Machine Learning Network for Well Casing Corrosion Detection
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Solution Overview
Problem
Existing methods for interpreting well log data, particularly for detecting casing corrosion, are labor-intensive, require expertise, and are sensitive to inversion parameter changes, limiting speed and repeatability.
Innovation Solution
A machine learning network is trained using datasets comprising casing thickness profiles and associated electromagnetic data to predict corrosion logs in target well sections, enabling automated interpretation and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inversion techniques and statistical methods are used to process electromagnetic data, then expertise-based interpretation can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual expert analysis (mechanical human cognition) with an automated machine learning system. The ML model is trained on historical well log data and corrosion outcomes, then automatically processes electromagnetic data to predict corrosion locations and severity, eliminating the need for labor-intensive expert interpretation while maintaining or improving accuracy
Solution Approach 2:
The patent performs preliminary training of the machine learning model using historical datasets before actual corrosion detection. This pre-training phase establishes the model's predictive capabilities, allowing rapid processing of new electromagnetic data without requiring time-consuming expert analysis during the actual detection phase
2Reliability
If manual interpretation methods are used, then expert judgment can be applied, but repeatability and consistency are limited
Solution Approach 1:
The patent replaces variable human expert judgment with a deterministic machine learning system. The same trained model produces consistent results when given identical input data, eliminating the variability inherent in manual interpretation while enabling faster processing through automated computation
Solution Approach 2:
The patent transforms the interpretation process from subjective expert parameter adjustment to objective model parameter application. The ML model uses fixed parameters learned during training, ensuring consistent application across different datasets and eliminating the inconsistency of manual parameter tuning by different experts
3Loss of information
If inversion techniques are used to process electromagnetic data, then corrosion log data can be obtained, but the process is sensitive to parameter changes and requires multiple processing steps
Solution Approach 1:
The patent replaces the complex multi-step inversion process with a streamlined machine learning prediction process. Instead of performing filtering, cleaning, editing, normalization, and inversion separately, the trained ML model directly predicts corrosion logs from raw or minimally preprocessed electromagnetic data, reducing complexity while maintaining information completeness
Solution Approach 2:
The patent creates a universal machine learning model that performs multiple functions simultaneously: data preprocessing, feature extraction, pattern recognition, and corrosion prediction. This single model replaces the need for separate processing steps, reducing overall system complexity while maintaining comprehensive corrosion data extraction
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 solution allows for rapid, automated interpretation of well log data, reducing reliance on human analysts and improving the accuracy and consistency of corrosion detection and remediation.
Implementation Method 1
corrosion log data from electromagnetic signal decay may be indicative of metal loss across the well casing
Data Source
AI summary
Systems and methods for automatic well integrity log interpretation verification are disclosed. The methods include obtaining a first dataset comprising casing thickness profiles and associated electromagnetic [EM]data from at least a first hydrocarbon well having a casing; selecting a training dataset using at least a subset of the casing thickness profiles and a subset of the associated EM data; and training, using the training dataset, a machine learning network to produce a predicted corrosion log of a target section of a second hydrocarbon well from measured EM data from the second hydrocarbon well.