Stoneley Dispersion Modeling for Reservoir Characterization
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Solution Overview
Problem
Existing methods for characterizing Stoneley wave dispersion in hydrocarbon reservoirs are computationally expensive and inefficient, making it difficult to accurately model and analyze rock properties such as porosity and fracture presence along well-rock interfaces, which are crucial for hydrocarbon recovery and well planning.
Innovation Solution
A method involving the determination of dispersion curves using a combination of theoretical and empirical models, where a first subset of depth windows is modeled directly and a second subset is initialized using a nearest neighbor search, with slowness-frequency pairs determined and refined using a recursive scanning method to characterize rock properties near the well.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dispersion modeling methods are used for all depth windows, then measurement precision of rock properties is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The well depth range is divided into multiple depth windows, with a first subset modeled using computationally intensive dispersion modeling and a second subset initialized using efficient nearest neighbor search. This segmentation allows different processing strategies to be applied to different portions of the data, balancing accuracy and computational efficiency.
Solution Approach 2:
Dispersion curves for the first subset of depth windows are computed in advance using full dispersion modeling, and these results are stored and used as reference data for initializing the second subset through nearest neighbor search. This preliminary computation avoids repeating expensive calculations for overlapping or adjacent depth windows.
2Measurement precision
If traditional dispersion modeling is applied to all depth windows, then rock property characterization accuracy is improved, but device complexity and computational resources required increase
Solution Approach 1:
A nearest neighbor search algorithm serves as an intermediary step between full dispersion modeling and final rock property characterization. Instead of applying full dispersion modeling to all depth windows, the system uses nearest neighbor search to initialize dispersion curves for the second subset, reducing computational complexity while maintaining acceptable accuracy through subsequent refinement.
3Reliability
If full dispersion modeling is performed for every depth window, then reliability of fracture detection is improved, but productivity of hydrocarbon reservoir analysis decreases
Solution Approach 1:
Full dispersion modeling is applied only to a first subset of depth windows rather than all depth windows. The second subset uses nearest neighbor search for initialization followed by recursive scanning for refinement, providing partial action that maintains sufficient reliability for fracture detection while significantly improving analysis productivity across the entire well depth range.
Data Source
AI summary
Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.