Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
- Keyword
-
- Adsorption (1)
- Artificial Intelligence (1)
- Calculation (1)
- Chemical Model (1)
- Chemistry (1)
-
- Clustering (1)
- Clustering Algorithms (1)
- Clustering Results (1)
- Computation Theory (1)
- Controlled Study (1)
- Desorption (1)
- Diffusion (1)
- Diffusion Flux (1)
- Energy Resource (1)
- Fuzzy ART (1)
- Fuzzy Adaptive Resonance Theories (1)
- Gas Flow (1)
- Gas Production (1)
- Mathematical Computing (1)
- Mathematical Model (1)
- Mixed Type (1)
- Models, Chemical (1)
- Natural Gas (1)
- Oil And Gas Field (1)
- Oil And Gas Fields (1)
- Oil Shale (1)
- Permeability (1)
- Petroleum Industry (1)
- Porosity (1)
- Simulation (1)
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Modeling Of Gas Production From Shale Reservoirs Considering Multiple Transport Mechanisms, Chaohua Guo, Mingzhen Wei, Hong Liu
Modeling Of Gas Production From Shale Reservoirs Considering Multiple Transport Mechanisms, Chaohua Guo, Mingzhen Wei, Hong Liu
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Gas transport in unconventional shale strata is a multi-mechanism-coupling process that is different from the process observed in conventional reservoirs. In micro fractures which are inborn or induced by hydraulic stimulation, viscous flow dominates. And gas surface diffusion and gas desorption should be further considered in organic nano pores. Also, the Klinkenberg effect should be considered when dealing with the gas transport problem. In addition, following two factors can play significant roles under certain circumstances but have not received enough attention in previous models. During pressure depletion, gas viscosity will change with Knudsen number; and pore radius will increase when …
Clustering Data Of Mixed Categorical And Numerical Type With Unsupervised Feature Learning, Dao Lam, Mingzhen Wei, Donald C. Wunsch
Clustering Data Of Mixed Categorical And Numerical Type With Unsupervised Feature Learning, Dao Lam, Mingzhen Wei, Donald C. Wunsch
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains …