On The Pore Size Distribution: Derivation And Testing Of A Stochastic Model To Analyze Pore-Size Data From Carbonate Reservoirs

1997 ◽  
Author(s):  
Waddah T. Alhanai
Keyword(s):  
Stochastic Model ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Carbonate Reservoirs ◽  
Size Data

WELL LOGS IN CARBONATE RESERVOIRS

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 779-853 ◽  
Author(s):  
L. G. Chombart
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Water Saturation ◽  
Decisive Role ◽  
Minimum Cost ◽  
Carbonate Reservoirs ◽  
Well Logs ◽  
Rock Types ◽  
Pore Structures

Modern well logs can play an important, often decisive role in the evaluation of carbonate reservoirs, and in well completions therein. To do so however, the logs must be selected and interpreted with due regard for the specific rock “types” and pore structures encountered by each well. Indeed, the basic condition stated applies to all evaluation and completion techniques now in use or conceivable. It becomes vitally important in carbonate reservoirs, however, because of their extraordinary heterogeneity. Characteristically, these reservoirs exhibit significant, often extreme, and always unpredictable variations in pore structure, pore size distribution and fluid content, within very short distances, in any direction. To cope with such a reservoir, an evaluation and logging program adhering to certain principles is most likely to yield valid results and insure better completions and greater ultimate recovery, at minimum cost. First, in every well, the cuttings or cores should be described precisely as to rock types and depths. Second, any techniques used should permit the largest possible number of determinations through the reservoir, so that any existing relationships between pore size distribution, porosity and water saturation may be established on a sound statistical basis. Among logging devices, “focusing” tools meet this requirement best. Third, starting very early in the development of the reservoir, the latter should be cored and logged in key wells, the cores subjected to capillary pressure and other petrophysical tests, and all potentially diagnostic logs run and analyzed in the light of all other data. Fourth, in non‐key wells, the logging program should include only those logs proved most reliable in the key wells for the pore structures encountered and the data desired (usually porosity, water saturation, net ft of pay).

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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