Downhole Formation Boundary Estimation via Clustering
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Solution Overview
Problem
Inversion models of downhole resistivity contain uncertainty, making it challenging to accurately determine the location of downhole formation boundaries and hydrocarbon reservoirs.
Innovation Solution
A downhole formation boundary estimation system that analyzes multiple inversion models, defines boundaries based on resistivity values, organizes boundary points into clusters, determines uncertainties, and estimates boundary locations to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inversion models are used to determine downhole formation boundaries, then boundary location information can be obtained, but the uncertainty in resistivity values causes inaccurate boundary detection
Solution Approach 1:
The patent segments the continuous boundary detection problem into discrete boundary point identification. By identifying specific boundary points where resistivity changes occur and organizing them into clusters, the system transforms the uncertain continuous boundary into discrete, manageable segments that can be analyzed individually, reducing the impact of overall resistivity uncertainty on boundary location accuracy.
Solution Approach 2:
The patent applies partial action by focusing computational efforts only on identifying and clustering boundary points rather than analyzing the entire formation model. By concentrating resources on the critical boundary regions where resistivity changes occur, the system achieves accurate boundary detection without the computational overhead of processing all formation data, thereby improving precision while managing uncertainty.
2Measurement precision
If multiple inversion models are analyzed to improve boundary detection accuracy, then measurement precision increases, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential boundary-related information from multiple inversion models by identifying boundary points where resistivity changes occur. Instead of processing and analyzing all data from multiple complex inversion models, the system extracts and clusters only the relevant boundary points, significantly reducing computational complexity while maintaining improved boundary detection accuracy through multi-model analysis.
3Measurement precision
If boundary points are organized into clusters to reduce uncertainty, then boundary estimation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing clustering only for identified boundary points rather than clustering all formation data points. This selective clustering approach focuses computational resources on the critical boundary regions where accuracy is most needed, reducing overall processing time while still achieving improved boundary estimation accuracy through uncertainty reduction in the clustered boundary points.
Data Source
AI summary
A computer-implemented method to estimate a boundary of a downhole formation data includes obtaining an inversion model of a downhole formation. The method also includes defining a boundary of the downhole formation. The method further includes determining the boundary based on values associated with the inversion model. The method further includes organizing the boundary into one or more clusters. The method further includes determining uncertainties associated with the one or more clusters. The method further includes estimating the boundary based on the one or more clusters and the uncertainties