Adaptive Data Mask for Sonic Logging Waveform Processing
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Solution Overview
Problem
Conventional waveform processing methods for sonic logging data use fixed data masks, which can incorrectly filter out true data values in heterogeneous subterranean formations, leading to inaccurate slowness estimates due to aliasing issues.
Innovation Solution
The method involves transforming sonic logging data into a time slowness domain and generating an adaptive data mask using amplitude analysis, which suppresses alias data and enhances the accuracy of semblance analysis by filtering out unwanted values based on intensity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed data mask is used to filter alias data, then the processing efficiency is improved, but the measurement precision deteriorates due to incorrect filtering of true data values in heterogeneous formations
Solution Approach 1:
The patent implements dynamic data masks that adapt to local waveform characteristics in the time-slowness domain. Instead of applying a static fixed mask, the system calculates dynamic masks based on instantaneous amplitude ratios and local coherence patterns, allowing the mask boundaries to adjust automatically to heterogeneous formation conditions while maintaining processing efficiency through automated adaptation.
Solution Approach 2:
The patent changes the parameters used for mask generation from fixed static values to dynamic parameters derived from waveform analysis. By computing instantaneous amplitude ratios and local coherence metrics across the time-slowness domain, the system generates parameter-adaptive masks that respond to varying formation properties, thereby preserving true data values while filtering alias data.
2Device complexity
If a user-defined fixed data mask is applied, then the device complexity is reduced, but the reliability deteriorates due to inability to adapt to heterogeneous subterranean formations
Solution Approach 1:
The patent enables the data processing system to generate its own adaptive masks automatically through waveform analysis in the time-slowness domain. The system performs self-adjustment by computing local coherence patterns and instantaneous amplitude ratios, eliminating the need for manual mask definition while improving reliability through adaptation to actual formation conditions encountered during logging operations.
Solution Approach 2:
The patent performs preliminary waveform transformation to the time-slowness domain and computes local coherence patterns before applying the final filter. By preparing dynamic masks in advance based on instantaneous amplitude analysis and local coherence metrics, the system ensures reliable data filtering is already in place before critical processing decisions are made, improving both reliability and operational efficiency.
Data Source
AI summary
The disclosure is directed to waveform processing methods for collected sonic logging data. The data can be collected from various types of well systems. The methods utilize an amplitude analysis, utilizing the collected data, to build an adaptive data mask. The adaptive data mask can then be applied to a semblance analysis of the collected data to suppress or partially suppress alias data elements. A threshold parameter can be utilized to eliminate intensity values that do not satisfy the threshold criteria. The adaptive data mask can utilize amplitude or instantaneous amplitude analysis. Also, disclosed is a computer program product capable of executing the methods and algorithms described herein. A waveform processing system is disclosed that can perform the methods and algorithms as described herein.