Modeling of fluid imbibition and chemical tracer transport in porous media for oil recovery applications

Date

2023-08-11

Authors

Velasco Lozano, Moises

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Abstract

Modeling of fluid and solute transport in porous media is fundamental to describing driving mechanisms of recovery methods before their field application, however, conventional simulations and experiments demand time and expertise. Therefore, this research work presents novel real-time solutions for spontaneous imbibition (SI) and chemical tracer transport in porous media for two-phase flow. Although imbibition tests are critical to evaluating the displacement of oil by water and chemical solutions, the existing models fail to properly estimate the entire imbibition process. Therefore, a new semi-analytical solution for SI, valid during the infinite-acting and boundary-dominated regimes, was derived. The solution was validated with experimental data for different flow geometries under diverse flow conditions and capillary pressure functions, obtaining differences of less than 5%. Additionally, a numerical model is presented to examine SI in cores with a discrete fracture by including a new transfer function in the fracture equation to account for the fluid exchange at the matrix-fracture boundary. As a result, the flow model is reduced to a one-dimensional equation that is numerically solved using finite differences, leading to the accurate and rapid modeling of fluid displacement, obtaining results comparable to two-dimensional simulations. In addition, first-ever solutions are presented for the modeling of chemical tracer transport in two-phase flow in capillary- and advective-dominated systems at core scale, accounting for hydrodynamic dispersion, partitioning, and adsorption. These novel solutions are derived using Laplace transform and a series of transformation variables that simplify the highly nonlinear advection-dispersion equation, resulting in real-time analysis with simple mathematical expressions that do not require complex numerical calculations or inversion methods. Finally, a convolutional neural network is developed to estimate residual oil saturation based on the generation of partitioning tracer responses as a function of ideal tracer profiles, where the results obtained demonstrate that this machine learning method serves as a complementary tool to significantly reduce the number of reservoir simulations. Thus, the models described in this work are innovative approaches that facilitate the analysis of fluid and tracer dynamics at core and field scales for oil recovery and subsurface applications.

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