DAS S-Wave Reflection Detection with Phase Picking and Dip Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for processing distributed acoustic sensing (DAS) data lack efficient and accurate techniques for identifying microseismic shear wave (S-wave) reflections, which are crucial for characterizing subsurface geologic features and optimizing hydrocarbon exploration projects.
Innovation Solution
A method involving passive seismic event detection, phase picking, noise reduction, dip filtering, and generating S-wave microseismic reflection gathers is employed to automatically detect S-wave reflections in DAS data, utilizing unsupervised machine learning and denoising techniques to enhance signal quality and identify geological features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DAS data processing methods are used, then the processing is simpler, but the detection accuracy of S-wave reflections is insufficient
Solution Approach 1:
The method segments the complex DAS data processing into distinct functional modules: passive seismic event detection, phase picking, noise reduction, dip filtering, and S-wave reflection identification. Each module handles a specific aspect of the processing chain, making the overall complex system manageable and improving detection accuracy through specialized processing at each stage.
Solution Approach 2:
The method performs preliminary noise reduction and event detection before focusing on S-wave reflection identification. By pre-processing the DAS data to remove noise and identify potential seismic events, the system prepares the data in advance, which improves the accuracy of subsequent S-wave reflection detection without requiring complex real-time processing.
2Productivity
If manual processing methods are used, then the method is more interpretable, but the processing efficiency is low
Solution Approach 1:
The system implements automated processing that performs seismic event detection, phase picking, and S-wave reflection identification without requiring manual intervention at each step. The algorithm automatically processes DAS data through the complete workflow, significantly improving processing efficiency while maintaining interpretability through documented processing stages and output visualizations.
Solution Approach 2:
The method incorporates iterative refinement where processing results are evaluated and used to adjust subsequent processing parameters. The system provides feedback loops in the noise reduction and dip filtering stages, allowing automatic optimization of processing parameters based on the characteristics of the input data, thereby maintaining interpretability while achieving high automation.
3Reliability
If noise reduction techniques are applied, then the signal quality improves, but the processing time increases
Solution Approach 1:
The method applies noise reduction selectively to specific portions of the DAS data that contain potential S-wave reflections, rather than processing the entire dataset uniformly. By focusing computational resources on relevant time windows and frequency ranges identified through preliminary event detection, the system achieves improved signal quality without proportionally increasing overall processing time.
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 efficient and accurate detection of S-wave reflections, enhancing the characterization of fractures in geological formations and improving the precision of subsurface imaging for hydrocarbon exploration.
Implementation Method 1
An acoustic signal 18 encountering the fiber-optic cable 12 is recorded as a change in strain or strain-rate along the cable and may be considered to be a seismic event.
Data Source
AI summary
A method is described for automatically detecting shear-wave (S-wave) microseismic reflections using distributed acoustic sensing (DAS). The method includes obtaining raw DAS data; performing passive seismic event detection and phase picking; extracting a passive seismic event based on the passive seismic event phase picks to generate a seismic S-wave event gather; reducing noise in the raw DAS data; using the passive seismic S-wave event gather and the passive seismic event phases to identify an apex of a passive seismic S-wave event in the denoised DAS dataset and dividing it into two portions based on the apex; dip filtering the two portions to remove the direct arrival of the passive seismic S-wave events to generate a dip-filtered gather; and generating an S-wave microseismic reflection gather based on the dip-filtered gather.