Article Per Year (5 Year)
p-Index From 2020 - 2025
1.8
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi TEKNOLOGIA Jurnal Migasian Akamigas Balongan Indramayu Petro : Jurnal Ilmiah Teknik Perminyakan Jurnal Migasian
RAKA SUDIRA WARDANA
UNIVERSITAS PERTAMINA
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Mathematics Public Health

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Migasian Akamigas Balongan Indramayu

 1 
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.
Designing Liquid-Gas Rate Window of Aerated Drilling Using Guo-Ghalambor Method Fauzia Fadhila Anwar; Raka Sudira Wardana
Jurnal Migasian Vol 4 No 2 (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.v4i2.135

Abstract

Loss circulation is a common problem in geothermal drilling due to naturally fractured formation and depleted reservoir pressure. This problem might lead to another problem such as a stuck pipe. In some cases, LCM is not effective in curing loss in a naturally fractured formation and cannot be used to cure loss circulation in the production zone. One of the methods that can be used to prevent loss circulation and also preventing reservoir damage in geothermal drilling is underbalanced drilling or aerated drilling. In an underbalanced or aerated drilling operation, the ratio of air injection rate ad liquid rate is critical to ensure the cutting carrying capacity while preventing hole problems. Usually, computer simulations are used to determine the safe gas-liquid rate limit due to the complexity of the multiphase flow in an underbalanced drilling system. Since the simulation software is not always available, a simpler and reliable method is needed to determine the gas-liquid rate limit in aerated drilling. the purpose of this paper is to design the operating window of the gas-liquid rate ratio in aerated drilling. the purpose of this paper is to design the operating window of gas-liquid rate ratio in aerated drilling using a simple yet reliable method such as the Guo-Ghalambor Liquid-Gas Rate Window method. The result of this research is a gas-liquid rate envelope that can be used to promote good cutting transport, preventing formation and borehole damaged while preventing loss circulation in geothermal well.
Stuck Pipe Detection For North Sumatera Geothermal Drilling Operation Using Artificial Neural Network Sarwono Sarwono; Lukas Lukas; Maria Angela Kartawidjaja; Raka Sudira Wardana
Jurnal Migasian Vol 6 No 1 (2022): 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.v6i1.192

Abstract

One of the most common problems encountered during geothermal drilling operations is stuck pipe. The risk of stuck pipe is higher in geothermal drilling operations since geothermal drilling targets the lost circulation zone at reservoir depth. The stuck pipe problem can cause a significant increase in drilling time and costs. The cost of a stuck pipe includes the time and money spent on extracting the pipe, fishing the parted BHA, and the effort required to plug and abandon the hole. Therefore preventing stuck pipes is far more cost effective than the most effective freeing procedures. Many researchers are working to identify the symptoms to reduce the risk of a stuck pipe. Due to the complexion of stuck pipe’s symptoms and indicators, some researcher proposed artificial intelligence (AI) as the tool to predict stuck pipes. Although researches have been made to build systems employing artificial intelligence (AI) to avoid stuck pipe occurrences in oil and gas drilling operations, few works have been done for geothermal drilling operations. This paper describes a study that employed Artificial Neural Networks (ANN) approaches to predict stuck pipe incidents. Field data were collected from 6 geothermal wells drilled in North Sumatera fields. ANN was used to construct models to forecast stuck pipe incidents. The investigation found that ANN showed good performance with 84% accuracy and 74% recall for the limited training dataset. These ANN approaches provide good predictions that can help increase response time and accuracy in preventing stuck pipes.
Co-Authors Bastian Andoni Bastian Andoni Fadhil Rhisnanda Fauzia Fadhila Anwar Hapsoro B.W Jan Friadi Sinaga Khansa Rasyidah Lukas Lukas Manahan Siallagan Maria Angela Kartawidjaja Meredita Susanty Ragil Sudira Wardana Sarwono Sarwono Weny Astuti