Well point fracture porosity prediction method based on dispersion characteristics of logging information

Abstract

The invention provides a well point fracture porosity prediction method based on dispersion characteristics of logging information. The well point fracture porosity prediction method based on the dispersion characteristics of the logging information comprises the following steps: Step 1, carrying out reservoir rock microscopic pore structure system division; Step 2, calculating the rock physical modulus of a dispersion medium model; Step 3, calculating the compressional wave velocity and the shear wave velocity of a rock physical model with multi-pore distribution; Step 4, carrying out matching inversion on the compressional wave velocity and the shear wave velocity of logging and the velocities of the theoretical model; and Step 5, when a matching objective function F(alpha<n>) is smallerthan delta Vp, outputting the attributes of reservoir fracture pores at a current point. The well point fracture porosity prediction method based on the dispersion characteristics of the logging information has the advantages that the logging information is utilized for fracture porosity prediction, so that the fracture development situation of a single well can be effectively predicted, and an effective basis can be provided for fracturing evaluation.

Description

technical field [0001] The invention relates to the field of exploration geophysics, in particular to a method for predicting the porosity of well point fractures based on the dispersion characteristics of well logging data. Background technique [0002] For oil and gas exploration and development, fractures are a very important feature of reservoirs. If the fractures in the oil and gas reservoir are well developed, it means that the reservoir has higher commercial value. On the contrary, if fractures are not developed, even if the formation contains oil and gas, considering a series of factors of industrial development costs, it may not even be considered as oil and gas reservoirs. In addition, the relevant physical characteristics of the fracture are also critical. Generally speaking, the larger the fracture size, the larger the fracture opening and closing degree, the larger the fracture space, and the higher the fracture density, it means that the reservoir has better ...

AI Technical Summary

Problems solved by technology

However, compared with P-wave data, S-wave data have a lower signal-to-noise ratio, are expensive to acquire, and are even difficult to obtain in 3D seismic

In addition, conventional studies usually take seismic wave velocity anisotropy as the final data, and few people use it to further calculate the relevant physical properties of fractures

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