Subsurface Porosity Distribution Visualization for NMR Logging
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Solution Overview
Problem
Existing NMR logging systems face difficulties in precisely determining porosity changes as a function of relaxation times due to low bin resolution and arbitrary color coding, making it challenging to read peak intensity and incremental porosity in relaxation time distributions, which complicates lithology identification and stratigraphy mapping across different wells.
Innovation Solution
The method involves generating a subsurface relaxation time distribution and porosity distribution using collected subsurface relaxation time data, sorting values into bins, and representing them using visual effects such as color gradients to clearly depict subsurface features, allowing for easier identification of porosity and correlations across multiple wells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NMR logging systems use traditional relaxation time distribution methods, then data collection is straightforward, but bin resolution is low and color coding is arbitrary, making it difficult to precisely determine porosity changes
Solution Approach 1:
The patent transforms the traditional one-dimensional relaxation time distribution into a two-dimensional pore size distribution by introducing pore size as an additional dimension. This allows porosity to be determined as a function of both relaxation time and pore size, significantly improving measurement precision while providing a more comprehensive view of subsurface properties
Solution Approach 2:
The patent changes the representation parameters from arbitrary color-coded bins to a systematic pore size-based classification system. By using pore size thresholds (e.g., 0.01 microns, 0.1 microns, 1.0 microns) to define distribution categories, the system eliminates arbitrariness and enables precise, reproducible porosity determination
2Ease of operation
If traditional relaxation time binning is used, then data processing is simple, but peak intensity and incremental porosity are difficult to read and interpret
Solution Approach 1:
The patent segments the pore size distribution into distinct categories based on pore size thresholds (e.g., small pores <0.01 microns, medium pores 0.01-0.1 microns, large pores >1.0 microns). This segmentation makes the data easily interpretable by clearly identifying peak intensities and incremental porosity changes across different pore size ranges
Solution Approach 2:
The patent uses systematic color coding to represent different pore size distribution categories, where colors correspond to specific pore size ranges and porosity values. This visual representation makes peak intensity and incremental porosity immediately readable and interpretable
3Reliability
If detailed pore size analysis is implemented, then lithology identification and stratigraphy mapping improve, but computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of pore sizes into standardized categories using fixed thresholds before detailed analysis. This preliminary action simplifies subsequent lithology identification and stratigraphy mapping by reducing the data to manageable, interpretable categories while maintaining reliability
Data Source
AI summary
Exemplary implementations may: obtain subsurface relaxation time data specifying subsurface relaxation time values corresponding to a well in the subsurface volume of interest; generating a subsurface relaxation time distribution using the subsurface relaxation time data; generating a subsurface porosity distribution using the subsurface relaxation time distribution; generating a representation of the subsurface porosity distribution in the subsurface volume of interest using visual effects to depict at least one of the one or more subsurface relaxation time values; and display the representation.