Subsurface Modeling Layer-Space Conversion
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Solution Overview
Problem
Existing subsurface modeling technologies face challenges in accurately modeling small-scale continuous layers, such as thin shale and mud deposits, which are crucial for capturing reservoir heterogeneity, as they are often overlooked due to their spatial continuity and volumetric insignificance, leading to incomplete representations that affect flow pathways and production efficiency in oil and gas reservoirs.
Innovation Solution
The method involves converting subsurface data from physical space to layer space, where subsurface configurations and conditioning characteristics are defined as functions of layers and lateral spatial location, enabling modeling within the layer space to generate representations that include small-scale continuous layers while honoring conditioning characteristics, and then converting back to physical space for realistic simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Cartesian grids are used for subsurface simulations, then the simulations can be computationally efficient and straightforward to implement, but small-scale continuous layers cannot be modeled accurately
Solution Approach 1:
The patent transforms the traditional 3D Cartesian grid into a 4D layer-space grid by adding a temporal dimension. This allows continuous layers to be represented as persistent entities across time steps, enabling accurate modeling of small-scale continuous layers while maintaining computational efficiency through the structured grid approach.
Solution Approach 2:
The patent segments the subsurface model into discrete continuous layers that can be tracked independently through time. By identifying and separating continuous layers from the bulk rock matrix, the model can accurately represent small-scale continuous features while maintaining overall computational efficiency.
2Reliability
If process-based approaches are used to honor conditioning subsurface characteristics, then the geological realism is improved, but the difficulty of honoring conditioning characteristics increases
Solution Approach 1:
The patent performs preliminary identification and characterization of continuous layers before the main simulation process. By pre-defining layer boundaries, properties, and continuity constraints, the subsequent simulation can honor conditioning characteristics more easily while maintaining geological realism.
Solution Approach 2:
The patent introduces continuous layers as intermediary entities that mediate between the conditioning subsurface characteristics and the simulation process. These layers serve as constraints that guide the simulation to produce geologically realistic results while honoring observed characteristics.
3Loss of information
If thin shale and mud deposits are included in the model, then the representation of reservoir heterogeneity is improved, but the computational complexity increases due to their spatial continuity
Solution Approach 1:
By adding the temporal dimension to create layer-space, the patent can represent continuous thin layers as persistent features without requiring fine spatial discretization in all directions. This maintains computational efficiency while accurately representing reservoir heterogeneity.
Solution Approach 2:
The patent applies different modeling resolutions to different parts of the subsurface model. Continuous thin layers are modeled with high fidelity where they affect flow, while surrounding rock matrix uses coarser discretization, maintaining computational efficiency while capturing essential heterogeneity.
Data Source
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AI summary
Data in physical space may be converted to layer space before performing modeling to generate one or more subsurface representations. Computational stratigraphy model representations that define subsurface configurations as a function of depth in the physical space may be converted to the layer space so that the subsurface configurations are defined as a function of layers. Conditioning information that defines conditioning characteristics as the function of depth in the physical space may be converted to the layer space so that the conditioning characteristics are defined as the function of layers. Modeling may be performed in the layer space to generate subsurface representations within layer space, and the subsurface representations may be converted into the physical space.