A New Bayesian Inversion Method for Thixotropic Model Parameters of Waxy Crude Oil

Waxy crude oil displays complex rheological behaviors, including viscoelasticity, thixotropy, and yield stress, under the gelling point temperature. To characterize these complex rheological behaviors, numerous models have been proposed, all of which typically involve extensive unknown parameters to be determined. Traditional least squares methods for determining model parameters have problems such as difficulty in determining the initial values for parameter fitting, susceptibility to falling into local optimal solutions, and instability of the fitting results. For this purpose, this paper proposes a parameter inversion method for determining thixotropic model parameters by integrating Adaptive Differential Evolution (ADE) with Bayesian inversion. By leveraging the global search capability of the ADE algorithm and the probabilistic uncertainty quantification advantages of the Bayesian method, the accuracy and stability of model parameter fitting are significantly enhanced. This paper conducted a comparative analysis between the proposed thixotropic model parameter inversion method and traditional methods based on the Houska model, using the thixotropic test data of Daqing and Xianhe waxy crude oil. The results show that the fitting errors of the new method are significantly smaller than those of the least squares method. Furthermore, the new method can invert to obtain superior thixotropic model parameters even under suboptimal initial parameter conditions while maintaining robust optimization capability. The novel method for inverting thixotropic model parameters outperforms the least squares method in terms of algorithm convergence, stability, robustness, and model fitting accuracy, solving the problem of difficult fitting of unknown parameters in thixotropic models.