A Genetic-Gauss-Newton Hybrid Algorithm for Optimizing Drilling Fluid Rheological Parameters

Optimization of drilling fluid rheological model parameters serves as the foundation and critical component of hydraulic calculations in drilling engineering. To address limitations of traditional nonlinear optimization algorithms—including high sensitivity to initial values and susceptibility to local optima—this paper introduces an adaptive rheological parameter optimization framework based on a Genetic-Gauss-Newton hybrid strategy. First, the genetic algorithm’s (GA) global search capability autonomously generates high-quality initial solutions, eliminating subjectivity from manual initial value setting. Subsequently, the Gauss-Newton (GN) algorithm’s local convergence characteristics refine the solutions through fine-grained iteration. By integrating mathematical representations of typical rheological models, a collaborative optimization mechanism of “Initial Value Independence-Global Exploration-Local Refinement” is established. Experimental validation demonstrates the algorithm achieves high-precision rheological parameter solutions without predefined initial values and completes model adaptability selection via a multi-dimensional evaluation system. The innovation lies in a fully automated optimization method eliminating manual initial value input, overcoming traditional algorithms’ strong initial value dependence. This provides a new methodological framework for drilling fluid rheological analysis, particularly suitable for real-time field testing scenarios, and offers significant engineering value for advancing intelligent drilling hydraulic calculations.