Enhancing ASP flooding by using special combinations of surfactants and starch nanoparticles

This study aimed to address the challenges faced by mature oilfields in extracting substantial oil quantities. It focused on improving the efficiency of alkaline–surfactant–polymer (ASP) flooding technique, which is a proven tertiary recovery technology, to overcome scaling issues and other hindrances in its large-scale implementation. Appropriate materials and their suitable concentrations were selected to enhance the ASP flooding technique. Special surfactants from Indonesia were introduced to improve the interfacial tension reduction and wettability alteration. Reservoir rock model that resembling Langgak oilfield in Sumatra was utilized, and low-salinity water was employed to mimic the oilfield conditions. Starches derived from cassava nanoparticles (CSNPs) and purple yam nanoparticles (PYNPs) were combined separately with conventional hydrolyzed polyacrylamide (HPAM) polymer to enhance its performance. Sodium hydroxide and sodium carbonate were used as alkaline in final ASP formula. It was demonstrated from this research that only two combinations of ASP formulations have led to improved oil recovery. One combination utilizing PYNPs resulted in 39.17% progressive recovery, while the other combination incorporating CSNPs achieved 35% incremental oil recovery. The ASP combination that resulted in recovery rate of 39.17% was composed of sodium hydroxide (NaOH) at a concentration of 1.28 wt.%, PSC EOR 2.2 (0.98 wt.%), and a combined polymer consisting of HPAM (0.2 wt.%) and PYNPs nano-starch (0.6 wt.%). The second combination led to 35% recovery rate and involved NaOH also at concentration 1.28 wt.%, PSC HOMF (0.63 wt.%), and a combined polymer comprising from HPAM (0.2 wt.%) and CSNPs nano-starch (0.8 wt.%). These findings of this study highlighted the potential of this modified ASP flooding to enhance oil recovery in mature oilfields, thereby offering valuable insights for oil industry

The prevention and prediction of corrosion using novel methods.

Corrosion causes huge economic losses worldwide with an annual direct cost estimated to be in the hundreds of billions of dollars. More than 30% of this corrosion is microbiologically influenced corrosion (MIC). In the first part of this thesis, novel experiments were conducted to prevent MIC in several industrial applications.The effect of adding specific natural oils to the oil-based coatings on their performance in sulfate reducing bacteria (SRB) and marine environments were evaluated. It was observed that the addition of optimum amounts of some natural oils to the oil-based coatings enhanced their performance and protection efficiency and increased their degradation resistance against the aggressive environments. These findings can lead to the development of new generations of environmentally-friendly oil-based coatings.The antimicrobial effects of selected natural products against Shewanella putrefaciens bacteria and SRB which are known to be associated with MIC were evaluated. It was observed that both the black thorn ( Acacia nilotica) and garlic (Allium sativum) possess bacteriostatic and bactericidal effects against these bacteria.The second part of this thesis was devoted to the modeling and simulation of MIC in different corrosive environments. Modeling of corrosion problems has the advantages of calculating and predicting the corrosion rates quickly, cheaply and accurately, mainly in situations where it is dangerous, very difficult or impossible to do that experimentally. To develop a novel and comprehensive MIC model, three modeling steps were adopted.The transient two-dimensional pitting MIC model was developed to study and estimate the pitting MIC of steel in SRB environments. The SRB cathodic depolarization theory was adopted as the SRB-influenced corrosion mechanism and was used in developing the model. The pitting MIC model was applied to marine, waste, and freshwater environments. The effects of substrate (sulfate) concentrations and SRB kinetic parameters on the shape, growth rate and depth of the corroded pits were also evaluated. The pitting MIC model was found to be very successful in estimating and predicting the pitting MIC in all the three studied environments. The model results were also found to be in a very good agreement with the corresponding experimental data found in the literature. (Abstract shortened by UMI.)Thesis (Ph.D.)--Dalhousie University (Canada), 2004