Underground Kick Detection Using Kalman Filter and BP Neural Network
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional ground detection methods struggle to accurately and timely detect complex conditions during drilling, particularly in gas invasion and kick detection, due to delays in observing changes in mud pit volume and other parameters.
Innovation Solution
A drilling well underground kick processing method and device that utilizes a combination of Kalman filtering and a pre-trained BP neural network to predict and correct logging data in real-time, determining if a kick occurs by matching estimated and actual data, enabling timely processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ground detection methods are used, then the detection system is simple, but the detection accuracy and timeliness deteriorate due to delays in observing changes in mud pit volume
Solution Approach 1:
The patent implements a self-feedback adjustment mechanism where the BP neural network continuously receives actual logging data, compares it with predicted values from the Kalman filter, and uses the prediction errors to adjust and optimize the detection model in real-time. This feedback loop enables the system to adapt to changing drilling conditions and improve detection accuracy while maintaining real-time performance
Solution Approach 2:
The patent replaces traditional mechanical ground-based detection methods with an intelligent computational system combining Kalman filtering and BP neural networks. This substitution transforms the detection approach from passive observation of mud pit volume changes to active real-time prediction and error analysis of multiple logging parameters, significantly improving both accuracy and timeliness
2Adaptability or versatility
If traditional ground detection methods are used, then the device complexity is low, but the ability to detect complex underground conditions deteriorates
Solution Approach 1:
The patent creates a multi-functional detection system where the integrated processor performs multiple functions: Kalman filtering for state prediction, BP neural network for pattern recognition, real-time data processing, and kick detection. This universal system can handle various drilling conditions and parameters through a single integrated platform, enhancing adaptability while consolidating complexity into one cohesive device
Solution Approach 2:
The patent utilizes changes in multiple logging parameters (mud pit volume, flow rate, pressure, temperature) and processes them through dynamic models that adapt to different drilling conditions. The system monitors parameter changes in real-time and uses these variations to detect kicks under complex underground conditions, transforming static detection into dynamic adaptive monitoring
Data Source
AI summary
A drilling well underground kick processing method and device are described. The method includes: collecting actual logging data ztat current moment; predicting, according to a filtering estimation value {circumflex over (x)}t−1 of logging data at previous moment and the actual logging data zt at the current moment, a state prediction value {circumflex over (x)}−t and a filtering estimation value {circumflex over (x)}t of the logging data at the current moment under the normal drilling condition by using a Kalman filter; inputting a prediction error, an innovation vector and a Kalman filtering gain matrix Kt at the current moment into a pre-trained BP neural network; obtaining a corrected filtering estimation value {circumflex over (x)}1t of the logging data at the current moment according to a filtering residual and the filtering estimation value {circumflex over (x)}t of the logging data at the current moment; and determining that a kick occurs under the condition that the corrected filtering estimation value {circumflex over (x)}1t is not matched with the actual logging data zt.