Abstract
Numerical simulation and experimental research of fluid flow in porous media enhance the practices of petroleum reservoirs' management. Experiments on acquired reservoir-rock samples are conducted for accurate characterization and realization of the insitu hydrocarbon reserves. Implementing precise numerical simulation of those experiments is crucial to acquire accurate conclusions from the obtained experimental results. Coreflooding experiments quantify reservoir rock's storage capacity, measure its transport connectivity, and evaluate recovery methods' effectiveness for that rock. In this paper, a reconstruction image-processing workflow of cores' CT scan is developed to build a finite difference numerical model for simulating coreflooding experiments. In a transient coreflooding experiment, a controlled pressure pulse with a known frequency and amplitude is transmitted to the rock sample. Rock permeability can be quantified by analytically solving the diffusivity flow equation for that experiment. Simulating the transient permeability experiment is very sensitive to the level of details of the pores' structure described in the numerical model. A transient permeability experiment with two different transient modes, sinusoidal oscillation, and pulse decay, was conducted on a standard Berea core sample. The Berea CT scan was image-processed to reconstruct the static porosity and permeability model in Petrel software using a 3D variogram geostatistical population. Injection and production sources were assigned to the finite gridblock, which correspond to flow nozzles of injection and production coreflooding setup's heads. Scheduled flow and controlled-pressure boundary-conditions were imposed on the dynamic model. Eclipse simulator was used to solve the dynamic model and calculate the pressure in each gridblock. The outlet pressure was calculated at each time step by three different realization approaches for porosity and permeability, i.e., from experiments, from statistical analysis for the CT scan, and by the proposed image processing workflow. The simulated outlet pressure from the prosed workflow matched ideally, with a Pearson correlation coefficient of 0.98 and 0.99, the recorded one in the two experiments compared to underestimated or overestimated outlet pressure from the other two traditional realization approaches, i.e., statistical or experimental.
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