Geophysical intelligent prediction method and device for oil reservoir seepage characteristic parameters and medium

Abstract

The invention relates to a geophysical intelligent prediction method and device for oil reservoir seepage characteristic parameters and a medium, and relates to the field of oil reservoir geophysicalexploration and development. The method comprises the following steps: acquiring elastic parameters of a reservoir; inputting the elastic parameters into m physical property parameter models, and outputting to obtain m prediction sub-parameters; carrying out weighted summation on the m prediction sub-parameters to obtain physical property parameters of the reservoir, wherein the physical propertyparameters are used for obtaining a prediction result of the remaining oil condition of the oil reservoir from the corresponding relation between the physical property parameters and the oil reservoirseepage field; after the elastic parameters of the reservoir are obtained, inputting the elastic parameters into different physical property parameter models, then inputting each prediction sub-parameter into a committee machine model, carrying out weighted summation corresponding to the weight of the prediction sub-parameter, and outputting to obtain the physical property parameters. By settinga plurality of physical property parameter models capable of predicting the physical property parameters from different dimensions, the prediction accuracy of the remaining oil of the oil reservoir isimproved.

Description

technical field [0001] The application relates to the field of geophysical exploration and development of oil reservoirs, in particular to a geophysical intelligent prediction method, device and medium of oil reservoir seepage characteristic parameters. Background technique [0002] In the process of oil and gas exploration, it is necessary to determine the reservoir conditions of the oilfield and obtain quantitative geological parameters used to indicate the geological characteristics of the reservoir. The geological parameters can characterize the oil and gas distribution of the oilfield reservoir and provide Provide guidance for the exploitation of oilfields. [0003] In related technologies, usually after obtaining reservoir elastic parameters such as compressional wave velocity, shear wave velocity, medium density and quality factor, the model based on the artificial neural network intelligent algorithm is input, and the reservoir physical parameters are obtained as out...

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Problems solved by technology

[0004] However, due to the complex geological conditions, the values ​​of the reservoir physical parameters obtained through the model based on the artificial neural network intelligent algorithm are relatively one-sided, and the prediction accuracy of the remaining oil in the reservoir is low

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