Reservoir Simulation Non-Physical Attribute Management
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Solution Overview
Problem
Reservoir simulations often produce non-physical attributes such as negative masses and saturations due to imperfect models and approximations, leading to sub-optimal production rates and increased computational intensity, which hinders efficient hydrocarbon production management.
Innovation Solution
The implementation of non-physical attribute management techniques that apply damp factors to maintain positive mobilities and volume balance, using Newton's method and fully-coupled equations to solve reservoir equations, thereby reducing the occurrence of non-physical attributes and expediting convergence to a more optimal production solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full physics numerical simulations are executed to model large reservoirs, then the accuracy and completeness of reservoir state prediction is improved, but the computational time and resources required increase significantly
Solution Approach 1:
The reservoir simulation is divided into multiple time steps, with adaptive sizing based on the magnitude of changes in key parameters. This segmentation allows the simulation to spend more computational effort on critical time periods while using smaller steps only when necessary, thereby improving accuracy where needed while reducing overall computational time.
Solution Approach 2:
The time step size is made dynamic rather than fixed, adjusting automatically based on the simulated state and rate of change of reservoir parameters. This dynamic adaptation enables the simulation to maintain accuracy during periods of rapid change while using larger steps during stable periods, resolving the contradiction between precision and computational efficiency.
2Measurement precision
If iterative numerical methods are used to solve reservoir equations, then the solution accuracy is improved, but the convergence speed decreases due to non-physical attributes causing repeated iterations
Solution Approach 1:
The method applies preliminary corrective actions during the iterative process by detecting non-physical attributes (negative saturations or masses) and applying damp factors to counteract their harmful effects before they can cause divergence or excessive iterations. This preliminary anti-action maintains solution accuracy while preventing convergence delays.
Solution Approach 2:
The method converts the harmful effect of non-physical attributes into a beneficial control mechanism. By detecting when non-physical attributes occur and applying targeted damp factors, the method uses these anomalies as feedback signals to adjust the solution path, ultimately improving both convergence speed and solution validity.
3Reliability
If damp factors are applied to reduce non-physical attributes, then the occurrence of negative masses and saturations is reduced, but the complexity of the solution algorithm increases
Solution Approach 1:
The algorithm performs self-diagnosis by automatically detecting non-physical attributes during the simulation process and self-corrects by applying appropriate damp factors. This self-service mechanism maintains physical validity of results without requiring external intervention or significantly increasing algorithmic complexity, as the corrections are integrated into the existing iterative solution framework.
Data Source
AI summary
A disclosed method for a hydrocarbon production system includes collecting production system data. The method also includes performing a simulation based on the collected data, a fluid model, and a fully-coupled set of equations. The method also includes expediting convergence of a solution for the simulation by reducing occurrences of non-physical attributes during the simulation. The method also includes storing control parameters determined for the solution for use with the production system.