Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Wenshu ZHA"'
Publikováno v:
Petroleum, Vol 9, Iss 3, Pp 364-372 (2023)
Various mechanisms are employed to interpret the low water recovery during the shale-gas production period, such as extra-trapped water in the fracture network, water imbibition due to osmotic pressure and capillary pressure. These lead to the diffic
Externí odkaz:
https://doaj.org/article/9a4a3f95f2d54079afc4cfff689e1219
Publikováno v:
Petroleum Exploration and Development, Vol 47, Iss 3, Pp 623-631 (2020)
Abstract: An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network (CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data tra
Externí odkaz:
https://doaj.org/article/a9098ee438524ba89cb5c545eae22d02
Publikováno v:
Journal of Chemistry, Vol 2015 (2015)
A mathematical dual porosity and dual permeability numerical model based on perpendicular bisection (PEBI) grid is developed to describe gas flow behaviors in shale-gas reservoirs by incorporating slippage corrected permeability and adsorbed gas effe
Externí odkaz:
https://doaj.org/article/65848cdd0ead4ee38b3b9cb64706351c
Publikováno v:
In Journal of Petroleum Science and Engineering November 2021 206
Publikováno v:
Computational Geosciences. 27:499-514
Publikováno v:
Physics of Fluids. 35:023603
Deep learning for solving partial differential equations (PDEs) has been a major research hotspot. Various neural network frameworks have been proposed to solve nonlinear PDEs. However, most deep learning-based methods need labeled data, while tradit
Publikováno v:
Petroleum Exploration and Development, Vol 47, Iss 3, Pp 623-631 (2020)
An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network (CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed b
Publikováno v:
Advances in Geo-Energy Research, Vol 4, Iss 1, Pp 107-114 (2020)
Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel d
Publikováno v:
Energy. 260:124889
Publikováno v:
Journal of Petroleum Science and Engineering. 216:110644