Graphical abstract Highlights • This study introduces an experimental and modeling approach to obtain asphaltene precipitation. • The new technique is more accurate for light to heavy oils, compared to previous models. • The new approach considers vital oil and process characteristics in asphaltene precipitation. • A sensitivity analysis is conducted to make better operating and policy decisions while dealing with asphaltene precipitation. • The most influencing parameters in asphaltene precipitation are pressure and dilution ratio. Abstract Asphaltenes are heavy fractions of petroleum mixtures, which precipitate out of homogenous bulk fluid phase due to changes in pressure, temperature, and composition. Asphaltene precipitation and subsequent deposition in porous media can adversely affect Enhanced Oil Recovery (EOR) processes as well as impede surface transportation of crude oil. It is therefore beneficial to develop accurate techniques for determination of asphaltene precipitation as well as reliable predictive modeling approaches to remedy potential production impairments. In this study, a laboratory work is performed using three different oil samples (light, medium-viscosity, and heavy crude oil) to measure the mass of precipitated asphaltenes in a series of titration experiments. The effects of various parameters such as precipitant type, pressure, dilution ratio, API gravity of oil, and resin-to-asphaltene ratio of crude oil on the amount of precipitated asphaltenes are studied. A deterministic tool, so-called Response Surface Methodology (RSM), is then proposed to estimate the mass of asphaltenes precipitated from these three oil types in a titration process as a function of input parameters selected through a systematic parametric sensitivity analysis. The Analysis of Variance (ANOVA) technique is also employed to assess the validity of the proposed predictive methodology. Among all the contributing parameters, pressure and dilution ratio are found to be the most influencing factors when predicting the mass of precipitated asphaltenes. The developed simple predictive tool appears to be an effective methodology to forecast the mass of precipitated asphaltenes over a wide range of input parameters for various oil types. [ABSTRACT FROM AUTHOR]