Real-Time Estimation Method of P-Wave Slowness Based on Kalman Filtering and STC
The real-time extraction of acoustic interval time is of significant importance for rapidly evaluating reservoir quality and timely optimizing exploration and development decisions. By analyzing the variation characteristics of acoustic wave propagation speeds in different rock formations, the physi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Editorial Office of Well Logging Technology
2025-06-01
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| Series: | Cejing jishu |
| Subjects: | |
| Online Access: | https://www.cnpcwlt.com/en/#/digest?ArticleID=5748 |
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| Summary: | The real-time extraction of acoustic interval time is of significant importance for rapidly evaluating reservoir quality and timely optimizing exploration and development decisions. By analyzing the variation characteristics of acoustic wave propagation speeds in different rock formations, the physical properties and structural features of subsurface strata can be effectively inferred. Acoustic interval time can not only be used to calculate formation thickness and wave velocity parameters, but also effectively identify interfaces and lithological changes within strata. The currently common real-time acoustic interval time extraction method is the first arrival threshold method. This method is prone to extracting incorrect modal waves in heterogeneous formations and complex borehole conditions, suffering from weak anti-interference capability, insufficient stability, and other issues. How to achieve stable interval time extraction while ensuring timeliness is a challenging problem. Aiming at the above problems, a novel method combining the Kalman filter algorithm with the slowness-time coherence (STC) method is proposed. This method can enhance the accuracy and stability of real-time compressional wave interval time extraction. Utilizing the Kalman filter to update the system's optimal state in real-time effectively improves the accuracy and stability of automated picking in low signal-to-noise ratio regions and slow formations. Furthermore, due to the lightweight and simplified nature of the Kalman filter, it can meet the real-time requirements of interval time extraction. This study processed acoustic logging data from three regions in China, including wireline logging and logging while drilling (LWD), verifying its advantages in extraction accuracy, stability, and timeliness under adverse conditions. It further expands the application scope of real-time interval time extraction algorithms, indicating broad prospects for the practical application of this method. |
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| ISSN: | 1004-1338 |