The Consideration of Pore Size Distribution in Organic-Rich Unconventional Formations May Increase Oil Production and Reserve by 25%, Eagle Ford Case Study

2015 ◽  
Author(s):  
Lu Jin ◽  
Hui Pu ◽  
Yuhe Wang ◽  
Yinghui Li
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Oil Production ◽  
Eagle Ford

Quantification of pore size distribution in reservoir rocks using MRI logging: A case study of South Pars Gas Field

2017 ◽  
Vol 130 ◽  
pp. 172-187 ◽  
Author(s):  
Mohammad Esmaili ◽  
Seyed Reza Shadizadeh ◽  
Bahram Habibnia ◽  
Jalal Neshat Ghojogh ◽  
Behrooz Noruzi-Masir ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Gas Field ◽  
Reservoir Rocks

A method for determining oil-bearing pore size distribution in shales: A case study from the Damintun Sag, China

2018 ◽  
Vol 166 ◽  
pp. 673-678 ◽  
Author(s):  
Guohui Chen ◽  
Shuangfang Lu ◽  
Junfang Zhang ◽  
Marina Pervukhina ◽  
Keyu Liu ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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