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Hapsoro B.W
Universitas Pertamina
Chemical Engineering, Chemistry & Bioengineering Chemistry Energy Engineering Environmental Science Health Professions Public Health

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Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data RAKA SUDIRA WARDANA; Meredita Susanty; Hapsoro B.W
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Akademi Minyak dan Gas Balongan Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.97

Abstract

Pore pressure is a critical parameter in designing drilling operations. Inaccurate pore pressure data can cause problems, even incidents in drilling operations. Pore pressure data can be obtained from direct measurement methods or estimated using indirect measurement methods such as empirical models. In the oil and gas industry, most of the time, direct measurement is only taken in certain depth due to relatively high costs. Hence, empirical models are commonly used to fill in the gap. However, most of the empirical models highly depend on specific basins or types of formation. Furthermore, to predict pore pressure using empirical models accurately requires a good understanding in determining Normal Compaction Trendline. This proposed approach aims to find a more straightforward yet accurate method to predict pore pressure. Using Artificial Neural Network Model as an alternative method for pore pressure prediction based on logging data such as gamma-ray, density, and sonic log, the result shows a promising accuracy.
Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data RAKA SUDIRA WARDANA; Meredita Susanty; Hapsoro B.W
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.97

Abstract

Pore pressure is a critical parameter in designing drilling operations. Inaccurate pore pressure data can cause problems, even incidents in drilling operations. Pore pressure data can be obtained from direct measurement methods or estimated using indirect measurement methods such as empirical models. In the oil and gas industry, most of the time, direct measurement is only taken in certain depth due to relatively high costs. Hence, empirical models are commonly used to fill in the gap. However, most of the empirical models highly depend on specific basins or types of formation. Furthermore, to predict pore pressure using empirical models accurately requires a good understanding in determining Normal Compaction Trendline. This proposed approach aims to find a more straightforward yet accurate method to predict pore pressure. Using Artificial Neural Network Model as an alternative method for pore pressure prediction based on logging data such as gamma-ray, density, and sonic log, the result shows a promising accuracy.
Co-Authors Meredita Susanty RAKA SUDIRA WARDANA