Developing a Fuzzy Logic Model to Predict Asphaltene Precipitation during Natural Depletion based on Experimental Data

Although the asphaltene problems have been studied for many years, there are numerous controversial issues around the nature of asphaltene. Since asphaltene precipitation imposes significant costs on oil industry, a comprehensive research must be done to address the issues about asphaltene structures. It is extremely important to investigate the behavior of asphaltene precipitation and ways to minimize it under changeable effective thermodynamic factors such as pressure, temperature, and composition. In this work, natural depletion tests were performed at three different temperatures of 200, 170, and 135 °F on Iranian heavy oil samples. At each step of the experiments, IP 143 standard test was used to measure the precipitated asphaltene. Then, a fuzzy logic model capable of predicting asphaltene precipitation in a range of temperatures was developed. The fuzzy logic model predicts experimental data accurately. The obtained results were finally compared with a solid model using the commercial software implementing the mentioned model, and it was concluded that there was good agreement between the fuzzy logic model and the simulation results.