###### Abstract  
Accurately measuring wettability is of the utmost importance because it influences several reservoir parameters while also impacting reservoir potential, recovery, development, and management plan. As such, this study proposes a new formulated mathematical model based on the correlation between the Amatt-USBM wettability measurement and field NMR T\({}_{2}\)LM log. The exponential relationship based on the existence of immiscible fluids in the pore space had a correlation coefficient of 0.95. Earlier studies on laboratory core wettability measurements using \(T_{2}\) distribution as a function of increasing water saturation were modified to include T\({}_{2}\)LM field data. Based on the trends observed, water-wet and oil-wet conditions were qualitatively identified. Using the mean T\({}_{2}\)LM for the intervals of interest and the formulated mathematical formula, the various wetting conditions in existence were quantitatively measured. Results of this agreed with the various core wettability measurements used to develop the mathematical equation. The results expressed the validity of the mathematical equation to characterise wettability at the field scale. With the cost of running NMR logs not favourable, and hence not always run, a deep ensemble super learner was employed to establish a relationship between NMR T\({}_{2}\)LM and wireline logs. This model is based on the architecture of a deep learning model and the theoretical background of ensemble models due to their reported superiority. The super learner was developed using nine ensemble models as base learners. The performance of nine ensemble models was compared to the deep ensemble super learner. Based on the RMSE, R\({}^{2}\), MAE, MAPD and MPD the deep ensemble super learner greatly outperformed the base learners. This indicates that the deep ensemble super learner can be used to predict NMR T\({}_{2}\)LM in the field. By applying the methodology and mathematical formula proposed in this study, the wettability of reservoirs can be accurately characterised as illustrated in the field deployment. surface wettability; empirical formula; NMR characterisation; artificial intelligence; water saturation; deep neural network +
Footnote †: journal: _Sci._  
Footnote †: journal: _Sci. pressure, relative permeability, effective porosity, electrical properties, simulated tertiary oil recovery, and residual oil saturation [2; 3]. Wettability is also an essential property upon which reservoir potential, recovery, subsequent plans of development, and reservoir management depend [4; 5]. However, many different methods have been developed to quantify the wettability of a system. They include quantitative methods--contact angles, imbibition, and forced displacement (Amott), United States Bureau of Mines (USBM) wettability method, and Nuclear Magnetic Resonance (NMR). The unique criteria in determining the degree of water or oil wetness are shown in Table 1 below. The qualitative methods include imbibition rates, microscope examination, flotation, glass slide method, relative permeability curves, permeability/saturation relationships, permeability curves, permeability/saturation relationships, capillary pressure curves, displacement capillary pressure, reservoir logs, nuclear magnetic resonance, and dye adsorption [5]. Although no single accepted method exists, three industry-standard quantitative methods are generally used: contact-angle measurement, the Amott method (imbibition and forced displacement), and the USBM method. For elementary and accurate measurements, the contact angle method can quantitatively determine the fundamental wetting condition of a specific flat solid surface. However, aside from ageing and preparing samples, uncertainty in measurements arises due to surface roughness and geometrical imperfection. However, this method is not reliable and applicable in complex geometries, heterogeneous reservoirs, and measuring the wettability of multiphase fluid [5].