NMR Data Denoising With Noise-Harmonic Frequency Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
NMR responses in reservoir characterization for oil and gas extraction are often noisy, leading to inaccurate determination of reservoir characteristics such as porosity and saturation due to environmental and tool-generated noise, which affects the reliability of NMR data.
Innovation Solution
A method and system for denoising NMR data by identifying noise harmonics and applying a filter based on these harmonics, using a moving average filter in the frequency domain to reduce noise while preserving the NMR signal, allowing for accurate determination of spin relaxation times and reservoir properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NMR measurements are used to determine reservoir characteristics, then reservoir characterization can be performed, but the NMR data becomes noisy leading to inaccurate determination of reservoir properties
Solution Approach 1:
The patent extracts and removes noise harmonics from the NMR data by identifying frequency components that correspond to noise rather than signal, and selectively eliminating these harmonic components through filtering operations, thereby separating the useful NMR signal from the harmful noise
Solution Approach 2:
The patent introduces a filtering operation as an intermediary step between data acquisition and analysis, using frequency-domain filtering to mediate the removal of noise harmonics while preserving the NMR signal, thus improving measurement precision without affecting the underlying reservoir characterization capability
2Object-affected harmful factors
If filtering is applied to reduce noise, then noise is reduced, but the NMR signal may be distorted or lost
Solution Approach 1:
The patent applies different filtering characteristics to different frequency components, using local quality filtering where the filter strength and characteristics vary with frequency, allowing aggressive noise reduction at noise-dominated frequencies while preserving signal integrity at signal-dominated frequencies
Solution Approach 2:
The patent employs dynamic filtering operations that adapt to the signal characteristics, using variable filter parameters and adaptive thresholding that respond to the local signal environment, allowing the filter to maintain optimal performance across varying signal-to-noise conditions without introducing distortion
3Object-affected harmful factors
If noise reduction methods are applied to all NMR data, then noise is reduced, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial filtering only where necessary, using threshold-based decision making to determine when filtering is needed and how aggressive the filtering should be, avoiding unnecessary computational operations on data that is already clean or where the signal characteristics indicate filtering would be counterproductive
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
The method improves the accuracy of NMR data by reducing noise, enabling precise calculation of spin magnetization and reservoir properties like porosity, permeability, and fluid saturation, thereby enhancing the efficiency of well operations.
Implementation Method 1
NMR is a physical phenomenon in which hydrogen nuclei in a constant magnetic field are perturbed by an oscillating magnetic field and respond by producing a distinct electromagnetic signal
Implementation Method 2
NMR logging uses this phenomenon to create a controlled magnetic field and transmit one or more radio frequency (RF) pulses into the reservoir to magnetically polarize the hydrogen nuclei
Implementation Method 3
transforming the NMR data to a frequency domain and comparing the noise in the frequency domain to the predefined noise threshold
Implementation Method 4
The first filter is applied to the NMR data in a frequency domain by: transforming the NMR data to the frequency domain to obtain frequency-domain NMR data; determining amplitude thresholds for respective frequency windows of a moving average applied to the frequency-domain NMR data
Data Source
AI summary
Systems and methods are provided for reducing noise in nuclear magnetic resonance (NMR) data by filtering the NMR data based on the noise harmonic of the NMR data. The NMR data is generated using an NMR device in a well bore hole to perform a pulse sequence (e.g., an inversion recovery pulse sequence followed by a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence). When significant noise is observed, a Fast-Fourier Transform (FFT) transforms the echo data to identify a fundamental frequency (and harmonics) of the noise, and the window width of a moving filter is based on the fundamental frequency. The moving filter is used to determine a threshold, and the amplitudes of frequency coefficients within the window that exceed the threshold are reduced to generate the filtered data, which is transformed (e.g., via IFFT) back to the time domain to provide improved echo data for further NMR analysis.