Adaptive Multiscale Reservoir Simulation for Complex Geology
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Solution Overview
Problem
Existing reservoir simulation techniques are inefficient and inaccurate when dealing with complex features such as high-contrast fluvial sands, fractured carbonate reservoirs, fractures, faults, and wells with varying well controls, which require significant time and processing power to simulate accurately.
Innovation Solution
An adaptive, multi-fidelity, multiscale method that combines multiple multiscale approximations with different resolutions to target specific computational challenges, using a system of nonlinear equations and operators to efficiently model fluid behavior in complex reservoirs, allowing for parallel processing and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reservoir simulation techniques are used to accurately model complex reservoir features (high-contrast fluvial sands, fractured carbonate reservoirs, faults, wells with varying controls), then simulation accuracy is improved, but simulation time and computational cost increase significantly
Solution Approach 1:
The reservoir is divided into multiple coarse grids, each targeting specific computational challenges or complex features. This segmentation allows different regions to be modeled at appropriate scales, improving accuracy where needed while maintaining efficiency in simpler regions.
Solution Approach 2:
The patent introduces a multi-scale dimension by combining multiple coarse grids with different resolutions. This allows the simulation to operate efficiently at coarse scales while capturing fine-scale details only where necessary, resolving the time-accuracy tradeoff.
2Measurement precision
If traditional reservoir simulation techniques are used to accurately model complex reservoir features, then simulation accuracy is improved, but processing power requirements increase significantly
Solution Approach 1:
By segmenting the reservoir into multiple coarse grids that target specific challenges, the computational workload is distributed and optimized. Each coarse grid can be processed independently, reducing overall processing power requirements while maintaining accuracy for complex features.
Solution Approach 2:
Different coarse grids are applied locally to different regions of the reservoir based on their specific characteristics. This allows high processing power to be applied only where complex features require it, rather than uniformly across the entire reservoir.
3Loss of time
If multi-scale method is used to speed up reservoir simulations, then simulation time is reduced, but the ability to individually consider complex features in reservoir geology, fluid behavior or driving forces is lost
Solution Approach 1:
The multi-scale method is enhanced by segmenting the reservoir into multiple coarse grids, each capable of individually targeting specific complex features. This restores the ability to consider complex geology, fluid behavior, and driving forces while maintaining computational speed-up.
Solution Approach 2:
The system creates a universal framework that can handle multiple types of complex features (geology, fluid behavior, driving forces) through a unified multi-coarse-grid approach, making the method versatile across different reservoir scenarios.
Data Source
AI summary
Computer-implemented systems and methods for modeling behavior of at least one fluid in a reservoir are disclosed. The techniques can include obtaining measurements of physical parameters, including pressure, at locations within the reservoir, and discretizing, based on a three-dimensional fine grid, a system of partial differential mass balance equations that model, based on the measurements, at least the physical parameters at the locations within the reservoir, such that a system of nonlinear equations is produced. The techniques can include iterating from a current time step to a next time step, such that a solution to the system of nonlinear equations for a time interval that includes the current time step and the next time step is produced. The iterating can include an adaptive multi-fidelity multiscale technique that employs multiple restriction operators, prolongation operators, and coarse grids, to model various computationally challenging reservoir features, behaviors, or both.