Oil reservoir geologic modeling static parameter distribution prediction method based on neighbor neural network

Abstract

The invention relates to an oil reservoir geologic modeling static parameter distribution prediction method based on a neighbor neural network, and the method comprises the following steps: S100, selecting an oil reservoir geologic prediction range, and calling the known space coordinates and corresponding static parameter values of all wells in the oil reservoir geologic prediction range; s200, finding an adjacent well of each well by adopting a neighbor algorithm; s300, establishing a neural network model and training the neural network model; and S400, predicting static parameter distribution of unknown space points in the selected oil reservoir geological prediction range by utilizing the optimal neural network model obtained by training. The method makes full use of the excellent ability of a neural network to approach a complex nonlinear function, can deeply excavate the nonlinear distribution relationship of static parameters in space, accords with the complex characteristics ofoil reservoir geology, can improve the precision of spatial interpolation, can also quantify the uncertainty of spatial interpolation through multiple random implementation, and the static parameterdistribution prediction precision is improved.

Description

technical field [0001] The invention relates to the technical field of spatial interpolation of reservoir geological modeling, in particular to a method for predicting distribution of static parameters of reservoir geological modeling based on a neighbor neural network. Background technique [0002] Reservoir geological modeling is a necessary link in the understanding and development of underground reservoirs, and it is a high-level summary of the spatial distribution of reservoir size, reservoir parameters, and static parameters such as porosity and permeability. Reservoir geological modeling makes full use of data such as drilling data and logging interpretation, and focuses on the study of the correlation of various geological variables in space to accurately describe the properties of reservoirs or predict the spatial distribution of static parameters. And provide the basis for development plan formulation. Understanding the spatial distribution of reservoir static par...

AI Technical Summary

Problems solved by technology

[0007] Therefore, in view of the problems of low spatial interpolation accuracy and large uncertainty in traditional reservoir geological modeling, it is urgent to propose a method that is suitable for low-dimensional features and small data samples, and can deeply mine the complex nonlinear spatial dependencies of static parameters. Static Parameter Distribution Prediction Method for Quantifying Geostatistical Spatial Interpolation Uncertainty

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