Fast Analysis of Optimal IOR Switch Time Using a Two-Factor Production Model and Least-Squares Monte Carlo Algorithm
- Aojie Hong (The National IOR Centre of Norway and University of Stavanger) | Reidar B. Bratvold (The National IOR Centre of Norway and University of Stavanger) | Larry W. Lake (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Norway One Day Seminar, 18 April, Bergen, Norway
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 7 Management and Information, 7.2 Risk Management and Decision-Making, 5.7.2 Recovery Factors, 5 Reservoir Desciption & Dynamics, 5.7 Reserves Evaluation, 5.5 Reservoir Simulation, 7.2.3 Decision-making Processes
- Least-Squares Monte Carlo Algorithm, IOR Switch Time, Two-Factor Production Model, Decision Analysis, Value-of-Information
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- 85 since 2007
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Simple or proxy production models are potentially very useful and tractable because they are computationally attractive whilst still providing insight to the decision at hand. Useful and tractable models are required for supporting high-quality decisions in uncertain, complex, and computationally demanding contexts. A key decision for development planning is: what is the optimal time to initiate an Improved-Oil-Recovery (IOR) process. We aim to illustrate the implementation and application of a useful and tractable approach for the analysis of the optimal IOR switch time using a two-factor production model and Least-Squares Monte Carlo (LSM) simulation.
The two-factor production model contains only two parameters for each recovery phases. One parameter describes how much recovery efficiency a recovery mechanism can ultimately achieve whilst the other describes how fast the recovery efficiency increases. The simplicity of the model makes it computationally attractive. The LSM algorithm is an approximate dynamic programming approach, which allows for learning over time. It provides a near-optimal solution for the IOR switch time problem. The Value-Of-Information (VOI) framework—a powerful decision-analysis tool—provides an estimate of the value of learning.
Closed-Loop Reservoir Management (CLRM) is considered to be a state-of-the-art approach to solving for the optimal IOR switch time. However, this approach can produce a suboptimal solution as the CLRM approach considers only uncertainties and actions reflecting currently available information but not those uncertainties and actions arising from future information. The dynamic programming approach used here considers both the impact of the information obtained before a decision is made and the impact of the information that might be obtained to support future decisions. We conclude that a dynamic programming approach, such as the LSM algorithm, can significantly improve both the timing and value of decisions, leading to a significant increase in a field's economic performance. Furthermore, the two-factor model combined with the LSM algorithm is tractable and provides useful insight into the IOR switch time problem.
The novelties provided by this work are: developing and illustrating the structure of the IOR switch time problem in a decision tree, demonstrating and discussing the suboptimality of the CLRM solution, developing and illustrating the detailed steps of applying the LSM algorithm for the IOR switch time decision, and implementing the two-factor model combined with the LSM algorithm for analyzing the optimal IOR switch time.
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Alkhatib, A. M., and King, P. R. 2011. Applying Real Options Theory in Determining Optimal Policies for a Surfactant Flood. Presented at the SPE Enhanced Oil Recovery Conference, Kuala Lumpur, Malaysia, 19–21 July. SPE-144869-MS. http://dx.doi.org/10.2118/144869-MS.
Bickel, J. E., and Bratvold, R. B. 2008. From Uncertainty Quantification to Decision Making in the Oil and Gas Industry. Energy Exploration & Exploitation 26 (5): 311–325. https://doi.org/10.1260/014459808787945344.
Bratvold, R. B., Bickel, J. E., and Lohne, H. P. 2009. Value of Information in the Oil and Gas Industry: Past, Present, and Future. SPE Reservoir Evaluation & Engineering 12 (4): 630–638. SPE-110378-PA. http://dx.doi.org/10.2118/110378-PA.
Barros, E. G. D., Leeuwenburgh, O., Van den Hof, P. M. J. 2015. Value of Multiple Production Measurements and Water Front Tracking in Closed-Loop Reservoir Management. Presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, 14–16 September. SPE-175608-MS. http://dx.doi.org/10.2118/175608-MS.
Brouwer, D. R., Nævdal, G., Jansen, J. D. 2004. Improved Reservoir Management Through Optimal Control and Continuous Model Updating. Presented at the SPE Annual Technical Conference and Exhibition, Houston, USA, 26–29 September. SPE-90149-MS. https://doi.org/10.2118/90149-MS.
Chen, Y., Oliver, D. S., Zhang, D. 2009. Efficient Ensemble-Based Closed-Loop Production Optimization. SPE Journal 14 (4): 634–645. SPE-112873-PA. https://doi.org/10.2118/112873-PA.
Howard, R. A. 1966. Information Value Theory. IEEE Transactions on Systems Science and Cybernetics 2 (1): 22–26. http://dx.doi.org/10.1109/TSSC.1966.300074.
Howard, R. A. 1980. An Assessment of Decision Analysis. Operations Research 28 (1): 4–27. https://doi.org/10.1287/opre.28.1.4.
Hong, A. J., Bratvold, R. B., and Nævdal, G. 2017. Robust Production Optimization with Capacitance-Resistance Model as Precursor. Computational Geosciences. https://doi.org/10.1007/s10596-017-9666-8.
Hong, A. J., Bratvold, R. B, Thomas, P. 2018. Value-of-Information for Model Parameter Updating through History Matching. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2018.02.004.
Jafarizadeh, B., and Bratvold, R. B. 2012. Two-Factor Oil-Price Model and Real Option Valuation: an Example of Oilfield Abandonment. SPE Economics & Management 4 (3): 158–170. SPE-162862-PA. http://dx.doi.org/10.2118/162862-PA.
Jafarizadeh, B., and Bratvold, R. B. 2013. Sell Spot or Sell Forward? Analysis of Oil-Trading Decisions with the Two-Factor Price Model and Simulation. SPE Economics & Management 5 (3): 80–88. SPE-165581-PA. http://dx.doi.org/10.2118/165581-PA.
Jansen, J. D., Brouwer, R., Douma, S. G. 2009. Closed Loop Reservoir Management. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, USA, 2–4 February. SPE-119098-MS. https://doi.org/10.2118/119098-MS.
Jochen, V. A., and Spivey, J. P. 1996. Probabilistic Reserves Estimation Using Decline Curve Analysis with the Bootstrap Method. Presented at the SPE Annual Technical Conference and Exhibition, Denver, USA, 6–9 October. SPE-36633-MS. http://dx.doi.org/10.2118/36633-MS.
Longstaff, F., and Schwartz, E. 2001. Valuing American Options by Simulation: a Simple Least-Squares Approach. Review of Financial Studies 14 (1): 113–147. http://dx.doi.org/10.1093/rfs/14.1.113.
Nævdal, G., Brouwer, D. R., Jansen, J. D. 2006. Waterflooding Using Closed-Loop Control. Computational Geosciences 10 (1): 37–60. https://doi.org/10.1007/s10596-005-9010-6.
Thomas, P, and Bratvold, R. B. 2015. A Real Options Approach to the Gas Cap Blowdown Decision. Presented at the SPE Annual Technical Conference and Exhibition, Houston, USA, 28–30 September. SPE-174868-MS. https://doi.org/10.2118/174868-MS.
Willigers, B. J. A., Begg, S. H., and Bratvold, R. B. 2011. Valuation of Swing Contracts by Least-Squares Monte Carlo Simulation. SPE Economics & Management 3 (4): 215–225. SPE-133044-PA. http://dx.doi.org/10.2118/133044-PA.
Wang, C., Li, G., Reynolds, A. C. 2009. Production Optimization in Closed-Loop Reservoir Management. SPE journal 14 (3): 506–523. SPE-109805-PA. https://doi.org/10.2118/109805-PA.