NMR Relaxation Distribution Pore Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current NMR logging methods struggle to accurately classify subterranean formations into micro-, meso-, and macro-pore size groups due to fuzzy boundaries between these categories, which complicates the derivation of porosity information from NMR relaxation distributions.
Innovation Solution
The method involves fitting multiple Gaussian functions to NMR relaxation distributions to establish fitting parameters, allowing for the characterization of porosity based on a relationship between these parameters and pore size distributions, using a computing system to analyze data from NMR logging tools during wireline or drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NMR logging methods are used to classify subterranean formations, then porosity information can be obtained, but the classification accuracy deteriorates due to fuzzy boundaries between micro-, meso-, and macro-pore size groups
Solution Approach 1:
The patent applies segmentation by dividing the continuous NMR relaxation distribution into distinct pore size groups (micro-, meso-, and macro-pores) using multiple Gaussian functions. Each Gaussian function represents a specific pore size group, allowing the continuous distribution to be segmented into discrete, interpretable categories despite the inherent fuzziness of boundary definitions.
Solution Approach 2:
The patent employs dynamic parameter fitting where multiple Gaussian functions are adjusted to match the NMR relaxation distribution. The fitting parameters (amplitude, mean, standard deviation) of each Gaussian function are dynamically optimized to represent different pore size groups, enabling adaptive classification that accounts for the fuzzy boundaries between pore categories.
2Measurement precision
If multiple Gaussian functions are fitted to NMR relaxation distributions, then porosity classification precision is improved, but computational complexity increases
Solution Approach 1:
The patent uses Gaussian functions as simplified mathematical models (copies) to represent the complex NMR relaxation distribution. Instead of directly analyzing the raw NMR data, the system creates a simplified representation using multiple Gaussian curves that can be easily fitted and interpreted, reducing computational complexity while maintaining classification accuracy.
Solution Approach 2:
The patent transforms the complex NMR relaxation distribution analysis into a parameter fitting problem. By changing the approach from direct distribution analysis to fitting Gaussian functions with specific parameters (amplitude, mean, standard deviation), the system simplifies the computational task while preserving the ability to accurately classify porosity into different pore size groups.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise classification of porosity facies by replicating NMR relaxation distributions, providing accurate porosity estimates and categorization of subterranean formations, even with fuzzy boundaries between pore size groups, thereby enhancing the understanding of subterranean formations.
Implementation Method 1
Nuclear magnetic resonance (NMR) logging is a type of logging that uses the NMR response of a formation to directly determine its porosity and permeability
Implementation Method 2
NMR logging exploits the large magnetic moment of hydrogen, which is abundant in rocks in the form of water. The NMR signal amplitude is proportional to the quantity of hydrogen nuclei present in a formation
Implementation Method 3
Moreover, the rate of decay of a NMR signal can be used to obtain information about the permeability of the formation
Data Source
AI summary
Porosity of a subterranean region is estimated by accessing a nuclear magnetic resonance (NMR) relaxation distribution corresponding to NMR measurements of a subterranean region in which the NMR relaxation distribution includes multiple of peaks, fitting Gaussian functions to the NMR relaxation distribution to establish values for fitting parameters for each of the Gaussian functions, determining the porosity of the subterranean region based on the values of the fitting parameters of the Gaussian functions, and categorizing the porosity based on a relationship between the fitting parameters of the Gaussian functions and a distribution of pore sizes.