Statistical Analysis Method for Optimal Prameters of Telemetry Communication
In order to improve the performance of telemetry of logging while drilling (TLWD) in complex drilling environment, the telemetry communication system is developing towards the trend of multi-architecture, multi-modal and multi-method, which brings a complication and inefficiency in the processing of...
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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=5741 |
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| Summary: | In order to improve the performance of telemetry of logging while drilling (TLWD) in complex drilling environment, the telemetry communication system is developing towards the trend of multi-architecture, multi-modal and multi-method, which brings a complication and inefficiency in the processing of selecting the optimal communication parameters. The paper proposes a statistical analysis-based optimization method for communication parameters of TLWD, utilizing massive measured data from downhole telemetry. The method involves extracting modulation-demodulation parameters, engineering parameters, signal characteristic parameters from intermediate demodulation processes, and system performance metrics. Which then calculates the influence factors of different parameters on performance indicators and identifies their dominant distribution ranges. Based on the influence factors and dominant distribution ranges, the method selects optimal values for communication parameters. This study validates its proposed communication parameter optimization method using telemetry data from Xinjiang experimental wells along with extensive simulated telemetry data from Yuedong and Lyuda experimental wells. The results confirm that the method significantly reduces the bit error rate by 3.3% and improves the success rate of deframing in most experimental wells. In addition, the proposed method completes a parameter optimization time of merely 0.08 seconds when processing 350 000 logging data points, demonstrating significant superiority over the existing optimization method based on machine learning and genetic algorithm. Furthermore, the optimized parameters identified by this approach prove more effective in reducing bit error rates. The method's proven optimization effectiveness and computational efficiency provide a practical solution for parameter optimization in TLWD. |
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| ISSN: | 1004-1338 |