A Review of Communication Technologies in Mud Pulse Telemetry Systems

Li, Chenxi; Xu, Zhengguang

doi:10.3390/electronics12183930

Cited by 2 publications

(2 citation statements)

References 71 publications

(103 reference statements)

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“…(5) Repeat steps (3) to (4) until the remaining number of data is less than N W , and obtain the filtered data X ′ filter . (6) Calculate the MSE of the original data X train and the filtered data X ′ filter as the loss function, and use Equation (10) to calculate the gradient value of this on the standard deviation threshold T. (7) Use the gradient value from step (6) to update the standard deviation threshold according to Equations ( 8) and ( 9). (8) Repeat steps (2) to (7), and perform multiple iterations to update until the loss function converges to a certain value and the optimal standard deviation threshold T W is obtained.…”

Section: Algorithmmentioning

confidence: 99%

“…However, this approach is limited by the low data transmission rates of LWD. The most common data transmission technology used in LWD is mud pulse telemetry (MPT), which operates at a rate of 0.5 bit/s to 1.0 bit/s [ 4 , 5 , 6 ]. Consequently, it is challenging to meet the real-time logging engineering requirements with this transmission rate.…”

Section: Introductionmentioning

confidence: 99%

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A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

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The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

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A physics-informed Bayesian data assimilation approach for real-time drilling tool lateral motion prediction

Song,

Shi,

et al. 2024

Front. Mech. Eng.

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In this study, a Bayesian data assimilation method that fuses physics with motion sensor data is demonstrated to infer the dynamic states at points of interest on the bottomhole assembly (BHA) with proper uncertainty quantification. A 4.75 inch-LWD (Logging-while-drilling) tool has been used as a use case, where the dynamic states at the formation evaluation sensor can be predicted in real time with the measurements at the motion sensor as the required inputs. This was achieved with a developed transfer function that utilizes unscented Kalman filtering technique. The robustness of the transfer function was evaluated with synthetic data obtained from finite element analysis (FEA) simulations for various BHA configurations and drilling conditions. It was found that the prediction by the transfer function agrees favorably well with the true states of motion at the formation evaluation sensor. Specifically, using the developed transfer function can help reduce the relative errors for the motion trajectories at the formation evaluation sensor by a factor of 3, and can significantly enhance measurement quality risk classification. The developed transfer function method was further assessed with experimental roll test data, which is considered as close to drilling conditions. The prediction by the transfer function was found consistently close to the ground truth in the presence of backward whirl. The developed modeling method can potentially have broader impacts by enabling fit-for-basin virtual V&V (Verification and Validation) to accelerate LWD tool development, or enabling future drilling optimization.

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A Review of Communication Technologies in Mud Pulse Telemetry Systems

Cited by 2 publications

References 71 publications

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

A physics-informed Bayesian data assimilation approach for real-time drilling tool lateral motion prediction

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