# Research Status of and Trends in 3D Geological Property Modeling Methods: A Review
## 3 Interpolation Methods for 3D Property Modeling
### Deep-Learning Based Modeling Methods  
In the rising stage of the second climax of deep learning, some scholars at home and abroad began to try to use the neural network method to carry out reservoir modeling research [102]. For example, [PERSON] et al. (1998) used seismic data to provide attribute distribution trends and converted seismic attribute parameters into reservoir parameters [103]. [PERSON] et al. (2007) used a high-order neural network to model porosity and sand body top depth [104]. [PERSON] et al. introduced a neural network into multipoint geo-statistics to develop a multipoint geological model. However, it has not been widely popularized due to problems such as difficult training and unstable effect [85; 86].  
At present, the reservoir modeling method based on the third climax of deep learning is in the ascendant, and some scholars and institutions have paid attention to this field. The research results mainly focus on the reservoir modeling method based on genetic adversarial networks. The basic idea is [105] (1) the generator network uses low-dimensional input to generate high-dimensional geological model output; (2) the generated geological model and the original geological model used for training are input into the discriminator network to judge whether the model is generated by the generator; (3) through mutual confrontation and optimization, the generator network generates a model as close to the real geological model as possible to \"confuse\" the discriminator network, so that it is difficult to distinguish whether the model is generated by the generator network, and the discriminator network identifies whether the model is generated by the generator network as far as possible until the discriminator network finally cannot distinguish whether the model is generated by the generator network. That is, the model generated by the generator network can conform to the actual geological model.  
[PERSON] et al. used WassersteinGAN, an improved form of generating a countermeasure network, to realize the channel background two-phase two-dimensional simulation of channels with different curvature [106]. [PERSON] et al. proposed SGAN to introduce the Markov Monte Carlo method into the generation countermeasure network to generate low-dimensional input and realized three-dimensional and multiphase simulation [107]. [PERSON] et al. [108] and [PERSON] et al. [109] further improved the training process of generating a countermeasure network, proposed a conditional simulation modeling method that meets both the geological model and well point hard data constraints, and reproduced the non-stationary state, which is difficult to deal with in traditional multipoint geostatistics. In addition, [PERSON] et al. used a convolution generation countermeasure network to carry out three-dimensional modeling of the core pore structure [110]. [PERSON] et al. used convolution to generate a countermeasure network to realize phase modeling based on seismic inversion data [111]. At present, the relevant algorithms and application verification are still in the stage of rapid development, and new research results continue to appear [112; 113; 114]. In addition, the PCA, MAF, PPMT, and other multivariate transform methods can be applied in the data preparation procedure for the deep-learning approaches dealing with the multivariate analysis [115].