Convolutional Neural Network for While-Drilling Risk Recognition
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Solution Overview
Problem
Current drilling technologies face challenges in recognizing and addressing safety risks in real-time due to subjective human judgment, high complexity, and limited historical data, leading to inefficiencies and increased costs.
Innovation Solution
An intelligent recognition method using a convolutional neural network for while-drilling safety risks, which processes and analyzes monitoring data to extract features autonomously, reducing subjectivity and improving real-time performance through data preprocessing and feature learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial recognition by on-site monitoring personnel is used, then professional knowledge and experience are utilized for judgment, but the judgment results have strong subjectivity and time latency
Solution Approach 1:
The patent replaces the mechanical system of human monitoring with an automated recognition system based on convolutional neural networks. The system automatically processes monitoring data and generates recognition results, eliminating human subjectivity and time latency while maintaining high judgment accuracy through deep learning algorithms
2Measurement precision
If complex expert systems and feature extraction algorithms are used, then recognition accuracy is improved, but the system complexity increases and adaptability decreases
Solution Approach 1:
The patent employs a convolutional neural network that performs autonomous feature extraction and recognition without requiring complex preprocessing or expert rule formulation. The system learns directly from raw monitoring data, automatically adapting to different well characteristics and safety risk patterns, thereby reducing system complexity while maintaining high recognition accuracy
3Measurement precision
If more historical data is collected for network training, then recognition accuracy is improved, but the limitations of application conditions increase and real-time performance deteriorates
Solution Approach 1:
The patent uses a convolutional neural network that can achieve high recognition accuracy with limited historical data. The network architecture is designed to extract essential features efficiently from available data, avoiding the need for extensive data collection that would compromise real-time performance. The system processes current monitoring data quickly while leveraging learned patterns from training data
Data Source
AI summary
The present invention discloses an intelligent recognition method for while-drilling safety risks based on a convolutional neural network. The method includes the following steps: 1, processing while-drilling safety risk parameter features and data, and establishing a correlation analysis model for monitoring-while-drilling parameters by using a Pearson coefficient correlation analysis method; 2, processing while-drilling safety monitoring data, analyzing a time span of each sample, constructing training sample data and test sample data, and preprocessing the samples; 3, designing a while-drilling safety risk recognition network structure; and 4, recognizing while-drilling safety risks by the trained safety risk recognition network. The method of the present invention is applied to monitoring-while-drilling engineering, which can greatly improve the drilling efficiency and a reservoir drilling rate, reduce a complex accident rate and cost in drilling, provide a powerful safety guarantee for drilling work, meet the current urgent demands for cost reduction and efficiency enhancement in drilling to a certain extent, and also provide a new idea for the development of intelligent drilling technologies in China.