Screening of enhanced oil recovery techniques for Iranian oil reservoirs using TOPSIS algorithm

In recent decades, parallel to amazing advances in the development of data mining methods, screening, as the first step of any enhanced oil recovery (EOR) project, has become an interesting subject of data mining methods. Screening of EOR methods is a multi-criteria decision making process, and the Multi-Criteria Decision Making (MCDM) method as a systematic statistical method, can be applied in this regard. In this paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as one of the methods under the MCDM category was used to screen 65 Iranian oil reservoirs. The screening method was employed for 10 different EOR techniques using a wide range of properties and conditions. The analysis used a database including more than 800 successful EOR projects across the world and for 9 ideal reservoir parameter values. The relative importance of the reservoir parameters was determined based on the Analytic Hierarchy Process (AHP) at nine importance levels. The findings showed that among the considered screening parameters, to determine the best EOR technique, lithology of the reservoir is the most influencing parameter. Additionally, almost 74% of the oil reservoirs under study, as a first priority, were eligible for CO2 injection, either miscible or immiscible. Thermal methods were in the second stage of ranking. The first and second candidate choice for onshore oil reservoirs was immiscible CO2 and hydrocarbon gas injection, respectively. For offshore reservoirs, CO2 injection and steam flooding were the best choices. Also, miscible N2 injection was the least important technique, due to the huge difference of considered reservoir pressure with minimum miscibility pressure (MMP) of N2 injection. The proposed technique is computationally fast and less expensive than field simulation studies for ranking EOR projects for any oil reservoir in the world.