The elasticity of the outer boundary and the solution of two-region composite reservoir seepage model

Li, Shunchu; He, Qian; Dong, Xiaoxu; Xia, Xing; Gui, Qinmin

doi:10.1080/10916466.2022.2048015

Cited by 3 publications

(1 citation statement)

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“…Cluster analysis can be based on the sample itself, automatic classification, no need to provide a classification standard. Li 11 clustered the geological attributes of multiple well points in the reservoir. For each small layer of the reservoir, the seepage capacity of the well point was graded and interpolated to obtain the cluster map of the seepage capacity of different small layers, which intuitively reflected the seepage capacity of each area of the small layer.…”

Section: Cluster Analysismentioning

confidence: 99%

Oil well productivity prediction in tight reservoirs based on machine learning

Zhu¹,

Wang²,

Tuo³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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The complex seepage laws and high production costs in tight oil reservoirs have prompted scholars to apply machine learning methods to optimize construction plans and predict development results. The machine learning method uses artificial intelligence algorithm to make data "speak" to reveal the internal relationship and change rule among parameters in the process of system operation. In this paper, principal component analysis, k-means clustering, time series and other methods are used to predict the production capacity of tight oil reservoirs, reveal the development law of tight oil reservoirs, and guide the efficient and rapid development of unconventional resources in China.

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Section: Cluster Analysismentioning

confidence: 99%