3D Facies Model Generator Using Depositional Feedback
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Solution Overview
Problem
Current methods for generating three-dimensional facies models are inadequate in accurately representing the stratigraphic sequence and depositional processes of reservoirs, leading to incorrect facies assignments and suboptimal model accuracy.
Innovation Solution
A three-dimensional facies model generator system that includes a model generator component and a facies data repository, which processes facies data and sequence stratigraphy to create a 3D stratigraphic sequence framework, calculates facies volume fractions, and compares these with depositional models to correct assignments, generating 3D probability trends for accurate facies modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for generating three-dimensional facies models are used, then the modeling process can be completed, but the accuracy of facies assignments and representation of stratigraphic sequences is insufficient
Solution Approach 1:
The system compares generated facies models with observed stratigraphic data and depositional models, then iteratively adjusts facies assignments and volume fractions to improve accuracy. This feedback loop ensures that the 3D facies model accurately represents both the stratigraphic sequence and depositional processes by continuously refining the model based on comparison with known geological constraints
Solution Approach 2:
The system performs preliminary facies assignments based on depositional models and sequence stratigraphy before final model generation. By pre-establishing facies frameworks based on geological principles and comparing them against observed data, the system ensures accurate facies assignments are made before the actual 3D modeling process, improving overall model reliability
2Productivity
If simple facies modeling approaches are used, then the modeling process is faster and simpler, but the representation of depositional processes and stratigraphic details is inadequate
Solution Approach 1:
The system segments the facies modeling process into distinct components: sequence stratigraphy analysis, depositional model generation, facies volume fraction calculation, and 3D model construction. This segmentation allows each component to be processed efficiently while maintaining high precision in representing depositional processes, as each segment can be optimized independently
Solution Approach 2:
The system uses parameter-based facies classification and volume fraction calculations that can be adjusted based on depositional models. By changing key parameters such as facies boundaries, volume fractions, and spatial distributions based on depositional processes, the system achieves both rapid modeling and accurate representation of geological features
3Ease of operation
If facies volume fractions are assigned without comparison to depositional models, then the modeling process is more straightforward, but incorrect facies assignments occur
Solution Approach 1:
The system automatically compares calculated facies volume fractions with depositional model predictions and adjusts assignments accordingly. This feedback mechanism maintains modeling simplicity by automating the comparison and adjustment process, while ensuring facies assignment correctness through continuous validation against geological constraints
Solution Approach 2:
The system performs self-validation by comparing its own facies assignments with depositional models and automatically correcting incorrect assignments. This self-service approach maintains ease of operation as the system handles the complex comparison and correction processes automatically without requiring manual intervention, while ensuring high precision in facies assignments
Data Source
AI summary
A 3D facies model generator system and methods thereof include a processor and repository comprising (i) facies data, (ii) sequence stratigraphy, and (iii) a depositional model for a subject reservoir. The processor is operable to generate a 3D stratigraphic sequence framework comprising a plurality of facies based on received facies data and sequence stratigraphy, generate a facies volume fraction for each facies, compare each facies volume fraction with the depositional model for the subject reservoir, reassign the facies volume fraction for the facies of the plurality of facies to another facies of the plurality of facies when the comparison determines that the facies of the 3D stratigraphic sequence framework is incorrectly assigned, generate a plurality of 3D probability trends for the plurality of facies based on the facies volume fraction for the respective facies, and generate the 3D facies model based on the 3D probability trends.