An Improved Inversion Method of Reservoir Parameters Is Based on the Exact Reflection Coefficient Equation

• Published in: IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
• Main Authors: Miao, Fa-Wei, He, Yan-Xiao, Wang, Shang-Xu, Huang, Han-Dong
• Format: Article
• Language: English
• Subjects: Approximation; Bayes methods; Bayesian analysis; Bayesian theory; Computational efficiency; Conditional probability; Elastic properties; Errors; exact reflection coefficient equation; Mathematical models; Normal distribution; Parameters; petrophysical direct inversion; Physics; Porosity; Predictive models; Probabilistic logic; Probability density function; Probability density functions; Probability theory; Properties; Reflectance; Reflection; Reservoirs; Rocks; Seismic activity; Seismic data; Seismological data; Uncertainty
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3386569

As a significant step to characterize a reservoir, reservoir properties estimation plays an essential role in linking elastic parameters with petrophysical property parameters. Most conventional-used estimation methods are implemented in sequential tactics. However, predicting the rock and fluid properties from inverted seismic elastic attributes not only adds cumulative error but also increases the uncertainty and computational cost of inversion. We have developed a direct seismic reservoir properties estimation method intended for the disadvantages of sequential petrophysics inversion methods. The method incorporates the Keys-Xu approximate model into the seismic forward operator, to build a direct correlation between reservoir properties and observed seismic data. Reservoir parameters retrieved can be obtained from prestack seismic data based on the rock-physics model and the exact Zoeppritz equation. The inversion process combines Bayesian theory and Gaussian prior distribution, by which the estimation of petrophysical properties, such as porosity, clay volume, and fluid saturation, can be expressed as a posterior probability density function. The optimal solution is the random value corresponding to the maximum posterior probability density. Theoretical model tests and real dataset applications show that this method can obtain accurate results. The main advantage of this method is the cumulative error of the two-step method is decreased and the uncertainty in the inversion process is reduced. The using of the exact Zoeppritz equation avoids the approximation error of other approximations, such as Aki-Richards, Shuey, and so on, and makes the method applicable to far-offset seismic data.