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

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 ind...

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Vydané v:Chemistry and technology of fuels and oils Ročník 61; číslo 1; s. 195 - 212
Hlavní autori: Zhao, Chunlan, He, Xi, Guo, Ping, Jing, Jintao, Zheng, Wenjuan, Wu, Xiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.03.2025
Springer Nature B.V
Predmet:
Algorithms
Chemistry
Chemistry and Materials Science
Classification
Geotechnical Engineering & Applied Earth Sciences
Industrial Chemistry/Chemical Engineering
Innovative Technologies of Oil and Gas
Machine learning
Mineral Resources
Natural gas reserves
Neural networks
Oil reserves
Physical properties
Reservoirs
Residual gas
Sandstone
Support vector machines
Unsupervised learning
China
dynamic fine-grained recursion
distribution of remaining gas types
feature selection
unsupervised learning
ISSN:0009-3092, 1573-8310
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0009-3092
1573-8310
DOI:10.1007/s10553-025-01853-8