas injection and well placement by considering both convection and diffusion fluxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffusion flux is included in the reservoir simulation model, then the accuracy of hydrocarbon recovery predictions is improved, but the computational complexity and time required for simulation increases
Solution Approach 1:
The simulation domain is segmented into two distinct regions: fractures and matrix blocks. This segmentation allows the application of different physical models to each region, with diffusion flux explicitly modeled in the matrix blocks while maintaining computational efficiency through the dual-porosity framework. The segregation of scales and processes enables accurate prediction without uniformly complex modeling throughout the entire reservoir.
Solution Approach 2:
The model dynamically adjusts the treatment of diffusion based on the Péclet number, which characterizes the relative importance of convective versus diffusive transport. When the Péclet number indicates diffusion dominance, the model activates diffusion flux calculations; when convection dominates, the model simplifies the treatment. This dynamic adaptation maintains accuracy where needed while reducing computational burden where simpler models suffice.
2Device complexity
If diffusion flux is neglected in favor of convection flux only, then the computational complexity is reduced, but the accuracy of oil recovery prediction deteriorates leading to underestimation
Solution Approach 1:
Rather than fully implementing complex diffusion modeling throughout the entire reservoir, the model applies diffusion flux calculations selectively in regions where it is most impactful—specifically within the matrix blocks and at fracture-matrix interfaces. This partial application of diffusion modeling captures the essential physics without the excessive computational cost of universal diffusion modeling, achieving adequate accuracy with reduced complexity.
Solution Approach 2:
The model uses the Péclet number as a key parameter to determine when and where diffusion flux should be included in the simulation. By changing the modeling approach based on this dimensionless parameter, the system adapts its complexity to match the physical conditions, avoiding unnecessary computational overhead in convection-dominated regions while maintaining accuracy in diffusion-influenced zones.
3Ease of operation
If gas injection is performed without considering diffusion flux, then the injection process is simpler to implement, but early breakthrough occurs and gas utilization efficiency decreases
Solution Approach 1:
The model performs preliminary assessment of diffusion importance through the Péclet number calculation before conducting the full gas injection simulation. This preliminary action identifies regions where diffusion will significantly impact gas distribution and oil recovery, allowing operators to anticipate potential early breakthrough issues and adjust injection strategies accordingly, rather than discovering inefficiencies during or after the injection process.
Solution Approach 2:
The simulation model incorporates diffusion flux calculations that provide feedback on gas distribution patterns and recovery efficiency. By including diffusion effects in the predictive model, operators receive accurate feedback on how gas will actually distribute through the reservoir, enabling optimization of injection rates and well placement to maximize gas utilization and avoid early breakthrough, thereby improving productivity despite increased modeling complexity.
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 recovery predictions by accounting for diffusion, potentially increasing oil recovery and optimizing gas injection strategies, while conserving computational time by switching between diffusion-inclusive and exclusive models based on the Péclet number threshold.
Implementation Method 1
molecular diffusion between the injected gas inside fractures and the fluids stored in the reservoir rock
Implementation Method 2
convection flux without accurately accounting for diffusion flux
Data Source
AI summary
A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.