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All Journal Scientific Contribution Oil and Gas
Utomo Pratama Iskandar
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Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Engineering Environmental Science

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.36.1.3

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 CO 2 EOR sequestration depends on the proper sources-sinks integration. 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 industrialCO 2 sources.
SUITABILITY ASSESSMENT AND STORAGE CAPACITY ESTIMATES OF RAMBUTAN COAL SEAMS FOR CO 2 STORAGE Utomo Pratama Iskandar
Scientific Contributions Oil and Gas Vol. 36 No. 1 (2013): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.36.1.5

Abstract

Coal seams are an alternative storage options besides depleted oil and gas reservoirs and deep saline formations. They are suitable to different degrees for CO2 geological storage as a result of various intrinsic and extrinsic. The potential use of this geological media requires suitability assessment and the amount of CO2 can be stored. This paper presents the fi rst attempt to evaluate the characteristics of coal seams in Rambutan Field, South Sumatera, in terms of their suitability for CO2 storage and the potential storage capacity. A set of 5 semi-qualitative criteria has been developed for the assessment of 4 seams that includes permeability, coal geometry, structure, homogeneity and depth. CO2 adsorption capacity estimates were derived from laboratory experiment by employing Isothermal Langmuir. The results show the 4 seams in general 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 m 3/t dry-ash-free basis respectively. The highest CO2 storage capacity can be stored at seam P enabling the CO2 in dense phase (supercritical).
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): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Herru Lastiadi S Masanori Kurihara Sugihardjo Usman