Determining The Dynamics Of The Petroleum Buffer Reserve Of Indonesia
Keywords:
System dynamics, Petroleum Buffer Reserve, Energy security, Monte Carlo simulation, Import DependenceAbstract
This study develops an integrated system dynamics and probabilistic Monte Carlo simulation framework to evaluate Indonesia’s Petroleum Buffer Reserve (PBR) strategy in alignment with Perpres 96/2024 and the nation’s broader energy security agenda. Monte Carlo simulation results reveal that only the scenario characterized by full import availability is capable of achieving the mandated 10.17-million-barrel reserve target, while all other scenarios consistently fail to accumulate sufficient stock under uncertainty. The probabilistic analysis further shows that this scenario yields a probability exceeding 98% of meeting or surpassing the target, in contrast to the near-zero success rates observed under restricted import conditions. Days-of-cover optimization highlights an additional strategic vulnerability: the current PBR target corresponds to only about 12 days of crude import protection. Building on the system’s dynamic behavior, this study recommends a minimum reserve target of 30 days as an immediately achievable benchmark. A 60-day reserve is identified as a feasible medium-term objective, provided that replenishment rates and storage capacity are enhanced. Achieving a 90-day reserve, consistent with international strategic stockpiling standards, would require substantial investment and diversification of supply sources. These findings underscore Indonesia’s structural dependence on imported crude oil and emphasize the need for assertive replenishment policies to strengthen national energy resilience.
References
Abdel-Latif, A., Saad–Eldien, A., & Marzouk, M. (2023). System dynamics applications in crisis management: A literature review. Journal of Simulation, 17(6). https://doi.org/10.1080/17477778.2022.2088306
Ainuddin, & Suryadilaga, M. A. (2021). Oil and Gas in the Dynamics of Time and Development. Scientific Contributions Oil and Gas, 44(2). https://doi.org/10.29017/scog.44.2.590
Ariyon, M., Sastraningsih, E., Nurhayati, S., & Rahmatillah, P. (2025). Economic Analysis of Marginal Oil Field Development By Testing The Feasibility of GVM in Sharia Method Against NPV. Scientific Contributions Oil and Gas, 48(2), 347–362. https://doi.org/10.29017/scog.v48i2.1752
Azizsafaei, M., Hosseinian-Far, A., Khandan, R., Sarwar, D., & Daneshkhah, A. (2022). Assessing Risks in Dairy Supply Chain Systems: A System Dynamics Approach. Systems, 10(4). https://doi.org/10.3390/systems10040114
Betancourt, M. (2019). The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo. Annalen Der Physik, 531(3). https://doi.org/10.1002/andp.201700214
Perpres 96, BPK RI (2024). https://peraturan.bpk.go.id/Details/297377/perpres-no-96-tahun-2024
Buchholz, A., Chopin, N., & Jacob, P. E. (2021). Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo. Bayesian Analysis, 16(3). https://doi.org/10.1214/20-BA1222
Davahli, M. R., Karwowski, W., & Taiar, R. (2020). A system dynamics simulation applied to healthcare: A systematic review. In International Journal of Environmental Research and Public Health (Vol. 17, Issue 16). https://doi.org/10.3390/ijerph17165741
Fanta, G. B., & Pretorius, L. (2023). Sociotechnical factors of sustainable digital health systems: A system dynamics model. Health Policy and Technology, 12(1). https://doi.org/10.1016/j.hlpt.2023.100729
Fielding, A. L. (2023). Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes. In Journal of Radiotherapy in Practice (Vol. 22, Issue 247). https://doi.org/10.1017/S1460396923000079
Guo, L., Huang, X., Li, Y., & Li, H. (2023). Forecasting crude oil futures price using machine learning methods: Evidence from China. Energy Economics, 127. https://doi.org/10.1016/j.eneco.2023.107089
Guzzo, D., Pigosso, D. C. A., Videira, N., & Mascarenhas, J. (2022). A system dynamics-based framework for examining Circular Economy transitions. Journal of Cleaner Production, 333. https://doi.org/10.1016/j.jclepro.2021.129933
Lane, D. C., & Rouwette, E. A. J. A. (2023). Towards a behavioural system dynamics: Exploring its scope and delineating its promise. European Journal of Operational Research, 306(2). https://doi.org/10.1016/j.ejor.2022.08.017
Mardiana, S. (2023). Gasoline Policy Simulation to Increase Responsiveness Using System Dynamics: A Case Study of Indonesia’s Gasoline Downstream Supply Chain. International Journal of Energy Economics and Policy, 13(6). https://doi.org/10.32479/ijeep.14933
Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google trends research: Systematic review. Journal of Medical Internet Research, 20(11). https://doi.org/10.2196/jmir.9366
Nemeth, C., & Fearnhead, P. (2021). Stochastic Gradient Markov Chain Monte Carlo. In Journal of the American Statistical Association (Vol. 116, Issue 533). https://doi.org/10.1080/01621459.2020.1847120
Oliveira, F. S., Zahur, N. B., & Wu, F. (2023). Analysis of the optimal policy for managing strategic petroleum reserves under long-term uncertainty: The ASEAN case. Computers and Industrial Engineering, 175(November 2022), 108834. https://doi.org/10.1016/j.cie.2022.108834
Patel, M., & Patel, N. (2019). Exploring Research Methodology. International Journal of Research and Review, 6(3).
Prima, A., Moengin, P., Astuti, P., Nugrahanti, A., Dahani, W., Yanti, W., & Butt, O. J. (2025). The Dynamic Interplay between International Crude Imports and Exports and Domestic Production of Indonesia. IOP Conference Series: Earth and Environmental Science, 1486(1). https://doi.org/10.1088/1755-1315/1486/1/012051
Purwosaputra, A. A., Artana, K. B., & Dinariyana, A. A. B. (2022). System Dynamics Modelling for a Sustainable Natural Gas Supply and Demand in Indonesia to Meet up the Additional Demand of 52 Converted Power Plants. IOP Conference Series: Earth and Environmental Science, 972(1). https://doi.org/10.1088/1755-1315/972/1/012007
Rakhmanto, P. A., Notonegoro, K., Setiati, R., & Mardiana, D. A. (2025). Analysis on The Linkages and Multiplier Effects of The Upstream Oil and Gas Sector on Indonesia’s Economy Using The Input-Output Method. Scientific Contributions Oil and Gas, 48(1), 207–216. https://doi.org/10.29017/scog.v48i1.1700
Rokicki, T., Bórawski, P., & Szeberényi, A. (2023). The Impact of the 2020–2022 Crises on EU Countries’ Independence from Energy Imports, Particularly from Russia. Energies, 16(18), 6629. https://doi.org/10.3390/en16186629
Sadiyah, H., Iswandi, E., Thamrin, S., Sasongko, N. A., & Kuntjoro, D. D. (2021). Challenges and prospects of developing city gas to reduce imported LPG in Indonesia. IOP Conference Series: Earth and Environmental Science, 753(1), 012027. https://doi.org/10.1088/1755-1315/753/1/012027
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104. https://doi.org/10.1016/j.jbusres.2019.07.039
Sokhanvar, A., & Bouri, E. (2023). Commodity price shocks related to the war in Ukraine and exchange rates of commodity exporters and importers. Borsa Istanbul Review, 23(1). https://doi.org/10.1016/j.bir.2022.09.001
Suo, C., Li, Y. P., Mei, H., Lv, J., Sun, J., & Nie, S. (2021). Towards sustainability for China’s energy system through developing an energy-climate-water nexus model. Renewable and Sustainable Energy Reviews, 135, 110394. https://doi.org/10.1016/j.rser.2020.110394
Sy, C. (2023). Modeling the Dynamics of Petroleum Price Fluctuations using the System Dynamics Approach. https://doi.org/10.46254/an13.20230082
van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2018). A simple introduction to Markov Chain Monte–Carlo sampling. Psychonomic Bulletin and Review, 25(1). https://doi.org/10.3758/s13423-016-1015-8
Vlăduţ, O., Grigore, G. E., Bodislav, D. A., Staicu, G. I., & Georgescu, R. I. (2024). Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries. Energies, 17(1), 253. https://doi.org/10.3390/en17010253
ACKNOWLEDGEMENT
The authors would like to extend their deepest appreciation to Universitas Trisakti for providing the institutional support, research facilities, and financial assistance that enabled the successful completion of this study. The generous support from the university reflects its strong commitment to advancing academic excellence and contributing to national energy research. This institutional backing played a crucial role in ensuring that the research activities could be carried out effectively and without disruption.
Special appreciation is extended to all faculty members, research supervisors, and academic advisors who provided expert guidance, constructive feedback, and meaningful scholarly insights throughout the development of this study. Their contributions helped refine the methodological approach and improve the overall rigor of the analysis. Their professional dedication and academic mentorship have greatly enriched the quality of this research.
In addition, the authors recognize that several components of the manuscript—such as language refinement, structural organization, data visualization assistance, and analytical interpretation—were developed with the help of artificial intelligence (AI) tools. These tools were used responsibly to improve clarity, efficiency, and consistency throughout the writing process. All conceptual decisions, interpretations, and final academic judgments, however, were made solely by the authors to ensure the integrity and originality of the research.
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are free to Share — copy and redistribute the material in any medium or format for any purpose, even commercially Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms, under the following terms Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
