Reservoir Fluid Modeling with Automated EoS Initialization
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Solution Overview
Problem
Current reservoir simulation methods face challenges in accurately determining initial conditions for fluid distribution, leading to convergence issues and inefficiencies in simulating hydrocarbon reservoirs, especially when fluid communication exists between reservoirs and surface networks.
Innovation Solution
A method and system that automatically select equations of state from a plurality of options based on sample information, generate initial conditions, and simulate physical phenomena to improve the accuracy and efficiency of reservoir modeling, reducing the number of iterations required for convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual process is used to set initial conditions for reservoir simulation, then flexibility in adjusting parameters is maintained, but the process becomes painstaking and time-consuming
Solution Approach 1:
The system performs self-service by automatically selecting equations of state and generating initial conditions without requiring manual intervention. The automated EoS selection process evaluates multiple equations and selects the most appropriate ones based on the reservoir data, while the initial condition generation automatically populates simulation parameters, eliminating the painstaking manual process described in the background.
2Productivity
If automated EoS selection is implemented, then the process becomes efficient and fast, but complexity of the system increases
Solution Approach 1:
The patent introduces an automated EoS selection module as an intermediary between the reservoir data and the simulation process. This intermediary automatically evaluates multiple equations of state, selects the most appropriate ones based on data characteristics, and generates initial conditions, thereby managing the complexity internally while presenting a simplified interface to users and achieving high productivity.
3Reliability
If accurate initial conditions are used, then simulation convergence is improved, but the process of determining them becomes more complex
Solution Approach 1:
The system automatically adjusts and optimizes simulation parameters by selecting the most appropriate equations of state based on the reservoir data characteristics. This parameter optimization ensures accurate initial conditions that improve simulation convergence, while the automation of this process manages the complexity that would otherwise be involved in manually determining these parameters.
4Measurement precision
If multiple equations of state are evaluated, then accuracy of fluid characterization is improved, but computational time increases
Solution Approach 1:
The automated EoS selection process evaluates multiple equations of state (excessive action) to ensure accurate fluid characterization, but the system is designed to efficiently process this evaluation and select only the necessary equations for use in simulation. This approach maintains high measurement precision while managing computational time through optimized evaluation and selection algorithms.
Data Source
AI summary
A method can include receiving sample information for reservoir fluid samples and automatically selecting one or more equations of state from a plurality of different equations of state, which can suitably match the reservoir fluid samples and/or other samples. Such a method can also include automatically generating initial conditions based at least in part on sample information where such initial conditions along with one or more selected equations of state can be utilized in simulating physical phenomena using at least a reservoir model to generate simulation results. Such a method can include outputting at least a portion of the simulation results, which may be utilized in one or more processes.