Reservoir Structure Characterization With Facies-Constrained Inversion
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Solution Overview
Problem
Conventional seismic inversion methods are ill-posed and sensitive to parameterization, leading to inaccurate subsurface reservoir structure characterization, especially in regions with wide, continuous shale and sand layers, which affects decision-making in hydrocarbon exploration and production.
Innovation Solution
A facies-constrained inversion method that involves facies classification and stratigraphic sequencing of well logs, blocky well log generation, and global optimization to determine layer thickness, reducing unknowns and stabilizing the inversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic inversion methods are used, then the inversion process can be performed, but the results are inaccurate and sensitive to parameterization due to the ill-posed nature of the problem
Solution Approach 1:
The patent segments the continuous subsurface model into discrete lithological layers with distinct properties. By dividing the inversion problem into discrete layers with facies-specific parameter ranges, the method transforms the ill-posed continuous inversion into a more stable discrete problem, reducing sensitivity to parameterization while maintaining accuracy in characterizing subsurface reservoir structures.
Solution Approach 2:
The patent changes the parameterization approach by using facies-based discrete parameters instead of continuous physical properties. By constraining parameters within facies-specific ranges and using categorical facies identifiers, the method stabilizes the inversion process and reduces sensitivity to initial parameter choices while improving the reliability of structural characterization.
2Manufacturing precision
If detailed seismic inversion is performed to characterize thin layers, then more structural detail is obtained, but the computational time and complexity increase significantly
Solution Approach 1:
The patent segments the inversion problem by focusing on discrete lithological layers rather than attempting to resolve all continuous subsurface variations. This segmentation approach provides clear structural detail for reservoir characterization while simplifying the algorithm by working with distinct layers and facies categories, reducing overall computational complexity.
Solution Approach 2:
The patent extracts and focuses on the most critical structural elements (lithological layer boundaries and facies distributions) rather than attempting to characterize all subsurface details. By taking out the essential structural information needed for reservoir evaluation, the method achieves sufficient detail for decision-making while avoiding the computational burden of full high-resolution inversion.
3Productivity
If conventional inversion algorithms are used, then the inversion can be completed, but the results change significantly with different parameterizations and input models
Solution Approach 1:
The patent fundamentally changes the parameterization scheme by using facies-based discrete parameters with constrained property ranges instead of unconstrained continuous parameters. This parameter transformation stabilizes the inversion results against variations in input models and initial conditions, improving reliability while maintaining computational efficiency through the simplified facies framework.
Solution Approach 2:
The patent incorporates feedback mechanisms where facies classifications from well logs guide the inversion process, and inversion results are used to refine facies distributions. This iterative feedback loop ensures result consistency by continuously constraining the solution within geologically reasonable facies boundaries, reducing sensitivity to different parameterizations.
Data Source
AI summary
A method is described for reservoir structure characterization including obtaining well logs and seismic data; performing facies classification and stratigraphic sequencing on the well logs to identify a plurality of layers; estimating wavelets from the seismic data and using the wavelets to tie synthetic seismograms from well logs to the seismic data; determining a mean value for each elastic property in each layer of the well logs and assigning the mean value to each layer to generate blocky well logs; using the wavelets to attempt to tie synthetic seismograms from the blocky well logs to the seismic data; defining facies-dependent properties based on the blocky well logs; performing global optimization using the facies-dependent properties and the seismic data to find thicknesses of the layers across the volume of interest; and mapping the reservoir structure based on the global optimization to generate a graphical representation of the reservoir structure.