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The Potential of Remote Sensing Data for Oil and Gas Exploration in Indonesia: a Review Tri Muji Susantoro; Suliantara; Agung Budi Harto; Herru Lastiadi Setiawan; Gatot Nugroho; Danang Surya Candra; Adis Jayati; Sayidah Sulma; M Rokhis Khomarudin; Rahmat Arief; Ahmat Maryanto; Yohanes Fridolin Hestrio; Kurdianto
Scientific Contributions Oil and Gas Vol. 46 No. 1 (2023): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.46.1.317

Abstract

Oil and gas are important commodities in Indonesia and remain the main source for energy in various sectors. Therefore, the government aim to produce 1 million barrels of oil per day (BOPD) by 2030. To achieve this goal, exploration work is needed to discover new reserves and maintain production in existing fields. This study reviews the experience of oil and gas exploration in Indonesia using remote sensing data and the potential of using remote sensing data for oil and gas exploration through surface anomalies. Surface anomalies are changes or deviations that occur on the surface as the result of the presence of oil and gas underneath. These anomalies included vegetation growing stunted, yellowing or dying, changes in the quantity and composition of clay minerals, iron oxide, increased concentrations of hydrocarbons, helium, radon, carbon dioxide, microbes, and the presence of paraffin dirt formation, as well as geomorphological changes. This study aims to assess and explain the capabilities of remote sensing data in Indonesia for oil and gas exploration. The results show that remote sensing can be used for the initial exploration of oil and gas by delineating areas of potential oil and gas traps based on topographical anomalies and geological mapping integrated with gravity data and increasing confidence in the presence of oil and gas in the subsurface based on surface anomalies. These results are expected that the usefulness of remote sensing can be used to support oil and gas exploration in Indonesia and can be recognized and used for oil and gas activities by utilizing existing methods and discovering methods for data processing and their applications.
SISTEM INFORMASI PEMINJAMAN DAN PENGEMBALIAN ASET BMN POLITEKNIK NEGERI TANAH LAUT Sofyan, Akhmad; Kurdianto; Wicaksono, Hari; Jirah, Aina; Ariyani, Imelda; Isnania, Nur; Putriana, Dina; Aprianti, Winda; Syafaadi Rizki, Afian
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 11 No 2 (2025): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v11i2.14516

Abstract

The management of State-Owned Assets (SOA) is a critical function within government organizations, with the enhancement of service efficiency for the public as a top priority. However, at Politeknik Negeri Tanah Laut, the asset loan process for SOA is still carried out manually, leading to a waste of time and energy. This study aims to address this issue by developing a web-based Asset Loan and Return Information System. The system is built using the Laravel Framework, with PHP as the programming language and MySQL as the database management system. The main actors in this system include Admins, SOA Staff, and Borrowers. The system is designed to improve the efficiency of SOA asset management. In the system's development, Admins are responsible for managing loan and asset data, while SOA Staff handle inventory management. Borrowers can access features such as viewing inventory, submitting loan requests, and monitoring the status of SOA asset loans. The methods used in this research include needs analysis, and the data utilized covers loan information and SOA asset inventory. The results of this study show that the implementation of a web-based system can enhance the efficiency of SOA asset loans and returns at Politeknik Negeri Tanah Laut, reducing the time and energy required, and providing better transparency in inventory management. Based on UAT results, the system received the highest rating in category B with 50%, followed by category A with 30%, and category C with 20%.