Multi-scale geological feature detection fusion method based on deep learning and evolutionary learning

Abstract

The invention discloses a multi-scale geological feature detection and fusion method based on deep learning and evolutionary learning, and relates to the technical field of geological feature detection and fusion, which includes the following steps: S1, output of reservoir evaluation parameters, S1.01, according to the input Multiple data volumes, seismic data, well logging data generate single and multi-body joint feature data, and mark the feature data. The multi-scale geological feature detection fusion method based on deep learning and evolutionary learning uses its own learning methods and data structures for different data, and realizes different evolution and learning according to different data. Different models can be used for different geology. During the learning process, the self-correction and improvement of the model and the evaluation of parameters will be realized. The learning process does not contain fixed models and models, and the final generated reservoir parameters have more geological significance. For the reservoir parameters Predictions will also be closer to reality.

Description

technical field [0001] The invention relates to the technical field of geological feature detection and fusion, in particular to a multi-scale geological feature detection and fusion method based on deep learning and evolutionary learning. Background technique [0002] In recent years, some companies and research institutions at home and abroad have made some new progress in the research of reservoir description. In 2017, Cao Junxing and others patented a reservoir detection method based on deep learning of seismic data; in 2016, Cheng Guojian et al. applied deep learning algorithms to study rock image processing; in 2016, Duan Youxiang et al. applied convolutional neural networks to predict geological reservoir parameters; 2018 In 2017, Zheng Yuzhe applied deep learning to study reservoir physical parameters; in 2017, Smith et al. applied deep learning to data fusion research, etc. [0003] Reservoir description technology is a technology applied to seismic data interpreta...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a multi-scale geological feature detection and fusion method based on deep learning and evolutionary learning, which solves the problem that the current analysis method based on logging data and seismic data inversion cannot fully and accurately identify reservoirs Porosity, fluid composition and oil and gas content, and the current application research is mainly to apply the existing technology of deep learning to the oil field for reservoir prediction, and there is no in-depth research on the essence of intelligent learning.

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