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 Amott-USBM wettability measurement and field NMR T2LM 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 T2 distribution as a function of increasing water saturation were modified to include T2LM field data. Based on the trends observed, water-wet and oil-wet conditions were qualitatively identified. Using the mean T2LM 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 T2LM 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, R2, 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 T2LM 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.
Highlights
Wettability can be defined as the intermolecular interaction between immiscible fluids and a rock’s pore space [1]
Being able to quantify the surface wettability in an early stage is vital for every reservoir
The Nuclear Magnetic Resonance (NMR) T2LM, when analysed against water saturation data, can indicate the type of wettability condition existing in a reservoir
Summary
Wettability can be defined as the intermolecular interaction between immiscible fluids and a rock’s pore space [1]. To decide about the type of EOR technique to employ to achieve maximum reservoir performance, detailed understanding, and prediction of surface wettability and the parameters that affect it is crucial. This statement means that parameters such as mineral composition, morphology, pore structures, surface roughness, salinity, and surface contaminations must be quantified at the lab and field scales. These parameters are of interest because a wrongly assumed parameter can have severe permanent damage to the reservoir and subsequently affect production, water flooding and EOR techniques [16–18].
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