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All Journal Scientific Contributions Oil and Gas
Utomo Pratama Iskandar
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Chemical Engineering, Chemistry & Bioengineering Energy

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A SYSTEMATIC APPROACH TO SOURCE-SINK MATCHING FOR CO2 EOR AND SEQUESTRATION Usman Pasarai; Utomo Pratama Iskandar; Sugihardjo Sugihardjo; Herru Lastiadi S
Scientific Contributions Oil and Gas Vol 36 No 1 (2013)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.36.1.640

Abstract

Carbon dioxide for enhanced oil recovery (CO2 EOR) can magnify oil production substantially while aconsistent amount of the CO2 injected remains sequestrated in the reservoir, which is benefi cial for reducingthe greenhouse gas (GHG) emission. The success of CO2 EOR sequestration depends on the proper sourcessinksintegration. This paper presents a systematic approach to pairing the CO2 captured from industrialactivities with oil reservoirs in South Sumatra basin for pilot project. Inventories of CO2 sources and oilreservoirs were done through survey and data questionnaires. The process of sources-sinks matching waspreceded by scoring and ranking of sources and sinks using criteria specifi cally developed for CO2 EORand sequestration. The top candidate of CO2 sources are matched to several best sinks that correspond toadded value, timing, injectivity, containment, and proximity. Two possible scenarios emerge for the initialpilot where the CO2 will be supplied from the gas gathering station (GGS) while the H3 and F21 oil fi eldsas the sinks. The pilot is intended to facilitate further commercial deployment of CO2 EOR sequestrationin the South Sumatera basin that was confi rmed has abundant EOR and storage sinks as well as industrialCO2 sources.
SUITABILITY ASSESSMENT AND STORAGE CAPACITY ESTIMATES OF RAMBUTAN COAL SEAMS FOR CO2 STORAGE Utomo Pratama Iskandar
Scientific Contributions Oil and Gas Vol 36 No 1 (2013)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.36.1.648

Abstract

Coal seams are an alternative storage options besides depleted oil and gas reservoirs and deep salineformations. They are suitable to different degrees for CO2 geological storage as a result of various intrinsicand extrinsic. The potential use of this geological media requires suitability assessment and the amountof CO2 can be stored. This paper presents the fi rst attempt to evaluate the characteristics of coal seams inRambutan Field, South Sumatera, in terms of their suitability for CO2 storage and the potential storagecapacity. A set of 5 semi-qualitative criteria has been developed for the assessment of 4 seams that includespermeability, coal geometry, structure, homogeneity and depth. CO2 adsorption capacity estimates werederived from laboratory experiment by employing Isothermal Langmuir. The results show the 4 seams ingeneral are suitable for CO2 storage. The adsorption capacity from seam 2, 3, 5 and P are 22.18, 25.09,24.53, and 34.12 m3/t dry-ash-free basis respectively. The highest CO2 storage capacity can be stored atseam P enabling the CO2 in dense phase (supercritical).
Carbon Capture And Storage (Ccs) - Enhanced Oil Recovery (Eor): Global Potential In Indonesia Utomo Pratama Iskandar; Ego Syahrial
Scientific Contributions Oil and Gas Vol 32 No 3 (2009)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.32.3.855

Abstract

Total global CO2 emissions from fossil-fuel will still increase in the next ten decades. These are attributed to the heavy reliance of human activities with fossil fuels. The uncontrolled CO2 emissions from combustion of fossil fuels cause the CO2 concentration alteration in the atmosphere. As the result, this phenomenon cause global warming and change the climate globally. In the future, CO2 emissions are predicted in range from 29 to 44 GtCO2/year in 2020. Therefore it is necessary to abate the CO2  missions to the level that would prevent dangerous anthropogenic interference to the global climate system. The growth of energy efficiency improvements, the switch to less-carbon intensive fuels and renewable resources employment is still low in the context CO2 emissions mitigation. Carbon Dioxide Capture and Storage (CCS) as a third option for these mitigation options might facilitate achieving CO2  missions stabilization goals. As a part of the commitment and participation on combating the global warming, Indonesia has signed the Kyoto Protocol in 1998 and ratified it in 2004 through Law No. 17/2004. On the other side, Indonesia oil production has been declining since in the last ten years but demand for this energy is still high. In this frame CCS-Enhanced Oil Recovery (EOR) by CO2 injection might answer the global warming challenges and alongside contribute to increase the oil production in the near future. This paper presents a preliminary study of CCS-EOR potential in Indonesia. A brief explanation of geological setting and reservoir screening for site selection also presented. Then some discussions about CCS-EOR global potential will be highlighted as well as the analysis. It is hoped that this study would provide a standard guideline for determining CCS- EOR potential in Indonesia.
Long Short-term Memory (LSTM) Networks for Forecasting Reservoir Performances in Carbon Capture, Utilisation, and Storage (CCUS) Operations Utomo Pratama Iskandar; Masanori Kurihara
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.943

Abstract

Forecasting reservoir performances during the carbon capture, utilization, and storage (CCUS) operations is essential to monitor the amount of incremental oil recovered and CO2 trapped. This paper proposes predictive data-driven models for forecasting oil, CO2, and water production on the existing wells and future infill well utilizing long short-term memory (LSTM) networks, a deep learning variant for time series modeling. Two models are developed based on the number of phases referred to: 3-phases (3P) and 1-phase (1P), one interest phase at a time. The models are trained on the dataset from multiple wells to account for the effect of interference of neighboring wells based on the inverse distance to the target well. The performance of the models is evaluated using walk-forward validation and compared based on quality metrics and length and consistency of the forecasting horizon. The results suggest that the 1P models demonstrate strong generalizability and robustness in capturing multivariate dependencies in the various datasets across eight wells with a long and consistent forecasting horizon. The 3P models have a shorter and comparable forecasting horizon. The 1P models show promising performances in forecasting the fluid production of future infill well when developed from the existing well with similar features to the infill well. The proposed approach offers an alternative to the physics-driven model in reservoir modeling and management and can be used in situations when conventional modeling is prohibitively expensive, slow, and labor-intensive.
Co-Authors Ego Syahrial Herru Lastiadi S Masanori Kurihara Sugihardjo Sugihardjo Sugihardjo Usman Usman Pasarai