Locally Lumped EOS Fluid Characterization for Reservoir Simulation
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Solution Overview
Problem
Existing EOS fluid characterization methods are inefficient in modeling hydrocarbon reservoirs due to the need for thousands of components, which increases computational costs and processing time, especially when dealing with varying recovery mechanisms across a reservoir.
Innovation Solution
A locally lumped EOS characterization method that groups components into a smaller number of pseudo-components, allowing for adaptive characterization that varies across the reservoir based on different recovery mechanisms, reducing the number of components needed for accurate phase behavior calculations and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thousands of components are used to accurately represent hydrocarbon reservoir fluids, then the accuracy of phase behavior calculations is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the reservoir into multiple regions based on different recovery mechanisms, and applies different component lumping strategies to each region. This segmentation allows accurate representation of phase behavior in each specific region while reducing the overall computational burden by not using thousands of components across the entire reservoir.
Solution Approach 2:
The patent implements locally-adapted EOS characterizations where the number and properties of pseudo-components are optimized for each specific recovery mechanism region. This local quality approach ensures high accuracy for each region's phase behavior calculations while minimizing the total number of components needed, thereby reducing processing time.
2Measurement precision
If thousands of components are used to accurately represent hydrocarbon reservoir fluids, then the accuracy of phase behavior calculations is improved, but the computational cost increases greatly
Solution Approach 1:
By segmenting the reservoir into regions with different recovery mechanisms and applying appropriate component lumping to each, the patent achieves accurate phase behavior calculations without the need to process thousands of components across the entire system, thereby reducing computational cost.
Solution Approach 2:
The locally-adapted EOS characterizations optimize the balance between accuracy and computational cost by using the minimum necessary number of components for each specific region's recovery mechanism, avoiding the excessive computational burden of using thousands of components system-wide.
3Measurement precision
If the number of lumped components is increased to improve fluid property matching, then the agreement with laboratory data is improved, but the computational efficiency decreases
Solution Approach 1:
The patent applies locally-adapted EOS characterizations where the number of pseudo-components is optimized for each recovery mechanism region. This ensures sufficient accuracy to match laboratory data for each specific region while maintaining computational efficiency by not using excessive numbers of components across the entire reservoir.
Solution Approach 2:
The patent changes the parameters of pseudo-components (molecular weight, critical temperature, critical pressure, binary interaction coefficients) locally for each region to achieve accurate matching with laboratory data. This parameter optimization allows accurate fluid property representation with a reduced number of components, preserving computational efficiency.
4Adaptability or versatility
If different EOS characterizations are used for different recovery mechanisms, then the adaptability to varying reservoir conditions is improved, but the device complexity increases
Solution Approach 1:
The patent segments the reservoir into regions with different recovery mechanisms and assigns appropriate EOS characterizations to each segment. This segmentation approach improves adaptability to varying conditions while managing system complexity by creating a modular structure where each region can be independently characterized.
Solution Approach 2:
The patent implements a dynamic approach where the EOS characterization (number and properties of pseudo-components) is adapted based on the recovery mechanism in each region. This dynamic adaptation improves versatility while the systematic methodology keeps the implementation complexity manageable.
Data Source
AI summary
In some embodiments, a method for locally lumped equation of state fluid characterization can include determining a set of components for the material balance calculations for a plurality of grid blocks of a reservoir. The plurality of grid blocks can experience different recovery methods between them. Lumping schemes can be determined for the plurality of grid blocks. Phase behavior calculations can be performed on the plurality of grid blocks, wherein different lumping schemes can be used across the plurality of grid blocks.