Deep Learning for Electrical Imaging Well Logging Feature Recognition
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Solution Overview
Problem
Traditional methods for automatic recognition and extraction of geological features in electrical imaging well logging images are limited by image segmentation quality, reliance on specific parameter indexes, and lack of universality, leading to inaccurate feature extraction and poor recognition accuracy, especially in complex geological scenarios.
Innovation Solution
A deep learning method is employed to construct a model with multiple hidden layers and an output layer, trained using historical data to recognize and optimize the extraction of geological features, enabling accurate and universal recognition of typical features in electrical imaging well logging images, including morphological optimization processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If image segmentation algorithm is used to separate feature regions, then automatic recognition and extraction can be achieved, but feature extraction accuracy deteriorates due to difficulty in accurate separation in complex geological situations
Solution Approach 1:
The patent replaces traditional mechanical image segmentation algorithms with a deep learning-based neural network system. The neural network automatically learns feature representations from raw imaging data without requiring manual segmentation, thereby maintaining automation while significantly improving feature extraction accuracy through hierarchical feature learning and adaptive parameter selection.
Solution Approach 2:
The patent dynamically adjusts key parameters including wavelet decomposition levels, neural network learning rates, and feature extraction thresholds based on the specific characteristics of the imaging data. This adaptive parameter optimization enables the system to maintain high accuracy across varying geological conditions while preserving automatic operation.
2Ease of operation
If classification methods based on statistical feature parameters are used, then feature classification can be performed, but recognition accuracy is limited and universality is poor due to influence of selected parameter indexes and specific regional geological conditions
Solution Approach 1:
The patent develops a universal deep learning model that can classify multiple types of geological features (fractures, caves, bedding, faults, folds) using the same architectural framework. The model learns transferable feature representations that generalize across different regional geological conditions, eliminating the need for region-specific parameter tuning while maintaining high classification accuracy.
Solution Approach 2:
The patent transitions from traditional statistical parameter-based classification to a multi-dimensional feature space created by the neural network. By representing features in this higher-dimensional space, the model can capture complex nonlinear relationships and achieve superior classification accuracy that is not constrained by the limited dimensionality of traditional statistical parameters.
3Difficulty of detecting and measuring
If traditional image processing methods are used, then specific geological feature targets can be recognized, but inability to meet differentiation and diversity needs of geological research and reservoir evaluation occurs
Solution Approach 1:
The patent employs hierarchical segmentation where the neural network first identifies coarse geological structures and then progressively detects finer feature details. This multi-scale approach enables simultaneous recognition of both specific feature targets and diverse geological patterns, satisfying both detailed detection requirements and broad research differentiation needs.
Solution Approach 2:
The patent implements a dynamic feature extraction system that adapts its processing depth and feature types based on the specific research objectives. The system can dynamically adjust between detailed fracture analysis, general geological classification, or reservoir evaluation metrics, providing versatile adaptability for different geological research and evaluation scenarios.
Data Source
AI summary
A method and an apparatus for automatically extracting image features of electrical imaging well logging, wherein the method comprises the steps of: acquiring historical data of electrical imaging well logging; pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering a full hole; recognizing and marking a typical geological feature in the electrical imaging well logging image covering the full hole, obtaining a processed image, and determining the processed image as a training sample according to types of the geological features; constructing a deep learning model including an input layer, a plurality of hidden layers, and an output layer; training the deep learning model using the training sample; using the trained deep learning model, recognizing type of a geological feature of an electrical imaging well logging image of a well section to be recognized, and performing morphological optimization processing on the recognition result to obtain a feature optimization recognition result. The solution can automatically, quickly and accurately recognize the typical geological features in the electrical imaging well logging image.