Multidimensional Sonic Data Compression for Telemetry Bandwidth Limits
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Solution Overview
Problem
In drilling and logging operations, the large amount of multidimensional sonic data generated by sonic measurement tools exceeds available telemetry bandwidth, necessitating effective compression methods to reduce data transmission requirements.
Innovation Solution
The proposed solution involves decorrelating and processing multidimensional sonic data using techniques such as Hadamard transforms and adaptive filtering to convert it into compressed data, which can be represented with fewer bits, thereby reducing the data volume while maintaining essential information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multidimensional sonic data is collected using multiple sensors and azimuthal directions, then measurement precision and information completeness are improved, but data volume increases consuming most or all available telemetry bandwidth
Solution Approach 1:
The patent extracts and transmits only the most critical formation characteristics derived from sonic data rather than transmitting the complete raw multidimensional sonic dataset. By identifying and transmitting only essential formation parameters, the system maintains measurement precision while significantly reducing data volume for telemetry transmission.
Solution Approach 2:
The patent performs preliminary processing and analysis of sonic data downhole to identify and extract key formation characteristics before transmission. By pre-processing the data to isolate essential information, the system reduces the amount of data requiring telemetry bandwidth while preserving the critical measurement precision needed for formation characterization.
2Quantity of substance
If compression is applied to reduce data volume, then telemetry bandwidth efficiency is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary identification and extraction of essential formation characteristics before compression and transmission. By pre-identifying which data elements are most critical, the compression algorithm can focus on preserving only those essential parameters, thereby reducing overall data volume while maintaining processing efficiency and avoiding excessive complexity.
3Measurement precision
If essential information is preserved during compression, then measurement precision is maintained, but data volume reduction efficiency decreases
Solution Approach 1:
The patent applies different processing and compression strategies to different components of the sonic data based on their relative importance. Critical formation characteristics are preserved with high fidelity while less critical data components undergo more aggressive compression. This localized quality approach maintains measurement precision for essential parameters while achieving overall data volume reduction.
Solution Approach 2:
The patent performs preliminary classification and prioritization of sonic data components to identify which parameters are essential for formation characterization. By pre-categorizing data by importance, the compression process can efficiently allocate bits and processing resources to preserve only the most critical information, thereby maintaining measurement precision while maximizing compression efficiency.
Data Source
AI summary
Compression of multidimensional sonic data is disclosed. Example methods disclosed herein to compress input multidimensional sonic data include decorrelating the input multidimensional sonic data to determine decorrelated multidimensional sonic data. In some such examples, the input multidimensional sonic data has a plurality of dimensions corresponding to a respective plurality of azimuthal directions from which the input multidimensional sonic data was obtained. In such examples, the decorrelated multidimensional sonic data is decorrelated among the plurality of dimensions of the input multidimensional sonic data. Such example methods also include processing the decorrelated multidimensional sonic data to determine compressed data representative of the input multidimensional sonic data.