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Localized reactive flow in carbonate rocks: Core‐flood experiments and network simulations
Author(s) -
Wang Haoyue,
Bernabé Yves,
Mok Ulrich,
Evans Brian
Publication year - 2016
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1002/2016jb013304
Subject(s) - calcite , permeability (electromagnetism) , dissolution , geology , volumetric flow rate , materials science , mechanics , mineralogy , chemistry , membrane , physics , biochemistry
We conducted four core‐flood experiments on samples of a micritic, reef limestone from Abu Dhabi under conditions of constant flow rate. The pore fluid was water in equilibrium with CO 2 , which, because of its lowered pH, is chemically reactive with the limestone. Flow rates were between 0.03 and 0.1 mL/min. The difference between up and downstream pore pressures dropped to final values ≪1 MPa over periods of 3–18 h. Scanning electron microscope and microtomography imaging of the starting material showed that the limestone is mostly calcite and lacks connected macroporosity and that the prevailing pores are few microns large. During each experiment, a wormhole formed by localized dissolution, an observation consistent with the decreases in pressure head between the up and downstream reservoirs. Moreover, we numerically modeled the changes in permeability during the experiments. We devised a network approach that separated the pore space into competing subnetworks of pipes. Thus, the problem was framed as a competition of flow of the reactive fluid among the adversary subnetworks. The precondition for localization within certain time is that the leading subnetwork rapidly becomes more transmissible than its competitors. This novel model successfully simulated features of the shape of the wormhole as it grew from few to about 100 µm, matched the pressure history patterns, and yielded the correct order of magnitude of the breakthrough time. Finally, we systematically studied the impact of changing the statistical parameters of the subnetworks. Larger mean radius and spatial correlation of the leading subnetwork led to faster localization.