A Reservoir and Fluid Prediction Method Based on Linear Mixing Prestack Elastic Parameter Inversion

Abstract

The invention relates to a pre-stack elastic parameter inversion realized reservoir and fluid prediction method based on linear mixture. By an inversion method of linear mixture and through linear calculation of longitudinal and transverse wave data and iteration of the speed ratio of longitudinal wave and transverse wave, elastic parameters are obtained efficiently and precisely, inversion errors by nonlinear inversion equation linearization are overcome, influence of wavelet estimation and low frequency model on inversion results is reduced, and defects of a nonlinear method are avoided. It is confirmed through model and practical data application that more accurate elastic parameters can be obtained based on the pre-stack elastic parameter inversion method of linear mixture. The invention has the following advantage: the method can directly be applied to practical data, and convergence results can be obtained within three times of iteration.

Description

technical field [0001] The invention belongs to the field of petroleum exploration, and in particular relates to a reservoir and fluid prediction method based on the inversion of pre-stack elastic parameters based on linear mixing, which is suitable for efficient and accurate calculation of pre-stack elastic parameters. Background technique [0002] Prestack inversion can simultaneously obtain multiple elastic parameters such as P-wave, S-wave, P-to-S wave velocity ratio, Poisson’s ratio, and Lamé constant, so that a more accurate comprehensive prediction of reservoir and fluid can be made from the elastic domain (Zhang et al. ,2013). The theoretical basis of its application is mainly derived from the theory of plane wave incidence and transmission - the Knot-Zoeppritz equation. The widely used Aki&Richards reflection coefficient formula for longitudinal wave incidence is: [0003] R pp ( θ ) ...

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Problems solved by technology

The current mainstream inversion methods usually firstly need to obtain the relationship between P and S waves from well logging data or petrophysical analysis results as prior information (for example, directly set α / β to 2), approximate the nonlinear AVO relationship to a linear relationship, and then use Methods such as least squares perform curve fitting or weighted superposition to invert various elastic parameters (Hou Bogang, 2006), but this must strongly rely on the shortcomings of the initial model, and the quality of the inversion results directly depends on the quality of the wavelet estimation

The nonlinear inversion method often adopts the generalized linear inversion method to transform the nonlinear least squares problem of solving model parameters into a linear least squares problem of solving model parameter corrections, and finally achieves the final inversion of all parameters by iteratively correcting model parameters. The purpose of lithology parameters, but the actual calculation is very time-consuming, and there may be multiple extreme values ​​in the optimization results (Hou Bogang, 2006)

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