Decision Making under Uncertainty in EOR - Applying the Least Squares MonteCarlo Method in Chemical EOR Implementation (SPE 154467)

This study builds on the previous work of the author in adapting the LSM (Least Squares MonteCarlo) method to an EOR context in order to value flexibility in the implementation of a surfactant flood and to produce the optimal policy. The LSM algorithm was developed in MATLAB and SchlumbergerC"s Eclipse was used as the reservoir simulator. The main focus of the algorithm was to consider the uncertainty in parameters that vary in time. The technical uncertainties considered were the residual oil saturation to the surfactant flood and surfactant adsorption while the main economic uncertain parameters considered were oil price, surfactant cost and water injection and production costs. The study is divided into two main sections: the first section considered the uncertainty in technical and economic parameters using a homogenous synthetic model, the second section considered the effects of the uncertainty of these parameters on a heterogeneous model based on permeability data from SPE Comparative Solution Project, Model 2. In this case, heterogeneity is considered as another uncertain parameter by having the heterogeneous permeability field realizations vary with time. This is achieved by using upscaled models and progressively replacing them with finer models. This procedure represents the increasing state of knowledge in terms of our understanding of the reservoir with time. 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. It also demonstrated that it is economical to implement surfactant flooding under uncertainty compared with the evaluations of traditional methods which produce uneconomical outlooks under the same conditions. Another advantage of this method is that it considers the value of information during the life of an EOR project which might undergo an alteration of the implementation strategies at different decision times.