The effect of injection brine salinity on the displacement efficiency of low water salinity flooding was investigated using sea water at 35,000 ppm, and two field injection waters, namely, Um-Eradhuma (UER) at 171,585 ppm and simsima (SIM) at 243,155 ppm. The salinity of the employed waters was varied from original salinity to 1,000 ppm and used in the displacement of oil in selected core samples. The results of this set of experiments revealed that UER salinity of 5,000 ppm is the optimum system for the candidate reservoir. UER original water and its optimum water were then used in this project as the high and low salinity waters in the CO₂–WAG flooding experiments. Displacement efficiencies were evaluated under three injection modes: carbon dioxide WAG miscible flooding (CO₂–WAG, 1:1, 2:1, and 1:2), continuous CO₂ injection, and waterflood. The WAG performance parameters, such as secondary and tertiary displacement efficiencies, CO₂ flood utilization factor, and CO₂ performance during different WAG flood cycles were determined. To insure miscibility condition between the injected gas and the employed oil, all of the flooding experiments were conducted at 3,200 psia (which is 300 psia above the minimum miscibility pressure of CO₂ and used oil) and 250 °F. Experimental results indicated that core length is a critical parameter in determining the optimum WAG process, and that a minimum core length of 29 cm is required to insure the generation of miscibility before breakthrough in CO2–WAG flooding experiments. On the other hand, core length had no effect on the performance of the low salinity flooding experiments. Using single core flooding low salinity CO₂–WAG of 1:2 flooding produced an improvement in the displacement efficiency of 29 % over the high salinity system. Also, composite core flooding experiments showed that the high salinity CO₂-2:1 WAG achieved a displacement efficiency of 98 %. These results indicate that achieving miscibility at the reservoir conditions is the dominant mechanism and that low salinity will have no major effect on the displacement efficiency of CO₂-Miscible WAG flooding. Results also indicate that oil recovery during different CO2–WAG cycles is a function of WAG ratios.

Automated image processing algorithms can improve the quality and speed of classifying the morphology of heterogeneous carbonate rock. Several commercial products have worked to produce petrophysical properties from 2D images and with less extent from 3D images, relying on image processing and flow simulation. Images are mainly micro-computed tomography (μCT), optical images of thin-section, or magnetic resonance images (MRI). However, most of the successful work is from the homogeneous and clastic rocks. In this work, we have demonstrated a Machine Learning assisted Image Recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images form μCT and MRI. Our research method consists of two parts: experimental and MLIR. Experimentally, we measured porosity of rock core plug with three different ways: (i) weight difference of dry and saturated rock, (ii) NMR T₂ relaxation of saturated rock, and (iii) helium gas injection of rock after cleaning and drying. We performed MLIR on 3D μCT and MRI images using random forest machine-learning algorithm. Petrophysicist provided a set of training data with classes (i.e., limestone, pyrite, and pore) as expert knowledge of μCT Image intensity correspondence to petrophysical properties. MLIR performed, alone, each task for identifying different lithology types and porosity. Determined volumes have been checked and confirmed with three different experimental datasets. The measured porosity, from three experiment-based approaches, is very close. Similarly, the MLR measured porosity produced excellent results comparatively with three experimental measurements, with an accuracy of 97.1% on the training set and 94.4% on blind test prediction.

Rock surface wettability is one of the most important factors impacting the oil recovery efficiency. Carbonate reservoir rocks show highly heterogeneous wettability, which is imposing big challenges in Enhanced Oil Recovery. Because of the complicated porous morphology and interaction between oil/water phases, the mechanism of how surface wettability affects the relative permeability is still unclear. In this work, we develop a Lattice Boltzmann method (LBM) to study the oil-water-rock interaction through systematic water-flooding simulations. Our LBM model is validated by the analytical solution of co-current oil-water flow and lab-scale microchannel oil displacement experiment. To further investigate the effect of rock surface wettability and reveal the pore-scale oil trapping mechanism, a series of water-flooding oil displacement simulations are conducted under different initial oil saturation and surface wettability conditions. For realistic pore-scale flow visualization, a 2D micro-CT image of carbonate rock in Abu Dhabi is used. Our simulation results indicate that the surface wettability has significant influence on the volume of trapped oil at the end of the recovery process; the trapped oil residue in an oil-wet pore could be 2.5 times more than that in a water-wet pore. This study is an important step toward understanding the complicated oil trapping mechanism in heterogeneous carbonate reservoirs with variable wettability conditions.

CO₂-enhanced oil recovery (CO₂-EOR) has gained prominence as an effective oil displacement method with low carbon emissions, yet its microscopic mechanisms remain incompletely understood. This study introduces a novel high-pressure microfluidic visualization system capable of operating at 0.1–10 MPa without confining pressure and featuring stratified porous media with a 63 μm minimum throat size to provide unprecedented insights into CO₂ and CO₂-foam EOR processes at the microscale. Through quantitative image analysis and advanced machine learning modeling, we reveal that increasing the CO₂ injection pressure nonlinearly reduces residual oil saturation, achieving near-complete miscibility at 6 MPa with only 2% residual oil—a finding that challenges conventional thresholds for miscibility in heterogeneous systems. Our work uniquely demonstrates that CO₂-foam flooding not only mobilizes capillary-trapped oil films but also dynamically alters interfacial tension and the pore-scale fluid distribution, a phenomenon previously underexplored. Support Vector Regression (R² = 0.71) further uncovers a nonlinear relationship between the surfactant concentration and residual oil saturation, offering a data-driven framework for parameter optimization. These results advance our fundamental understanding by bridging microscale dynamics with field-applicable insights, while the integration of machine learning with microfluidics represents a methodological leap for EOR research.

In one of the biggest carbonate reservoir in offshore Abu Dhabi that has been producing oil for more than 40 years, oil potential evaluation is affected by many uncertainties as evaluated via pulsed neutron technology across perforated intervals. The Acid used to stimulate the wells after workovers affect the responses of the pulsed neutron logs. Efforts have been exerted to eliminate this effect. Observations from this field showed that the acid life cycle in the formation can be extracted by comparing and correlating several pulsed neutron sigma logs that are acquired over the years across the same perforated intervals. It has been noticeable that one of the most influential parameters in removing or eliminating acid existance in reservoir rock is water production, otherwise acid effect on pulse neutron sigma log interpretation remains, resulting in calculating high water saturation across dry oil producing zones. Understanding this acid effect life cycle helped understanding reservoir performance and reducing uncertainty of pulse neutron sigma interpretation. This paper is showing these observations in our field and is detailing acid effect modeling on pulsed neutron log responses.