Acoustic Logging Mud Slowness Determination
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Solution Overview
Problem
Conventional downhole acoustic logging systems face challenges in accurately determining real-time mud slowness and formation type due to low signal-to-noise ratios and interference from multiple wave modes, making it difficult to automatically pick shear waves in complex borehole wave fields, especially in hard or soft formations.
Innovation Solution
The method involves identifying formation type by determining mud wave slowness and using this information to constrain refracted shear wave slowness picking, combining monopole and dipole data processing to enhance accuracy and reliability through information sharing and dispersion processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional acoustic logging methods are used to extract slowness measurements from borehole waves, then slowness data can be obtained, but the signal-to-noise ratio is low and wave mode identification becomes challenging
Solution Approach 1:
The patent segments the complex borehole wave field into distinct wave modes (compressional, shear, Stoneley, leaky-P waves) and processes each separately using mode-specific filtering and picking algorithms. This segmentation allows accurate extraction of slowness measurements from each wave mode independently, overcoming the low signal-to-noise ratio problem by focusing processing power on identifying specific wave characteristics rather than treating all waves uniformly.
2Loss of information
If multiple wave modes are present in the borehole wave field, then comprehensive acoustic data is captured, but automatic picking of target wave modes becomes difficult
Solution Approach 1:
The patent introduces an intermediary classification system that acts as a mediator between the raw multi-mode acoustic data and the final slowness measurements. This intermediary layer uses machine learning classifiers and wave mode identification algorithms to automatically categorize and separate different wave modes, enabling accurate automatic picking even in the presence of multiple overlapping wave modes. The intermediary processing stage transforms the complex mixed signal into distinct, identifiable wave mode components.
3Extent of automation
If real-time processing is implemented without user input, then automated slowness picking can be achieved, but the system cannot distinguish between different wave types in hard or soft formations
Solution Approach 1:
The patent dynamically changes processing parameters based on detected formation characteristics and wave mode properties. The system automatically adjusts filtering thresholds, picking algorithms, and analysis parameters according to the specific formation type (hard or soft) and wave mode identified. This parameter adaptation enables reliable automatic distinction between different wave types without requiring user input, as the system self-adjusts to optimize discrimination accuracy for each specific downhole condition.
4Device complexity
If conventional processing methods are used with single source type, then processing simplicity is maintained, but the ability to distinguish shear waves from other waves is reduced
Solution Approach 1:
The patent merges data from multiple source types (monopole and dipole sources) and combines multiple processing approaches (frequency-domain analysis, time-domain picking, and mode classification) into a unified processing framework. This combination allows the system to distinguish shear waves from other wave modes with high accuracy by cross-validating results across different source types and processing methods, while maintaining relatively simple implementation through integrated automated workflows.
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 real-time determination of mud slowness and formation type, improving the accuracy of shear wave slowness picking and providing valuable characteristics like porosity and Young's modulus, thereby enhancing well planning and formation evaluation.
Implementation Method 1
acoustic waveforms are generated using a transmitter, and the acoustic responses are received using one or more receiver arrays
Data Source
AI summary
An acoustic logging system identifies hydrocarbon formation types by a real-time model-constrained mud wave slowness determination method using borehole guided waves. The system also combines data processing from different acoustic waveform processing techniques using an information sharing procedure, for example, using monopole source data and dipole source data, to further improve the processing results and to achieve more stable and reliable real-time shear slowness answers.