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A novel sulfonated alkyl ester (SAE) was developed. The sulfonated alkyl ester has unique chemical structures that aimed to combine the advantages of ester-based and sulfonate-based surfactant in one compound. This surfactant was studied for its performance to reduce interfacial tensions in a wide range of salinity with monovalent and divalent ions and in a light and heavy oil samples. The study showed that the sulfonated alkyl ester surfactant gives a good performance in a light oil sample in a high monovalent ion concentration and also give a good performance in a heavy oil sample in both low and high concentration of monovalent and divalent ions solution.

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Crude oil with a high wax content and high pour point can be very challenging when enhanced oil recovery by surfactant flooding is to be applied. High wax content in crude oil will lead to high intermolecular interaction because of the increasing cohesion forces. It causes interfacial (IFT) tension between oil and brine to be high. Hence, oil recovery is relatively low. This paper presents formulation of an amphoteric sulfonate alkyl ester (SAE) surfactant with a nonionic surfactant (ester group) to reduce oil-brine IFT in waxy oil of T-KS field, in Indonesia. The ion-dipole forces may occur between SAE surfactant and nonionic cosurfactant molecules. The forces cause sulfonate chain to be attracted to oil phase. The formulated surfactant produces low interfacial tension between brine and waxy oil of T-KS oil field. Its ability to displace remaining oil in the pore space was also tested using coreflood tests. These tests demonstrate considerably good incremental recovery.

Optimizing water injection rate distribution in waterflooding operations is a vital reservoir management aspect since water injection capacities may be constrained due to geographic location and facility limitations. Traditionally, numerical grid-based reservoir simulation is used for waterflood performance evaluation and prediction. However, the reservoir simulation approach can be time-consuming and expensive with the vast amount of wells data in mature fields. Capacitance Resistance Model (CRM) has been widely used recently as a data-driven physics-based model for rapid evaluation in waterflood projects. Even though CRM has a smaller computation load than numerical reservoir simulation, large mature fields containing hundreds of wells still pose a challenge for model calibration and optimization. In this study, we propose an alternative solution to improve CRM application in large-scale waterfloods that is particularly suitable for peripheral injection configuration. Our approach attempts to reduce CRM problem size by employing a clustering algorithm to automatically group producer wells with an irregular peripheral pattern. The selection of well groups considers well position and high throughput well (key well). We validate our solution through an application in a mature peripheral waterflood field case in South Sumatra. Based on the case study, we obtained up to 18.2 times increase in computation speed due to parameter reduction, with excellent history match accuracy.