NMR Inversion Matrix Reduction for Downhole Evaluation
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Solution Overview
Problem
NMR logging tools face significant memory management and computational efficiency challenges due to large inversion matrices, which can lead to poor system performance or failure during real-time and post-data-acquisition processing, especially when dealing with 2D and 3D inversions of T1, T2, and diffusivity distributions.
Innovation Solution
The method employs orthogonal-triangular decomposition to approximate the least squares solution of the NMR inversion process, reducing memory requirements by projecting NMR data into a reduced row vector space and using sparse regularization matrices, allowing for efficient processing of NMR data without significant distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NMR inversion algorithms are used to process large datasets with multiple echo trains, then fluid typing and quantification accuracy is improved, but memory requirements and computational complexity increase significantly causing system performance degradation or failure
Solution Approach 1:
The patent segments the large inversion matrix into smaller sub-matrices by dividing the NMR echo train data into multiple segments. Each segment is processed independently with its own reduced inversion matrix, avoiding the need to handle the full large-scale inversion matrix in memory at once. This segmentation maintains measurement precision while reducing memory requirements and computational complexity.
Solution Approach 2:
The patent transforms the traditional 1D inversion approach into a multi-dimensional processing framework by organizing echo train data across multiple segments and dimensions. This dimensional reorganization allows the system to process large datasets through multiple smaller inversion operations rather than a single large inversion, reducing memory footprint while preserving fluid typing accuracy.
2Measurement precision
If complete NMR echo train data is processed to ensure accurate formation evaluation, then measurement precision is improved, but processing time and computational resources increase causing delays in real-time operations
Solution Approach 1:
The patent divides the complete NMR echo train into multiple segments that can be processed in parallel or sequentially with reduced computational overhead. Each segment undergoes inversion with a smaller matrix, significantly reducing processing time compared to processing the entire dataset as a single large matrix, while still maintaining accurate formation evaluation through comprehensive coverage of all echo data.
Solution Approach 2:
The patent performs preliminary processing steps on the NMR echo train data before the main inversion operation, including data organization, segmentation, and pre-computation of certain parameters. This preliminary action prepares the data in an optimized format that accelerates the subsequent inversion process, reducing overall processing time while preserving measurement precision.
3Measurement precision
If high-resolution NMR data with multiple acquisition parameters is collected to capture fluid contrast, then measurement precision is improved, but the size of inversion matrices becomes unmanageable leading to system failure
Solution Approach 1:
The patent segments the high-resolution NMR data collected with multiple acquisition parameters into manageable subsets. Each subset is processed with a corresponding reduced inversion matrix, allowing the system to handle large volumes of high-quality data without creating unmanageably large inversion matrices. This segmentation preserves fluid contrast detection capability while making the data volume computationally tractable.
Solution Approach 2:
The patent applies partial processing to the NMR echo trains by selecting and processing representative segments or subsets of the complete data set in certain operational modes. This partial action approach maintains adequate fluid contrast detection precision while significantly reducing the data volume that requires full inversion processing, preventing system overload.
Data Source
AI summary
A formation evaluation system reduces inversion matrixes used to determine formation properties, thereby increasing the memory management and processing efficiency of the evaluation system. NMR data is acquired from a wellbore and expressed mathematically by the system as a least squares solution to a linear system. The least squares solution is approximated using a numerical decomposition method and the evaluation system determines a formation property using the approximated least squares solution. Thereafter, a downhole operation may be planned, analyzed or conducted using the determined formation property.