scholarly journals Facies And Diagenesis Of Tabalar And Tendehantu Carbonate Formation In Mangkalihat Peninsula Area: An Outcrop Preliminary Study To Oligocene – Miocene Reservoir Candidate Prospect

2012 ◽  
Vol 7 (2) ◽  
pp. 78-91
Author(s):  
Risman Adhitiya ◽  
Merza Media Adeyosfi ◽  
Syahreza S. Angkasa ◽  
Felix Sihombing
Keyword(s):  
Thin Section ◽  
Carbonate Rock ◽  
Carbonate Formation ◽  
Data Compilation ◽  
Sample Data ◽  
Preliminary Study ◽  
Thin Section Analysis ◽  
Fair Poor ◽  
Petrographic Thin Section ◽  
Section Analysis

Mangkalihat peninsula is located between Kutai and Tarakan basins, which known as two Hydro Carbon (HC) Prolific basins in Eastern Borneo. The petroleoum system in this area is poorly known because of the different system between Kutai and Tarakan Basin. The field study is focusing in the eastern part of Mangkalihat Peninsula, where The Tabalar and Tendehantu Formation are exposed. The data compilation is from outcrop, thin section and plug sample which permeability and porosity values were measured by Klickenberg method. Outcrop analysis showed that Tendehantu Formation can be divided into two lithofacies, whileTabalar Formation has only one lithofacies. The petrographic thin section analysis showed three microfacies from the two formations. Pore destruction caused by diagenesis can de indicated with the presence of bladed and equant cement in vuggy pores, while the diagenetical development of porosity is marked by the microfracturing that was assumed to be formed by compaction and deep burial and matrix dissolution in some of the samples. Petrography and plug sample data integration shows the quality value of those carbonate rock as a reservoir from the permeability and porosity parameter. Based on the microfacies grouping showed in three microfacies, the porosity value is 5.26 - 17.32 % (tight to good), and permeability value is 0.041 – 7.27mD (fair – poor). The carbonate rock quality is influenced by the whole diagenetic processes that happened in each lithofacies.

Image Processing and Machine Learning Approaches for Petrographic Thin Section Analysis

2017 ◽  
Author(s):  
Semen Budennyy ◽  
Alexey Pachezhertsev ◽  
Alexander Bukharev ◽  
Artem Erofeev ◽  
Dmitry Mitrushkin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Thin Section ◽  
Learning Approaches ◽  
Thin Section Analysis ◽  
Petrographic Thin Section ◽  
Section Analysis

Image Processing and Machine Learning Approaches for Petrographic Thin Section Analysis (Russian)

2017 ◽  
Author(s):  
Semen Budennyy ◽  
Alexey Pachezhertsev ◽  
Alexander Bukharev ◽  
Artem Erofeev ◽  
Dmitry Mitrushkin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Thin Section ◽  
Learning Approaches ◽  
Thin Section Analysis ◽  
Petrographic Thin Section ◽  
Section Analysis

Details of Thin Section Analysis of Pottery Fabrics

1987 ◽  
Vol 53 (S3) ◽  
pp. 53-59
Author(s):  
Carol Allen
Keyword(s):  
Thin Section ◽  
Clay Matrix ◽  
Thin Section Analysis ◽  
Maximum Measurement ◽  
Section Analysis

Terms usedGrog relates to clay inclusions within the pottery which appear as discrete pieces of varying sizes. Usually such entities are assumed to be crushed parts of pre-existing pots.Sizes of quartz, grog, shell or other inclusions referred to in the analysis concern measurements taken on the longest axis of the grain or piece. In each case the maximum measurement stated is the longest observed in that particular section, and does not infer that other grains in the sample were all of this size, unless stated.Percentages of materials shown as included in the clay matrix are estimates expressed by area using comparison charts (Terry and Chilingar 1955, and Flugel 1982, 247-257).

Sign in / Sign up

Export Citation Format

Share Document