Research on High-Frequency Torsional Oscillation Identification Using TSWOA-SVM Based on Downhole Parameters

The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine (TSWOA-SVM) for accurate HFTO identification. Initially, the population is initialized using Fuch chaotic mapping and a reverse learning strategy to enhance population quality and accelerate the whale optimization algorithm (WOA) convergence. Subsequently, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient, balancing the global search and local exploration capabilities of WOA. A simulated annealing strategy is incorporated to guide the population in accepting suboptimal solutions with a certain probability, based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the optimized whale optimization algorithm is applied to enhance the support vector machine, leading to the establishment of the HFTO identification model. Experimental results demonstrate that the TSWOA-SVM model significantly outperforms the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in HFTO identification, achieving a classification accuracy exceeding 97%. And the 5-fold crossover experiment showed that the TSWOA-SVM model had the highest average accuracy and the smallest accuracy variance. Overall, the non-parametric TSWOA-SVM algorithm effectively mitigates uncertainties introduced by modeling errors and enhances the accuracy and speed of HFTO identification. By integrating advanced optimization techniques, this method minimizes the influence of initial parameter values and balances global exploration with local exploitation. The findings of this study can serve as a practical guide for managing near-bit states and optimizing drilling parameters.