Machine Learning Mud Pulse Recognition Network for Borehole Signal Processing
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Solution Overview
Problem
In borehole operations, the increased distance between the mud pulser and the receiving transducer leads to a less favorable signal-to-noise ratio and increased errors in mud pulse data transmission, particularly due to inter-symbol interference and noise contamination.
Innovation Solution
A machine learning mud pulse recognition network (MPRN) is employed to process and enhance the signal-to-noise ratio (SNR) of mud pulse data transmissions by utilizing neural networks, such as feedforward or convolutional networks, which are trained with pressure transducer data to adapt to drilling environments and integrate multiple transducer data, reducing the need for additional noise cancellation algorithms and enabling direct pulse detection and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the distance between the mud pulser and the receiving transducer is increased to transmit data from downhole to surface, then the data transmission capability is improved, but the signal-to-noise ratio deteriorates and errors increase
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with machine learning algorithms. The MPRN uses neural networks to automatically learn and extract mud pulse signals from noisy data, substituting conventional filtering and noise cancellation techniques. This allows the system to maintain high data transmission capability over long distances while improving signal-to-noise ratio through intelligent pattern recognition rather than mechanical signal conditioning.
Solution Approach 2:
The patent changes the approach from physical signal parameter optimization to data parameter optimization. Instead of adjusting physical parameters like transducer sensitivity or signal amplitude, the system transforms raw mud pulse data into normalized corrected data and processes it through machine learning models. This parameter transformation enables the system to achieve reliable data transmission over increased distances by optimizing data representation and processing methods.
2Reliability
If traditional noise cancellation algorithms are used to improve signal-to-noise ratio, then the signal quality is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical noise cancellation algorithms with machine learning-based signal recognition. The MPRN uses trained neural networks to distinguish mud pulse signals from noise through pattern recognition, eliminating the need for multiple sequential noise cancellation stages. This substitution reduces device complexity while maintaining or improving signal-to-noise ratio, as the machine learning model learns to filter noise during training without requiring complex real-time processing algorithms.
Solution Approach 2:
The machine learning mud pulse recognition network performs self-service by automatically adapting to different drilling environments and noise conditions. The model is trained on diverse data sets that include various noise patterns, enabling it to self-adjust and recognize signals without requiring manual tuning or complex adaptive algorithms. This self-learning capability reduces system complexity compared to traditional methods that require multiple adjustable parameters and algorithms.
Data Source
AI summary
This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.