Online Extraction of Gravitational Acceleration While Drilling

To solve the serious distortion of attitude calculation caused by drilling tool vibration while drilling, an online method of gravitational acceleration extraction based on magnetic inertia golden jackal optimization (MIGJO) is proposed. Firstly, by analyzing the characteristics of vibration signals while drilling, the gravity extraction model is established. Secondly, according to the output characteristics of magnetic inertial sensors, the objective function and constraint conditions of gravitational acceleration are given. Then, according to the variation characteristics of different non-gravitational accelerations while drilling, a dimension-by-dimension dynamic scale random walk initialization is proposed, and the exploration and exploitation of the gravity factor balance algorithm is designed by using the relative error of gravity modulus and number of iterations. In addition, based on the information interaction factor and fitness value of the current solution, the attack defense coefficient is designed to coordinate the attack defense search strategy of the golden jackal, and the position of the solution vector is dynamically updated by taking advantage of the similarity between the current gravity solution and the local gravity design. Finally, the results of the gravity extraction simulation experiment and the actual drilling experiment show that MIGJO has the lowest average iterations and the lowest fitness value compared with GJO and PSO, respectively. The convergence speed is increased by 24.8%, and the average absolute errors of the calculated inclination angle and tool face angle are controlled within 0.65° and 0.82°, respectively, which improves the extraction accuracy of the gravitational acceleration while drilling.