Prediction Method of Static Parameter Distribution of Reservoir Geological Modeling Based on Near Neighbor Neural Network

Abstract

The present invention relates to a method for predicting the static parameter distribution of reservoir geological modeling based on the nearest neighbor neural network. Coordinates and corresponding static parameter values; S200 uses the nearest neighbor algorithm to find the adjacent wells of each well; S300 establishes and trains the neural network model; S400 uses the optimal neural network model obtained from training to predict Static parametric distribution of points in unknown space. This method makes full use of the excellent ability of the neural network to approximate complex nonlinear functions, and can dig deep into the nonlinear distribution relationship of static parameters in space, which conforms to the complex characteristics of reservoir geology, can improve the accuracy of spatial interpolation, and can also be realized through multiple stochastic , to quantify the uncertainty of spatial interpolation and improve the accuracy of static parameter distribution predictions.

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

Images