Machine Learning Acoustic Logging for Material Identification
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Solution Overview
Problem
Current logging tools for oil and gas exploration often rely on single measurement methods, such as acoustic or electromagnetic techniques, which are inaccurate in predicting materials behind pipe strings due to human error in data interpretation.
Innovation Solution
An acoustic logging system utilizing a machine learning model, trained with data from multiple receivers, to predict materials outside the pipe string by processing sonic or ultrasonic pulse-echo and flexural wave data, including casing thickness and mud properties, enhancing accuracy through machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single measurement method (acoustic or electromagnetic) is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent combines multiple measurement methods (acoustic and electromagnetic) into a single logging tool that collects both types of data simultaneously. The acoustic system uses receivers to detect wave patterns while the electromagnetic system measures conductivity, and both datasets are integrated through machine learning algorithms to achieve more accurate material identification than either method alone.
Solution Approach 2:
The patent creates a composite measurement approach by integrating data from acoustically active materials (receivers detecting wave patterns) and electromagnetically active materials (conductive materials measuring electrical properties). This composite methodology allows the system to leverage the complementary strengths of both measurement types for improved material characterization.
2Reliability
If human determination of recorded data is used, then device complexity is reduced, but reliability deteriorates due to human error
Solution Approach 1:
The patent replaces the mechanical process of human data interpretation with an automated machine learning system. The neural network algorithm automatically processes the acoustic and electromagnetic data, identifying material properties without human intervention. This substitution eliminates human error while maintaining the ability to interpret complex multi-parameter datasets.
Solution Approach 2:
The machine learning model is trained to autonomously interpret the logging data without requiring human expertise for each measurement. The system self-calibrates and self-interprets the complex acoustic and electromagnetic signals, making independent determinations about material properties behind the casing based on the integrated data patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides continuous, accurate in-situ measurements of materials behind the pipe string, improving the accuracy of material identification and reducing human error, thereby enhancing wellbore monitoring and production operations.
Implementation Method 1
emit an acoustic signal which may be reflected and/or refracted off different interfaces inside a wellbore
Implementation Method 2
insonifying a pipe string within the wellbore with the acoustic logging tool, recording sonic or ultrasonic data
Data Source
AI summary
A method for identifying a material behind a pipe string. The method may comprise disposing an acoustic logging tool into a wellbore, insonifying a pipe string within the wellbore with the acoustic logging tool, recording sonic or ultrasonic data. The method may further comprise inputting the sonic or ultrasonic data into trained a machine learning model and identifying the material behind the pipe string using the trained machine learning model.