Drilling Tool Euler Angle Solving for Dynamic Attitude Measurement
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Solution Overview
Problem
Existing Euler angle methods for drilling tools are limited to static measurements, making them inefficient and costly, and unable to provide continuous, dynamic, and real-time attitude parameter solutions due to data unavailability during tool vibrations.
Innovation Solution
A method utilizing a particle swarm optimization algorithm to calculate Euler angles by acquiring a rough rotation angle, disposing particles within a variation range, and iteratively updating their positions to converge on an optimal position using fitness functions based on geomagnetic field measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Euler angle method is used for attitude measurement, then the measurement precision can be achieved, but the method is only applicable when the drilling tool is static and cannot provide continuous dynamic measurement
Solution Approach 1:
The patent transforms the static Euler angle measurement method into a dynamic solution by introducing a particle swarm optimization algorithm that can continuously track and update attitude parameters during drilling operations. The algorithm dynamically adjusts particle positions and velocities to converge on optimal attitude solutions in real-time, enabling continuous measurement during tool rotation and vibration.
Solution Approach 2:
The patent replaces the traditional mechanical/static measurement approach with a computational optimization algorithm. Instead of relying on static accelerometer data, the system uses iterative mathematical optimization to solve for attitude parameters, substituting the mechanical measurement paradigm with a computational one that can handle dynamic conditions.
2Measurement precision
If the drilling tool is stopped for attitude measurement, then the measurement accuracy can be ensured, but the drilling cost increases and time efficiency decreases
Solution Approach 1:
The patent performs preliminary calculations by establishing a fitness function that incorporates rough rotation angles and variation ranges before the optimization process begins. This preliminary preparation enables the particle swarm algorithm to converge faster during actual measurement, reducing the time required for accurate attitude determination without requiring tool stoppage.
Solution Approach 2:
The patent enables continuous attitude measurement during drilling operations by using the particle swarm optimization algorithm to process data in real-time. The useful action of measurement continues uninterrupted during drilling, eliminating the need to stop the tool for measurement while maintaining accuracy through iterative optimization.
3Productivity
If the particle swarm optimization algorithm is used to calculate Euler angles, then the calculation efficiency and precision are improved, but the computational complexity increases
Solution Approach 1:
The patent segments the attitude calculation problem into manageable components by defining a fitness function with specific sub-components (rough rotation angle calculation, variation range determination, particle evaluation). This segmentation makes the complex optimization problem tractable by breaking it into sequential computational steps that can be efficiently executed.
Data Source
AI summary
The present disclosure discloses a solving method and system for an Euler angle attitude. The solving method includes: acquiring a rough rotation angle ΔT of a tool surface of a drilling tool in a search interval; performing calculation according to the rough rotation angle ΔT to obtain a variation range RangeT of the tool surface; disposing a plurality of particles within the variation range RangeT, corresponding position coordinates of the particles to an Euler angle value, randomly generating initial speeds of the particles, calculating a fitness function of each particle, and selecting the particle with the minimum fitness function as an optimal particle; updating a speed and a position of each particle, and gradually moving the particles closer to an optimal position by adopting an iteration method; and after performing n iterations until the precision of the fitness function of the optimal particle reaches a threshold.