Dynamic Reservoir and Stochastic Oil Pricing Model of IPC Contracts: Optimizing and Sensitivity Analyzing

Document Type : Research Paper

Authors

1 Prof., Department of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 Ph.D. Candidate, Department of Industrial Engineering, School of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran.

Abstract

Objective: Iran's oil reservoirs are operated in cooperation with international oil companies under a contract titled Iran Petroleum Contract (IPC). During the life of the concluded contracts, Iran and oil companies seek to maximize the economic value as well as the value of the cash flow. In this paper, IPC was modeled and the sensitivity of parameters was illustrated.
Methods: Contract financial flow was mathematically modeled considerin the physical characteristics of the oil well. This model was simulated using Matlab to evaluate the effect of different values of two parameters of production rate and wages in IPC contracts on the field production process.
Results: Results show that contractors were inclined to lower production rates if the fee per barrel is not set in the proper range (3$-7$). Furthermore, for very low oil prices (under 30$) contractor is at the risk of investment and for higher prices contractor’s share saturates.
Conclusion: All the parameters of the problem including contractual, reservoir, field parameters, oil price, investment costs, and operating costs play a role in the profitability of the project, and knowing contract parameters sensitivity can give Iran a clear view of negotiating the contract.

Keywords

References

جعفرنژاد، احمد؛ یوسفی زنوز، رضا (1387). ارائه مدل فازی رتبه‌بندی ریسک در پروژه‌های حفاری شرکت پتروپارس. مدیریت صنعتی، 1(1)، 21-38.
دوست محمدی، ایمان؛ عالم تبریز، اکبر؛ راد، عباس؛ زندیه، مصطفی (1399). طراحی و تبیین مدل تخصیص منبع افزونه و بافر برای بهبود پایایی پروژه‌ها در شرایط عدم قطعیت زمان و هزینه (مورد مطالعه: صنعت نفت و گاز). مدیریت صنعتی، 12(4)، 521-544.
عسکری، محمد مهدی؛ شیریجیان، محمد؛ طاهری فرد، علی (1394). تحلیل ساختاری مقایسه سطوح بهینه سرمایه‌گذاری و تولید نفت در قراردادهای بالادستی بیع متقابل مشارکت در تولید و قرارداد نفتی ایران. پژوهشنامه اقتصادی، 15(58)، 111-158.
گلابچی، محمود؛ فرجی، امیر (1394). مدل عصبی ـ فازی پشتیبان تصمیم فازهای اولیه پروژه‌های صنعت نفت. مدیریت صنعتی، 7(4)، 837-860.
محمدی، تیمور؛ مؤمنی، فرشاد؛ کاظمی نجف آبادی، عباس؛ بهادری، شیرکو (1395). تأثیر قراردادهای نفتی بر روند تولید نفت ایران. مطالعات اقتصاد انرژی، 12(5)، 25-52.
 
References
Al-Harthy, M.H. (2007). Stochastic oil price models: comparison and impact. The Engineering Economist, 52(3), 269-284.
Arps, J.J. (1945). Analysis of decline curves. Transactions of the AIME, 160(01), 228-247.
Askari, M.M., Shirijian, M. & Taheri Fard, A. (2016). Structural and Comparative Analysis of Optimal Levels of Investment and Oil Production in Upstream Contracts of Buy Back, Production Sharing & Iran Petroleum Contract, 15(58), 111-158. (in persian)
Azamipour, V., Assareh, M., Dehghani, M.R. & Mittermeir, G.M. (2017). An efficient workflow for production allocation during water flooding. Journal of Energy Resources Technology, 139(3), 32902.
Baumeister, C. & Kilian, L. (2015). Forecasting the real price of oil in a changing world: a forecast combination approach. Journal of Business & Economic Statistics, 33(3), 338-351.
Baumeister, C. & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. Journal of Economic Perspectives, 30(1), 139-60.
Dias, M.A.G. & Rocha, K. (2015). Petroleum concessions with extendible options: investment timing and value using mean reversion and jump processes for oil prices. Discussion Paper, 82.
Doost Mohamadi, I., Alem Tabriz, A., Raad, A. & Zandieh, M. (2020). Designing and Explaining a Redundancy Resource and Buffer allocation Model for Project Reliability Improvement with Time and Cost Uncertainty (The Case of Oil and Gas Industry Projects). Industrial Management Journal, 12(4), 521-544. (in Persian)
Farahi, M.M., Ahmadi, M. & Dabir, B. (2021). Model-based water-flooding optimization using multi-objective approach for efficient reservoir management. Journal of Petroleum Science and Engineering, 196, 107988.
Garratt, A., Vahey, S.P. & Zhang, Y. (2019). Real-time forecast combinations for the oil price. Journal of Applied Econometrics, 34(3), 456-462.
Golabchi, M., Faraji, A. (2015). Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects. Industrial Management Journal, 7(4), 837-860. (in Persian)
Haavardsson, N.F. & Huseby, A.B. (2007). Multisegment production profile models—A tool for enhanced total value chain analysis. Journal of petroleum science and engineering, 58(1-2), 325-338.
Hamilton, J.D. (2009). Understanding crude oil prices. The Energy Journal, 30(2), 179-206.
Höök, M., Söderbergh, B., Jakobsson, K., Aleklett, K. (2009). The evolution of giant oil field production behaviour, Natural Resources Research, 18(1), 39-56.
Hourfar, F., Khoshnevisan, L., Moshiri, B., Elkamel, A. (2020). Mixed H/Passivity controller design through LMI approach applicable for waterflooding optimization in the presence of geological uncertainty. Computers & Chemical Engineering, 142(9), 55-107.
Hoyland, L.A., Papatzacos, P. &Skjaeveland, S.M. (1989).Critical rate for water coning: correlation and analytical solution. SPE Reservoir Engineering, 4(04), 495-502.
Hurst, W. (1934). Unsteady flow of fluids in oil reservoirs. Journal of Applied Physics, 5(1), 20-30.
Jafarjejad, A. & Yousefizenoz, R. (2008). A Fuzzy Model of Ranking Risks at Petropars Company’s Excavation of Oil Well Projects. Industrial Management Journal, 1(1), 21-38. (in Persian)
Jinwang, F., Dong, H.E., Fang, L., Huizhi, Y. & Xiumin, C. (2007). Research on relationship of rate of oil production with stable production period and decline rate in fresh developing zone. Fault-Block Oil & Gas Field, 47-48.
Kaiser, M.J. &Yu, Y. (2010). Economic limit of field production in Texas. Applied energy, 87(10), 3235-3254.
Kilian, L. (2019). Measuring global real economic activity: Do recent critiques hold up to scrutiny? Economics Letters, 178, 106-110.
Mohammadi, T., Momeni, F., Kazemi Naja fAbadi, A. & Bahadori, S. (2016). Effect of Petroleum Contracts on Iranian Oil Production Trend. Quarterly Energy Economics Review, 12 (50), 25-52. (in Persian)
Mohammadi, T., Momeni, F., Kazeminajaf Abadi, A. & Bahadori, Sh. (2016). Effect of Petroleum Contracts on Iranian Oil Production Trend, quarterly Energy Economics review, 12(50), 25-52. (in Persian)
Pandey, R.K., Dahiya, A.K. & Mandal, A. (2021). Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations. Energy Technology, 9(1), 2000749.
Sagheer, A. &Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
Smith, J.L. (2014). A parsimonious model of tax avoidance and distortions in petroleum exploration and development. Energy Economics, Elsevier, 43(C), 140-157.
Van den Hof, P.M., Jansen, J.D., Heemink, A. (2012). Recent developments in model-based optimization and control of subsurface flow in oil reservoirs. IFAC Proceedings, 45(8), 189-200.
Van Everdingen, A. &Hurst, W. (1949). The application of the Laplace transformation to flow problems in reservoirs. Journal of Petroleum Technology, 1(12), 305-324.
Zhao, X., Luo, D., Xia, L. (2012). Modelling optimal production rate with contract effects for international oil development projects. Energy, Elsevier, 45(1), 662-668.
Industrial Management Journal
Volume 14, Issue 1
2022
Pages 143-167

Files

Share

How to cite

Statistics