Development of decision support system for the selection of chemical additive to cure asphaltene deposition problem in oil industry

This research study aims to develop a decision support system for the selection of asphaltene-controlling chemical additives in crude oil. The decision support will incorporate all necessary criteria which include chemical efficiency, economic and HSE hazards. Therefore, in this study, a decision support system using the Fuzzy TOPSIS method was successfully developed and implemented for evaluating the performance ranking of four chemical additives used to control asphaltene stability in single crude oil. The four chemical additives which include DBSA, CDEA, salicylic acid and toluene were tested during the pre-asphaltene precipitation and post-asphaltene precipitation stages at three different concentrations, i.e., 2.5 wt%, 7.5 wt% and 15 wt%. Deposition level and onset measurement tests were conducted during the pre-asphaltene precipitation treatment process, while for the post-asphaltene precipitation treatment stage only deposition level test was conducted. Onset measurement tests were carried out to validate the results of pre-treatment deposition level tests. To record the deposition level precisely, all test tubes were calibrated and measurements of deposits in tubes were done using ImageJ software. The decision support system was applied to three major cases, namely case 1, case 2 and case 3, and each comprises some or all various criteria used in this study. The criteria taken in this study were the maximum efficiency achieved, concentration of chemical additive at which maximum efficiency was achieved, chemical HSE hazards and chemical cost. Each major case was run various times as its sub-cases by altering the weight importance of different criteria. The results indicate that the DBSA yields the highest accuracy of 100 percent at both pre- and post-treatment phases but at different concentrations. Moreover, DBSA also ranked first performance-wise when only maximum efficiency and dosage level were considered as criteria, i.e., in case 1. The CDEA secured second rank, while the third rank was shared by salicylic acid and toluene. However, when all four criteria were taken into account, CDEA was found to be the first rank chemical during pre-asphaltene precipitation treatment (case 2) in all scenarios of changing weight importance of criteria. DBSA was found to be the second rank chemical followed by salicylic acid and toluene. The comparatively better performance of CDEA over DBSA was due to the reason of its low cost and higher efficiency achievement at a minimum concentration of chemicals used. During the post-asphaltene precipitation treatment stage (case 3), the ranks of chemicals were changed significantly as the weight importance of criteria was changed concerning each other. In this case, CDEA was found to be the best chemical additive in most scenarios followed by DBSA and salicylic acid. Relatively, most of the chemical additive performance was affected adversely during the post-treatment stage as compared to the pre-treatment stage. This finding depicts that the selection of chemical additives for the post-treatment process was more challenging and complex as compared to the pre-treatment stage. The development of a decision support system will certainly prove to be a major advancement and tool toward offering a powerful and reliable integrated framework for the selection of chemical additives to control asphaltene instability in crude oils. Moreover, the decision support system is unique and flexible and can easily incorporate various important factors as criteria in future studies necessary to judge chemical additive performance to be utilized under varying conditions.