Abstract
AbstractTo explore creep parameters and creep characteristics of salt rock, an Ansys numerical model of salt rock sample was established by using fractional creep constitutive model of salt rock, and an orthogonal test scheme was designed based on uniaxial compression test of salt rock samples. A large number of training data were obtained by combining the numerical model with the experimental scheme, and the model parameters were inverted by using the BP neural network. The model parameters are used for forwarding calculation, and the results are in good agreement with the measured strain data. This shows that the model parameter inversion method proposed in this paper can obtain reasonable parameter values and then accurately predict the creep behaviour of salt rock, which provides a good technical basis for related engineering practice and scientific research in the future.
Highlights
With the development of world industry, the demand for oil and gas energy is growing rapidly
The material parameters of salt rock are the basis for studying the long-term mechanical behaviour of salt rock, and obtaining accurate material parameters is helpful to promote the safe and efficient utilization of salt caverns, which is of great significance to the development of energy storage
This paper considers the inversion method of salt rock material parameters based on the constitutive model
Summary
With the development of world industry, the demand for oil and gas energy is growing rapidly. This paper considers the inversion method of salt rock material parameters based on the constitutive model. Bosch et al [20] established a general formula for inversion of seismic data under logging constraints based on petrophysical and geological statistical models and obtained the physical parameters of surrounding rock of oil and gas reservoirs with the help of logging data. The model is composed of three parts in series: transient creep represented by Hooke body, initial creep and steady-state creep characteristic represented by Abel dashpot, and accelerated creep characteristic represented by fractional-order nonlinear dashpot element with strain triggering This model involves five model parameters, and these parameters, respectively, represent the physical properties of the three components that make up the model. The parameters of the fractional-order model are retrieved by using a trained neural network
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