Unstructured Grid Reservoir Simulation Automation
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Solution Overview
Problem
Current reservoir simulation models, especially structured grid models, face challenges in accurately modeling complex geological features and irregular geometries, leading to inconsistencies and increased computational costs due to the need for user manual interaction and high expertise in defining gridding parameters for unstructured grid models.
Innovation Solution
A method using machine learning to determine and allocate well data points with a convex hull for generating unstructured grid models, automating the process of forming reservoir regions and adjusting grid spacing based on well trajectory and completion data, thereby reducing user interaction and improving modeling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If structured grid models are used for reservoir simulation, then ease of cell block referencing and mature software availability are improved, but the ability to model complicated geological features and irregular geometries deteriorates
Solution Approach 1:
The patent segments the reservoir model into structured and unstructured grid regions, allowing different grid types to coexist. The structured grid handles regular areas while unstructured grids model complex geological features, thus maintaining ease of operation for standard regions while improving adaptability for complicated areas.
Solution Approach 2:
The patent introduces unstructured grid technology as an additional dimensional approach to reservoir modeling. By adding this new modeling dimension alongside traditional structured grids, the system can handle both simple and complex geological features without sacrificing the advantages of either approach.
2Adaptability or versatility
If unstructured grid modeling is used to model complicated geological features, then adaptability to irregular geometry is improved, but device complexity and lack of mature workflow increase
Solution Approach 1:
The patent merges structured and unstructured grid workflows into a unified system. By combining the mature, simple structured grid approach with the flexible unstructured grid capability, the system achieves adaptability for irregular geometries while reducing overall workflow complexity through integration and automated transition procedures.
Solution Approach 2:
The patent introduces an intermediary automated workflow system that manages the complexity of unstructured grid modeling. This intermediary layer handles grid generation, refinement, and coarsening automatically, reducing the burden on users and simplifying the overall process while maintaining the adaptability benefits of unstructured grids.
3Adaptability or versatility
If user manual interaction is required for defining input criteria for unstructured grid, then flexibility in defining regions of interest is improved, but consistency and reliability of simulation workflow deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically analyzes reservoir data, identifies regions of interest, and adjusts grid parameters based on simulation results. This feedback loop maintains flexibility in defining regions while improving consistency through automated, data-driven decisions that reduce human error and variability.
Solution Approach 2:
The patent enables the simulation system to automatically define regions of interest and generate appropriate unstructured grids without requiring manual user input. The system self-adjusts grid refinement and coarsening based on geological features and simulation requirements, maintaining flexibility through automated intelligence while ensuring consistent, reliable workflows.
4Manufacturing precision
If grid refinement is applied near wells to maintain high resolution, then manufacturing precision of well trajectory modeling is improved, but computational cost and processing time increase
Solution Approach 1:
The patent applies local quality by using grid refinement selectively only in regions where high precision is needed (near wells and complex geological features) while using coarser grids in simpler areas. This localized approach maintains manufacturing precision for critical regions while significantly reducing overall computational cost and processing time compared to uniform fine gridding.
Solution Approach 2:
The patent applies partial action by implementing grid refinement only to the extent necessary for accurate well trajectory modeling rather than applying it uniformly across the entire reservoir. This partial refinement strategy achieves the required precision for well regions while avoiding the excessive computational burden of full-domain fine gridding.
5Productivity
If field-scale unstructured grid simulation is implemented, then productivity and coverage of reservoir modeling is improved, but device complexity and computational resources required increase
Solution Approach 1:
The patent segments the field-scale reservoir into manageable structured and unstructured grid regions, allowing complex unstructured modeling to be applied only where necessary while using simpler structured grids elsewhere. This segmentation enables field-scale coverage with improved productivity while controlling device complexity by limiting unstructured grid usage to essential areas.
Solution Approach 2:
The patent applies unstructured grid technology partially, only in regions where it provides necessary value for field-scale modeling. By using unstructured grids selectively rather than universally across the entire field, the system achieves improved productivity and coverage while managing computational resources and device complexity effectively.
Data Source
AI summary
Reservoir management based on unstructured grid reservoir simulation is improved with machine learning based intelligent automation. Reservoir heterogeneity, geological internal boundary features and well geometry complexity are taken into account to automatically detect well zone and focusing reservoir area by calculating the region-of-interests in the model and defining cell spacing for grid coarsening and refinement in the reservoir. Data points for wells in the reservoir are grouped into reservoir regions according to datasets organized as a convex hull, which is a minimum convex set in spatial geometry which encloses the totality of such data points. The reservoir regions form a basis for grid spacing utilized in the reservoir model.