Upscaled Subsurface Permeability Modeling With Synthetic DFNs
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Solution Overview
Problem
Conventional methods for upscaling fracture properties from core-scale to field-scale models in Discrete Fracture Network (DFN) modeling are computationally intensive and often rely on simplifying assumptions, failing to accurately capture the complex spatial correlations and heterogeneity of fracture attributes.
Innovation Solution
A method involving the use of field-derived DFN data and a synthetic driver-based DFN, combined with deep-learning models, to generate a single-upscaled permeability model by upscaling fracture characterization data and determining fracture permeability for each cell in a subsurface model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional upscaling techniques are used to scale fracture properties from core-scale to field-scale, then the modeling process becomes computationally intensive and time-consuming, but the accuracy of fracture network representation is insufficient due to simplifying assumptions and empirical relationships
Solution Approach 1:
The patent creates synthetic driver-based DFNs that copy the essential statistical and spatial characteristics of field-derived DFN data. By generating synthetic fracture networks that replicate key properties (aperture, length, orientation, connectivity) without requiring full-scale field data, the method achieves accurate fracture network representation while reducing computational burden compared to using actual field data at field scale.
Solution Approach 2:
The patent transforms fracture properties by upscaling them from core-scale measurements to field-scale representations using the synthetic driver-based DFN framework. This involves changing the spatial scale and statistical parameters of fracture attributes while maintaining their essential characteristics, enabling accurate field-scale modeling without the computational intensity of direct field data processing.
2Reliability
If detailed field-derived DFN data is used to capture complex spatial correlations and heterogeneity of fracture attributes, then the model accuracy improves, but the computational intensity and time required for upscaling increases significantly
Solution Approach 1:
The synthetic driver-based DFN copies the statistical distributions and spatial correlation structures of field-derived DFN data at a reduced complexity level. This allows the model to capture essential fracture network behavior and generate accurate permeability models without processing the full complexity of detailed field data, thereby reducing upscaling time while maintaining reliability.
Solution Approach 2:
The patent extracts only the essential statistical and spatial characteristics from detailed field-derived DFN data to create the synthetic driver-based model. By taking out only the critical parameters needed for permeability prediction (such as fracture density, aperture distributions, and spatial correlation lengths) rather than using complete detailed data, the method reduces upscaling time while preserving model accuracy.
3Productivity
If conventional upscaling methods with simplifying assumptions are used, then the computational process is faster and less intensive, but the fracture attributes and their spatial correlations are not adequately captured
Solution Approach 1:
The synthetic driver-based DFN copies the complex spatial correlations and heterogeneity patterns of fracture attributes from field data, enabling accurate fracture attribute characterization. This copying approach maintains high measurement precision for fracture properties while using computationally efficient synthetic data rather than processing complete detailed field data, thus achieving both speed and accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming fracture attributes from core-scale to field-scale through the synthetic driver-based framework. This involves scaling parameters such as fracture aperture, length, and spatial correlation lengths in a physically consistent manner, enabling accurate fracture attribute characterization at field scale without the computational burden of conventional detailed upscaling methods.
Data Source
AI summary
A method for generating a single-upscaled permeability model for a subsurface is disclosed. The method includes receiving input data including field-derived discrete fracture network (DFN) data and a subsurface model. The method also includes generating a synthetic driver-based DFN based upon the field-derived DFN data. The method further includes generating the single-upscaled permeability model using the subsurface model and the synthetic driver-based DFN.