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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- 93 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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