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Journal : Journal of Petroleum and Geothermal Technology

Implementation of CO2 Source-Sinks Match Database Development. Case Study: West Java Tony, Brian; Nugraha, Fanata Yudha; Al Hakim, Muhamad Firdaus; Putra, I Putu Raditya Ambara; Chandra, Steven
Journal of Petroleum and Geothermal Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jpgt.v5i2.13432

Abstract

Carbon capture and storage (CCS) is widely recognized as a significant technology in mitigating carbon dioxide (CO2) emissions from major industrial facilities, such as power plants and refineries. CCS involves the capture of concentrated CO2 streams from point sources, followed by subsequent safe and secure storage in appropriate geological reservoirs. We developed spatial database system using Geographic Information System (GIS) tools to facilitate source-sink matching between CO2 emitter and CO2 storage to foster the implementation of CCS/CCUS technologies in Indonesia. In this study, we proposed workflow approach to determine the location of CO2 sinks/storage candidates given limited data available. Additionally, this method spatially characterizes and represents probable clusters where opportunities for CCS/CCUS implementation are present. We consider the existing pipeline route and Right of Ways (ROW) to minimize the potential cost related to transportation of CO2 using pipeline. The priority of available storage is classified based on the storage capacity, distance, and other technical criteria to determine the optimal location of potential CO2 injection. We applied the workflow to Coal Fired Power Plant in West Java as the CO2 source, and we obtained 6 depleted fields that are connected to the existing ROW with CO2 storage capacity of 42.03 MMT.
The use of PhaseNet for Event Identification of Microearthquake Monitoring in Geothermal Field Al Hakim, Muhamad Firdaus; Ambara Putra, I Putu Raditya
Journal of Petroleum and Geothermal Technology Vol. 6 No. 1 (2025): May
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jpgt.v6i1.13437

Abstract

Geothermal energy is a sustainable energy source that requires continuous microseismic monitoring to assess reservoir integrity and geomechanical behavior. Traditional phase identification methods are challenged by noisy environments and complex waveforms, especially in geothermal fields. This study explores the efficacy of PhaseNet, a deep learning neural network model, in detecting P and S wave arrival times for micro-earthquake events. The PhaseNet model was retrained using local seismic data from a geothermal field and tested for its performance in identifying seismic phases. The results were validated against a manual seismic catalog, with additional clustering and association analysis conducted using GaMMA and hypocenter locations determined with NonLinLoc. The findings demonstrate that PhaseNet, combined with GaMMA, provides robust phase detection capabilities, essential for early-stage monitoring in geothermal development.