z-logo
open-access-imgOpen Access
Evaluation of shale hydrocarbon potential in upper Talang Akar formation based on laboratory geochemical data analysis and Total Organic Carbon (TOC) modelling
Author(s) -
S. G.N. Azizah,
Abd Haris,
Julikah
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/538/1/012069
Subject(s) - oil shale , total organic carbon , source rock , hydrocarbon , geology , maturity (psychological) , kerogen , mineralogy , petroleum engineering , organic geochemistry , carbon fibers , geochemistry , environmental chemistry , paleontology , chemistry , psychology , developmental psychology , materials science , organic chemistry , structural basin , composite number , composite material
In shale hydrocarbon exploration, shale acts as the source rock and reservoir rock. This study aims to evaluate the potentional of shale hydrocarbons based on the laboratory geochemical data and TOC (Total Organic Carbon) modelling. Evaluation of shale hydrocarbons in sthe study field was carried out on two wells, X-1 and X-3 wells with a target in the upper part of Talang Akar formation as a source rock. Geochemical data is needed for evaluating the quality of the source rock by looking at the total organic carbon content, the maturity, and the hydrocarbon produced by the source rock. Analysis of the laboratory geochemical data resulted that the shale of upper Talang Akar formation had enough potential and mature organic material and also will generate the hydrocarbon in oil form. One of the main parameters for the success of shale hydrocarbons exploration is to know the amount of organic material of the source rock. TOC obtained from laboratory geochemical data is discrete data. Therefore, TOC modelling is carried out to obtain prediction of continuous TOC in upper Talang Akar formation in both wells. This prediction uses three different methods, those are Passey, multiple linear regression, and neural network. Based on those three methods, neural network produces the best data. The correlation obtained from X-1 well and X-3 well using neural network method are 0.96 and 0.84 respectively.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here