3D Pore Surface Roughness Quantification for Corrected NMR Logging
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Solution Overview
Problem
NMR logging underestimates pore sizes in reservoir rocks due to the irregular surface roughness of the solid-pore interface, leading to inaccurate T2 time measurements without accounting for surface roughness effects.
Innovation Solution
A method and system for quantifying three-dimensional pore surface roughness using micro-computed tomography and image analysis to generate a meshed surface, construct reference and constructed surfaces, evaluate surface distances, and determine a roughness coefficient, which is used to correct T2 times and calculate accurate average pore sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NMR logging assumes simple pore geometry with smooth surface, then the measurement process is simple and fast, but the pore size measurement precision deteriorates due to unaccounted surface roughness effects
Solution Approach 1:
The patent applies preliminary action by pre-determining the surface roughness coefficient through micro-CT scanning and image analysis before performing NMR log interpretation. This pre-characterization of pore surface roughness allows the NMR measurements to be corrected using the predetermined coefficient, thereby improving measurement precision without adding complexity to the actual NMR logging process.
Solution Approach 2:
The patent introduces an intermediary approach by using micro-CT scanning and image analysis as a bridge between the physical pore structure and NMR measurements. The surface roughness coefficient derived from this intermediary process serves as a correction factor that reconciles the simplified NMR model with the complex actual pore geometry, improving accuracy without direct modification of the NMR tool.
2Measurement precision
If surface roughness is accounted for in NMR interpretation, then pore size measurement precision improves, but the complexity of interpretation increases
Solution Approach 1:
The patent applies parameter changes by introducing a surface roughness coefficient as a new parameter in the NMR interpretation model. This coefficient quantifies the deviation from smooth surface assumptions and enables correction of T2 relaxation times. The method transforms the complex geometric problem into a manageable parameter adjustment, improving precision while controlling interpretation complexity through a single corrective parameter.
3Measurement precision
If micro-CT scanning and image analysis are used to determine surface roughness, then measurement precision improves, but the time and resources required increase
Solution Approach 1:
The patent applies preliminary action by performing micro-CT scanning and image analysis once to establish the surface roughness coefficient before NMR measurements. This pre-characterization approach allows the time-consuming detailed imaging to be done only once, while subsequent NMR interpretations benefit from the pre-determined coefficient, significantly reducing the time loss for routine measurements.
Solution Approach 2:
The patent uses copying by creating a digital 3D model of the pore space from micro-CT data and generating a meshed surface representation. This digital copy allows for repeated analysis and extraction of the surface roughness coefficient without requiring additional physical scanning, reducing time and resource requirements for subsequent measurements.
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
Accurately estimates pore sizes by correcting T2 times, enhancing the reliability of petrophysical property characterization, including porosity, pore-size distribution, and permeability in reservoir rocks.
Implementation Method 1
obtaining, using a micro-computed tomography system, a 3D image of a pore space
Implementation Method 2
Nuclear Magnetic Resonance (NMR) is a logging tool for measuring the petrophysical properties of reservoir rocks
Data Source
AI summary
Methods and systems for a novel three-dimensional (“3D”) pore roughness quantification are disclosed. The methods include obtaining an image of a pore space. The methods further include, using an image analysis system: discretizing the 3D image to generate a meshed surface; constructing a reference surface and a constructed surface from the meshed surface; evaluating a plurality of surface distances; and determining a roughness coefficient. The methods further include obtaining an observed T2 time for at least one sample depth in a well; determining a corrected T2 time from the observed T2 time; and determining an average pore size in a rock formation.