A New Screening Workflow for Water-Based EOR Techniques Using Sector Model Simulation and Optimization

Screening of water-based Enhanced Oil Recovery (EOR) techniques is a preliminary task in designing an EOR technique for an oil reservoir. Commonly, a screening table is looked up either manually or automatically to screen out unfitted EOR techniques for a specific reservoir, based on a worldwide database of successful EOR projects. This approach disregards the physics of the EOR processes within the reservoir and does not consider the optimal values of decision variables such as well control parameters during the screening and ranking of EOR techniques. In this study, we address these challenges by presenting a workflow for screening water-based EOR techniques. This includes surfactant flooding, polymer flooding, low-salinity water flooding, and Alkaline-Surfactant-Polymer (ASP), flooding in an oil reservoir. To conduct this, several representative sectors are extracted from the oil reservoir’s dynamic model. In the first step, an automated screening based on the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) algorithm determines the ranking of EOR techniques and similar EOR projects. In the next step, each water-based EOR project is simulated on each reservoir sector to rank the EOR techniques using the Recovery Factor (RF). Finally, stochastic optimizations of decision variables in each EOR technique in each reservoir sector update the ranking and screening. The final rankings from these three steps are averaged across all sectors to list the suitable EOR techniques and find their relative technical performance. The differences in the ranking list using the table-based screening, flow simulation, and optimization highlight the benefits of the proposed workflow.

Keywords:

EOR screening , Decision making , Sector Simulation , EOR Optimization