Identification of gas-oil reservoir is always important but rather difficult in global gas-oil exploration. It is of the great significance to improve the accuracy of reservoir recognition. Seismic exploration is one of the most valuable methods of gas-oil exploration, and the huge amounts of seismic attribute data can be useful for gas-oil exploration. One limitation of the Generative Topographic Mapping (GTM) algorithm is that it cannot determine the classifications of the data points with close probabilities accurately, and it would be more likely to result in confused clarification and fuzzy boundary. To overcome the limitation, an advanced GTM algorithm with Euclidean Distance (GTM-ED) is proposed in this paper, and we use Euclidean Distance to compute the distance from the edge points to the neighbor centroids, and classify it to the closet class to avoid the problems of confused classification. And then the GTM-ED algorithm is used in the research of reservoir identification model, experiments are made with actual seismic data set. First of all, the GTM algorithm is discussed, and then the GTM-ED algorithm is introduced. And afterwards, many experiments are made. In the experiments, the log data and geological data are selected as the labels, and the comparison and analysis are made through three aspects, including relative criteria, absolute criteria, and run-time, and then the results of each model are visualized. The experimental results indicate that the GTM-ED can achieve better results in reservoir clustering and unknown reservoir identification. And in the actual application, the visualization of the GTM-ED can behave better than the GTM in showing the geological characteristics of paleochannel, the string of beads-like reservoirs and linear lava.
Hysteresis effects of three-phase relative permeabilities on black-oil reservoir simulation under WAG injection protocols