Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation
- Ali Alkhatib (Imperial College London) | Masoud Babaei (Imperial College London) | Peter R. King (Imperial College London)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- April 2013
- Document Type
- Journal Paper
- 721 - 735
- 2013. Society of Petroleum Engineers
- 7.2.3 Decision-making Processes, 25.4.7 Chemical flooding methods
- 5 in the last 30 days
- 500 since 2007
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This study introduces a decision-making evaluation method for flexibility in surfactant flooding. The method aims to capture the effects of uncertainty in the time series for both technical and economic parameters and produce a near optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the least-squares Monte Carlo (LSM) method, which is best-suited for evaluating flexibility in project implementation. The decision analyzed was that of finding the best time to start surfactant flooding during the lifetime of a field under uncertainty. The study was conducted on two reservoir models: a 3D homogeneous model and a 2D heterogeneous model. The technical uncertainties considered were the residual oil saturation (ROS) to the surfactant flood, surfactant adsorption, and reservoir heterogeneity, and the main economic uncertain parameters considered were oil price, surfactant cost, and water-injection and -production costs. The results show that the LSM method provides a decision-making tool that was able to capture the value of flexibility in surfactant-flooding implementation and provides some insight into the effect of uncertainty on decision making, which can help mitigate adverse circumstances should they arise or capture the upside potential if circumstances prove beneficial. The results found that the optimal policy obtained was reliable and that heterogeneity and different well-placement patterns affect the value of flexibility and optimal policy for different reservoir models. Furthermore, possible extensions to enhance the LSM method were discussed.
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