CO2 injection is a well-known Enhanced Oil Recovery (EOR) technique that has been used for years to improve oil extraction from carbonate rock and other oil reservoirs. Optimal functioning of CO2 injection requires a thorough understanding of how this method affects the petrophysical properties of the rocks. We evaluated pore-scale changes in these properties, notably porosity and absolute permeability, following injection of CO2-saturated water in two coquina outcrop samples from the Morro do Chaves Formation in Brazil. The coquinas are close analogues of Presalt oil reservoirs off the coast of southern Brazil. The effects of carbonated water injection were evaluated using a series of experimental and numerical steps before and after coreflooding: cleaning, basic petrophysics, microtomography (microCT) imaging, nuclear magnetic resonance (NMR) analyses, and pore network modeling (PNM). Our study was motivated by an earlier experiment which did not show the development of a wormhole in the center of the sample, with a concomitant increase in permeability of the coquina as often noted in the literature. We instead observed a substantial decrease in the absolute permeability (between 71 and 77%), but with little effect on the porosity and no wormhole formation. While all tests were carried out on both samples, here we present a comprehensive analysis for one of the samples to illustrate changes at the pore network level. Different techniques were used for the pore-scale analyses, including pore network modeling using PoreStudio, and software developed by the authors to enable a statistical analysis of the pore network. Results provided much insight in how injected carbonated water affects the pore network of carbonate rocks.
Trapped or residual air (or gas) is known to affect the multiphase hydraulic properties of both soils and rocks. Trapped air is known to impact many vadose zone hydrologic applications such as infiltration and flow in the capillary fringe, but is also a major issue affecting recoverable oil reserves. Although many studies have focused on the relationship between porosity and trapped gas saturation (Sgt) in sandstones, far fewer studies have been carried out for carbonate rocks. This work aims to analyze the influence of porous media properties on trapped gas saturation in carbonate rocks. For this we used thirteen Indiana Limestone and Silurian dolomite rock samples from the USA, and several coquinas from the Morro do Chaves formation in Brazil. Pore size distributions were obtained for all samples using Nuclear Magnetic Resonance (NMR), and Mercury Injection Capillary Pressure (MICP) data from three of the samples to determine their pore throat size distributions. Additionally, 3D microtomography (microCT) images were used to quantify macropore profiles and pore connectivities. Results indicate a lower capacity of gas trapping in carbonate rocks in which micro- and mesopores predominate. Results also indicate that in carbonate rocks, pore size exerts a greater influence on the ability of gas trapping compared to pore connectivity, so that rocks with a predominance of macropores have greater capacity for gas trapping, even when the macropores are well interconnected. These findings show that pore characteristics very much affect the processes governing gas trapping in carbonate rocks, and indirectly the multiphase hydraulic properties and recoverable oil reserves of carbonate rock reservoirs.
Uniaxial compressive strength (UCS) represents one of the key mechanical properties used to characterize rocks along with the other important properties of porosity and density. While several studies have proved the accuracy of artificial intelligence in modeling UCS, some authors believe that the use of artificial intelligence is not practical in predicting. The present paper highlights the ability of an artificial neural network (ANN) as an accurate and revolutionary method with regression models, as a conventional statistical analysis, to predict UCS within carbonate rocks and mortar. Thus, ANN and multiple linear regressions (MLR) were applied to estimate the UCS values of the tested samples. For experimentation we carried out ultrasonic measurements on cubic samples before testing uniaxial compressive strength perpendicularly to the stress direction. The models were performed to correlate effective porosity, density and ultrasonic velocity to the UCS measurements. The resulting models would allow the prediction of carbonate rocks and mortar’s UCS values usually determined by laborious experiments. Although the results demonstrate the usefulness of the MLP method as a simple, practical and economical model, the ANN model is more accurate.