On the Development of an Enhanced Method to Predict Asphaltene Precipitation

Abstract

Abstract Asphaltene precipitation and subsequent deposition is a potential problem in oil production because the significant costs for wellbore cleaning and the associated production loss. To better understand the mechanisms by which asphaltenes precipitate and deposit, in this work we present experimental evidence that supports the idea that precipitation and aggregation of asphaltenes is a multi-step process, where the former is driven primarily by thermodynamics whereas the latter is driven by kinetics. Under this multi-step mechanism, asphaltene precipitation is a fully reversible process. On the other hand, from the precipitated phase, subsequent aggregation and aging leads to the formation of more solid-like structures. Furthermore, we also present experimental results that suggest that the currently available commercial technologies to detect asphaltene precipitation (i.e. NIR spectroscopy and High Pressure Microscopy) might not be appropriate to detect the exact point of asphaltene precipitation, but instead they give a combined reading of precipitation plus aggregation. For this reason, the results obtained using these methods are very sensitive to the depressurization rates. The better understanding of the asphaltene behavior has enabled the development of an enhanced modeling approach based on the Perturbed Chain version of the Statistical Associating Fluid Theory equation of state (PC-SAFT EOS), which is used to predict the precipitation of asphaltenes at reservoir conditions and requires fewer simulation parameters than previous methods. A case study is presented in which our modeling technique was proven useful for the detection and correction of inconsistencies in experiments done using a bottom-hole sample at reservoir conditions.