Modeling Reservoir Connectivity and Tar Mat Using Gravity-Induced Asphaltene Compositional Grading

Abstract

Reservoir compartmentalization is one of the major issues on both on- and offshore reservoirs. High capital costs are involved especially in deepwater exploration and production, making it essential to assess prior to production the extent of compartmentalization within a reservoir. Within a continuous reservoir, fluid properties vary with depth because of compositional grading. Considerable fluid flow is required to attain thermodynamic equilibrium yielding compositional gradients, suggesting connectivity. In this work, an algorithm that makes use of the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state (EoS) is proposed to address the isothermal asphaltene compositional grading in a uniform gravitational field. The model is validated against well log and production data. The results are compared to field data to evaluate the reservoir compartmentalization. An approximate analytical solution for asphaltene compositional grading, derived from solution thermodynamics, is also presented. Asphaltene compositional grading under extreme cases can lead to tar-mat formation. The PC-SAFT asphaltene compositional grading introduced in this study is extended to model the possibility of a tar-mat formation because of gravitational segregation of asphaltene.