Neural-network-based model for predicting asphaltene stability in crude oils

Asphaltene deposition poses a major challenge in oilfield operations, leading to pipeline blockages, reduced reservoir permeability, and eventual declines in production output. When asphaltenes become destabilized in crude oil, they precipitate and form aggregates, leading to significant deposition. Therefore, monitoring the stability of asphaltenes in crude oil is crucial to prevent the exacerbation of these problems. The weight subfractions are a critical input for calculating any asphaltene stability index; however, obtaining them involves costly and time-consuming experimental procedures. In this research, a statistical machine-learning-based artificial neural network model is proposed to predict this critical parameter at a wide range of operational conditions, with satisfactory accuracy. An extended dataset of over 200 experimental data points was collected from the literature and used to train and test the proposed model. The results demonstrated that the proposed model performed very well versus several commonly used asphaltene prediction models: colloidal instability index, colloidal stability index, asphaltene-to-resins-ratio, and Stankiewics stability plot. This suggests that the proposed model can be a valuable tool for providing input to various asphaltene modeling tasks.