AI Mud Pulse Decoding for Noisy MWD Signal Classification

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Solution Overview

Problem

Signal integrity issues in measurement-while-drilling (MWD) data due to dynamic forces in well boreholes affect the accuracy of decoding mud pulse and electromagnetic telemetry signals.

Innovation Solution

Utilizing a trained machine learning model to classify and decode mud pulse signals, enhanced by user interface feedback for correcting signal values and synchronization, and continuous model updating with cloud-based computing systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional decoding methods are used for MWD signals, then the system complexity is low, but the measurement precision and reliability of signal decoding deteriorate due to signal integrity issues caused by dynamic forces in well boreholes

Engineering Contradiction:

Improvesignal decoding accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

A trained machine learning model serves as an intermediary between the received mud pulse signal and the decoding process. The model pre-processes and classifies the signal features, extracting meaningful patterns while filtering out noise and distortions caused by dynamic forces in the well borehole, thereby improving decoding accuracy without requiring complex manual processing systems

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanical signal processing methods with an artificial intelligence-based machine learning model. This substitution enables the system to automatically adapt to varying signal conditions and dynamic forces in the well borehole, significantly improving measurement precision while the model handles the complexity internally

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If machine learning models are continuously updated with user feedback, then the measurement precision and reliability improve, but the loss of time for model training and processing increases

Engineering Contradiction:

Improvesignal decoding reliabilityVSAvoidmodel training time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The machine learning model is pre-trained on a comprehensive dataset of mud pulse signals with various characteristics and noise patterns before deployment. This preliminary training establishes a robust baseline model that can reliably decode signals immediately, while subsequent user feedback is used for incremental updates rather than complete retraining, minimizing time loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback mechanism where user corrections to decoded values are captured and used to continuously update the machine learning model. This feedback loop gradually improves model reliability and accuracy over time, with each update making the system more robust against future signal variations and reducing the need for extensive retraining

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250389187A1Enhanced measurement-while-drilling decoding using artificial intelligence

Publication Date: 2025.12.25 ERDOS MILLER INC

AI summary

In one embodiment, a method is disclosed for using a trained machine learning model to classify mud pulse signals. The method may include receiving a mud pulse signal from a measurement while drilling (MWD) tool, wherein the mud pulse signal includes data. The method may also include decoding, using the trained machine learning model, the data to determine a value of the data, and providing a user interface comprising the value of the data for presentation on a computing device of a user.