# Data-Driven Analyses of Low Salinity Waterflooding in Carbonates
## 1 Introduction  
Estimates show that approximately 60% of the world's oil reserves are held in carbonate reservoirs [1]. The amount of oil that can be produced from these reservoirs by natural production is below 30%. This small value of oil recovery is due to the heterogeneity, low matrix permeability, presence of fractures, and oil-wet conditions in carbonates. Hence, enhanced oil recovery (EOR) methods are required to reduce the residual oil and increase oil production. Low salinity water (LSW) flooding is one of the promising techniques for EOR in carbonate formations. It is a process of injecting low saline water with an optimized ion composition into the reservoir in order to recover incremental oil [2]. A survey of the literature shows that based on the type of crude oil and the properties of the reservoir and injection/formation brines, several mechanisms are proposed to explain the performance of LSW flooding. Multicomponent ionic exchange (MIE) [3, 4, 5], reduction in interfacial tension [6, 7], expansion of the electric double layer [8, 9, 10, 11, 12], and rock dissolution [13, 14, 15, 16, 17], micro-dispersion formation [18, 19] are the main mechanisms suggested by researchers to explain the incremental oil recovery by LSW in carbonates. Most of these mechanisms result in the alteration of wettability in carbonate rock, which is the most desirable and widely accepted reason for improving oil recovery byLSW. It is believed that some rock/fluid and fluid/fluid properties control oil recovery improvement using LSW, which should be considered to achieve a successful outcome. Most carbonates are observed to be neutral or oil-wet [20]. This wettability can be related to the retention of the carboxylic group, with negative charging of heavy oil compounds on the positively charged rock surface. Injection of LSW with specific ions and interaction between the injected active ions, called potential determining ions (PDIs), and rock surfaces may alter the initial wettability, resulting in oil detachment and incremental oil recovery. PDIs are primarily sulfate, calcium, and magnesium (SO4\({}^{2-}\), Ca\({}^{2+}\), Mg\({}^{2+}\)) ions that interact with the carbonate surface. Hence, the amount of active ions in the injected brine and the porous media are essential for alteration in fluid/rock interactions and the LSW performance [21; 22; 23; 24; 25]. The concentrations of inactive ions, such as Na\({}^{+}\) and Cl\({}^{-}\), are also critical in influencing different mechanisms [26; 27]. The acidic number (AN) is defined as the amount of KOH (in mg) required to neutralize 1 g of oil [28]. AN is another controlling parameter during LSW injection, as it determines the amount of carboxylic group in the crude oil, which has a major influence on the carbonate wettability. The effect of base number (BN), which is the quantity of the basic components in oil, is less than the effect of AN [29]. The importance of temperature during LSW flooding in limestones was investigated in different studies [21; 30; 31]. Measurements from several studies have identified that the wettability alteration in carbonates also involves changing the effluent pH during LSW injection [32; 33]. Hence, AN, temperature, and pH are additional parameters affecting the success of LSW flooding in carbonate formations. A closer look at the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is challenging to understand the relationship between various parameters and the low salinity effect in carbonates. The mechanisms involved in increasing oil recovery are still not clear and more analyses are required. The data available from the literature can be analyzed, using data analysis methods, to predict the performance of LSW in carbonates and study the active mechanisms of oil displacement. By data analysis, it is possible to develop linear and nonlinear relationships between the variables and the recovery factor. Machine learning (ML) can be applied as a powerful tool to develop these models.