Multi-point geostatistics three-dimensional modeling method based on deep learning

Abstract

The invention discloses a multi-point geostatistics three-dimensional modeling method based on deep learning. A method combining with deep learning is provided for reconstruction acceleration aiming at the problem of time consumption during the matching process of a traditional multi-point geostatistics computing method. A layer-by-layer reconstruction method is utilized to convert three-dimensional reconstruction into two-dimensional reconstruction. A deep neural network is designed to learn the mapping relationship from a sampling map to a porosity map, and the relationship is applied to thereconstruction of the sampling map. The method is characterized in that a thought of using deep learning to accelerate the matching process of the multi-point geostatistics method and a multi-point geostatistics three-dimensional modeling algorithm based on the thought are provided, a data set applied to the method is made, the performance of a network is evaluated by adopting a manner in which avisual effect and a counting function fixed variable are compared, compared with a pretty time-consuming manner in which point-by-point simulation is conducted of a traditional multi-point geostatistics method, the speed is greatly increased under the premise that the precision is ensured, and the method has better application value.

Description

technical field [0001] The invention relates to a three-dimensional modeling method of porous media based on two-dimensional images, in particular to a multi-point geological statistics porous media modeling method combined with deep learning, which belongs to the technical field of image processing. Background technique [0002] Porous media such as rock cores, alloys, and ceramics exist widely in nature and man-made environments, and play an important role in practical engineering applications. In practical applications, people need to have an accurate understanding and cognition of their macroscopic properties, and the macroscopic properties are directly determined by their microstructure, so it is very important to study their microstructure. [0003] Taking the core as an example, there are two main ways to obtain the microstructure image of the core. One is to use three-dimensional imaging techniques, such as computed tomography (CT), focused ion beam scanning electro...

AI Technical Summary

Problems solved by technology

However, the matching process of MPS needs to continuously retrieve the patterns of the training images, which is very time-consuming and occupies most of the time of the whole reconstruction process.

Although there is a method based on GPU acceleration at present, the acceleration effect is not very obvious, and it is essentially a point-by-point simulation mechanism.

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