Produced water (PW) generated by Australian offshore oil and gas activities is typically discharged to the ocean after treatment. These complex mixtures of organic and inorganic compounds can pose significant environmental risk to receiving waters if not managed appropriately. Oil and gas operators in Australia are required to demonstrate that environmental impacts of their activity are managed to levels that are as low as reasonably practicable (ALARP), e.g., through risk assessments comparing predicted no-effect concentrations (PNECs) with predicted environmental concentrations (PECs) of PW. Probabilistic species sensitivity distribution (SSD) approaches are increasingly being used to derive PW PNECs and subsequently to calculate dilutions of PW (termed “safe” dilutions) required to protect a nominated percentage of species in the receiving environment (e.g., 95% or 99% (PC95 and PC99), respectively). Limitations associated with SSDs include fitting a single model to small (6-8 species) datasets, resulting in large uncertainty (very wide 95% confidence limits) in the region associated with PC99 and PC95 values. Recent advances in SSD methodology, in the form of model-averaging, claim to overcome some of these limitations by applying the average model fit of multiple models to a dataset. We assessed the advantages and limitations of four different SSD software packages (Burrlioz, shinyssdtools, SSD Toolbox and MOSAIC) with graphical user interfaces (GUIs) for determining PNEC values for five PWs from a gas/condensate platform off the North-West Shelf of Australia. We found that model-averaging reduced occurrences of extreme uncertainty around PC95 and PC99 values compared to single model fitting and was less prone to derivation of overly conservative PC99 and PC95 values that resulted from lack of fit to single models. Our results support the use of model-averaging for improved robustness in deriving PNECs and subsequent “safe” dilution values for PW discharge management and risk assessment.