Decision making and uncertainty quantification for surfactant-polymer flooding

Abstract

The aim of this thesis is to develop a robust parametric uncertainty quantification method and a decision making method for a chemical EOR process. The main motivation is that uncertainty is detrimental to the wide scale implementation of chemical EOR. Poor scale-up performance is not in line with the success in laboratory applications. Furthermore, economic uncertainty is also an important factor as low oil prices can deter EOR investment. As an example of chemical EOR we used Surfactant-polymer flooding due to its high potential and complexity. The approach was based on using Value of Flexibility evaluation in order to optimize the surfactant-polymer flooding in the presence of economic and technical uncertainty. This method was inspired by real options theory which provides a framework to value flexibility and captures the effect of uncertainty as the process evolves through time. By doing so, it provides the means to capitalize on the upside opportunities that these uncertainties present or to help mitigate worsening circumstances. In addition, it fulfils a secondary objective to develop a decision making process that combines both technical and economic uncertainty. The Least Squares Monte Carlo (LSM) method was chosen to value flexibility in surfactant-polymer flooding. The algorithm depends on two main components; the stochastic simulation of the input state variables and the dynamic programming approach that produce the optimal policy. The produced optimal policy represents the influence of uncertainty in the time series of the relevant input parameters. Different chemical related parameters were modelled stochastically such as surfactant and polymer adsorption rates and residual oil saturation. Static uncertainty in heterogeneity was incorporated using Gaussian and multiple-point statistics generated grids and dynamic uncertainty in heterogeneity was modelled using upscaling techniques. Economic uncertainties such as the oil price and surfactant and polymer cost were incorporated into the model as well. The results obtained for the initial case studies showed that the method produced higher value compared with static policy scenarios. It showed that by designing flexibility into the implementation of the surfactant-polymer flood, it is possible to create value in the presence of uncertainty. An attempt to enhance the performance of the LSM algorithm was introduced by using the probabilistic collocation method (PCM) to sample the distributions of the technical state input parameters more efficiently, requiring significantly less computational time compared to Monte Carlo sampling. The combined approach was then applied to more complex decisions to demonstrate its scalability. It was found that the LSM algorithm could value flexibility for surfactant-polymer flooding and that it introduces a new approach to highly uncertain problems. However, there are some limitations to the extendibility of the algorithm to more complex higher dimensional problems. The main limitation was observed when using a finer discretization of the decision space because it requires a significant increase in the number of stochastic realization for the results to converge, thus increasing the computational requirement significantly. The contributions of this thesis can be summarized into the following: an attempt to use real options theory to value flexibility in SP flooding processes, the development of an approximate dynamic programming approach to produce optimal policies, the robust quantification of parametric uncertainty for SP flooding using PCM and an attempt to improve the efficiency of the LSM method by coupling it with the PCM code in order to extend its applicability to more complex problems.