Accelerated Surfactant Selection for EOR Using Computational Methods

Buijse, Marten Adriaan (Shell Exploration & Production) | Tandon, Kunj (Shell Technology Centre Bangalore) | Jain, Shekhar (Shell Technology Centre Bangalore) | Jain, Amit (Shell Technology Centre Bangalore) | Handgraaf, Jan-Willem (Culgi B.V.) | Fraaije, Johannes G. E. M. (Leiden University)

OnePetro 

Surfactant formulations are extensively being developed in the oil industry for Enhanced Oil Recovery (EOR) applications. Surfactants suitable for EOR will form an oil-brine microemulsion (µE) with ultra-low interfacial tension (IFT), necessary for
high recovery factors. Experimental screening of surfactants, to identify suitable formulations for reservoir conditions, is a laborious and time consuming process. In this paper we demonstrate an alternative, and novel, molecular modeling approach which is suitable for predicting µE properties and calculating optimum conditions. The molecular modeling simulations are based on the recently developed Method of Moments (MoM). The µE physics underlying the MoM is briefly reviewed in this
paper.

In the MoM the bending properties of the interfacial surfactant film are calculated as moments of the lateral stress profile. At optimum salinity the zeroth and first moments of the lateral stress profile are zero and the IFT will reach a minimum. In addition to optimum salinity, the bending rigidity (stiffness) of the surfactant film is another interesting microstructure property. The bending rigidity determines the oil/brine domain size, solubilization and magnitude of the IFT. The bending rigidity is accessible in the MoM via the saddle-splay modulus ks, which is calculated as the second moment of the lateral stress profile. It is shown in the paper how the shape of the lateral stress profile depends on molecular properties of the surfactant and on salinity.

MoM simulations were carried out using the coarse-grained Dissipative Particle Dynamics (DPD) method. This computational approach is highly scalable, while preserving the structural information of chemical components in the system. This makes the method useful while screening the wide design space of possible surfactant-oil-brine combinations. We will discuss the predictive technique and some validation examples of predicting optimum salinity for oil-brine micro-emulsions. We will then demonstrate the effect of surfactant structural parameters like chain length, cosolvent etc. on the optimum salinity of the microemulsions.