Research on the Distribution of Remaining Gas Based on the Dynamic Fine-Grained K-Means Recursive Algorithm

Abstract

Due to the poor physical properties of tight sandstone gas reservoirs and complex reservoir space, the development is difficult and the final recovery is low. The main reason is that the local enrichment of residual gas is difficult to be accurately described. In this paper, the high dimensional index system affecting residual gas was established, and the main controlling factors affecting residual gas were obtained through the dimensional reduction of machine learning. Firstly, two machine learning algorithms are used to identify the main factors affecting the remaining gas. Then use it as input to perform unsupervised learning on the grid using K-means and label it. Finally, by integrating the spatial coordinate parameters of the grids, setting thresholds, and dynamically recursively searching each grid, resulting in the distribution of remaining gas types for each layer. The results show that the main factors affecting the residual gas are reserve abundance, effective thickness and pressure. In addition, the first and second layers are dominated by high residual gas reservoirs, the third layer has more high residual gas reservoirs, the fourth and fifth layers are dominated by medium residual gas reservoirs, and the sixth layer has very little residual gas.