SECONDARY POROSITY IDENTIFICATION: VUGS IN CARBONATE ROCK FORMATIONS USING MACHINE LEARNING BASED ON CONVENTIONAL LOGS DATA
In most cases, conventional methods that use well log and image analysis to determine and delineate secondary porosity are time-consuming and costly. Consequently, this research will use machine learning methods to process and analyze log data such as gamma ray, neutron, density and resistivity i...
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| 格式: | Theses |
| 語言: | Indonesia |
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| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/85076 |
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| 總結: | In most cases, conventional methods that use well log and image analysis to
determine and delineate secondary porosity are time-consuming and costly.
Consequently, this research will use machine learning methods to process and
analyze log data such as gamma ray, neutron, density and resistivity in wells that
do not have image log data, in order to improve the accuracy and rate of
identification of secondary porosity types in carbonate rock formations using
datasets that have been annotated with secondary porosity information from
Formation Micro Imager (FMI). This research can improve the efficiency and
accuracy of the secondary porosity identification process in carbonate rock
formations, in order to open up better reservoir discovery opportunities at
minimal cost. In this research, additional features such as shale volume, porosity
density, total porosity, and effective porosity were created to improve the
performance of the model. This research succeeded in finding an accurate and
efficient machine learning algorithm to determine the type of secondary porosity,
which is expected to reduce the time and cost for reservoir analysis and
characterization. |
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