In this study, enhanced oil recovery (EOR) techniques—namely low salinity and nanofluid EOR—are probed at the nanometer-scale using an atomic force microscope (AFM). Mica substrates were used as model clay-rich rocks while AFM tips were coated to present alkyl (-CH₃), aromatic (-C₆H₅) and carboxylic acid (-COOH) functional groups, to simulate oil media. We prepared brine formulations to test brine dilution and cation bridging effects while selected concentrations (0 to 1 wt%) of hydrophilic SiO₂ nanoparticles dispersed in 1 wt% NaCl were used as nanofluids. Samples were immersed in fluid cells and chemical force mapping was used to measure the adhesion force between polar/non-polar moieties to substrates. Adhesion work was evaluated based on force-displacement curves and compared with theories. Results from AFM studies indicate that low salinity waters and nanoparticle dispersions promote nanoscale wettability alteration by significantly reducing three-phase adhesion force and the reversible thermodynamic work of adhesion, also known as adhesion energy. The maximum reduction in adhesion energy obtained in experiments was in excellent agreement with existing theories. Electrostatic repulsion and reduced non-electrostatic adhesion are prominent surface forces common to both low salinity and nanofluid EOR. Structural forces are complex in nature and may not always decrease total adhesion force and energy at high nanoparticle concentration. Wettability effects also depend on surface chemical groups and the presence of divalent Mg²⁺ and Ca²⁺ cations. This study provides fresh insights and fundamental information about low salinity and nanofluid EOR while demonstrating the application of force-distance spectroscopy in investigating EOR techniques.

Shale oil reservoirs are prolific on the short term due to hydraulic fracturing and horizontal drilling but experience significant production decline, leading to poor ultimate recovery and leaving billions of barrels of oil buried in the ground. In this study, a systematic multi-scale investigation of an enhanced oil recovery (EOR) process using relatively inexpensive silicon dioxide nanoparticles and carbon dioxide for shale oil reservoirs was conducted. Using the Tuscaloosa Marine Shale (TMS) as a case study, aqueous dispersions of nanosilica in conjunction with CO2 were investigated at nano-to-core scales. At the nanoscale, atomic force microscope was used to investigate the wettability modification performance of silica nanoparticles by measuring adhesion force between specific functional groups and pure minerals in nanofluid media. At the micron-scale, the roles of silica-based nanofluids in fluid/fluid interactions and rock/fluid interactions were distinguished by characterizing interfacial tension and advancing contact angle using optical tensiometer and the dual-drop-dual-crystal technique, respectively. Core-scale investigations consisted of: high-pressure CO2 EOR coreflood experiment, reservoir rock/fluid characterization, physics-based modelling of capillary pressure and relative permeability using nano-to-core scale experimental data, and compositional simulation. Results showed that hydrophilic silica nanoparticle (HNP) dispersions can effectively improve nanoscale wettability alteration (towards less oil-wet state) by decreasing adhesion force and work required to spontaneously desorb dominant functional groups in TMS crude oil from pure mineral surfaces. However, the grafting of aminosilanes on the surfaces of nanosilica generally increased adhesion force. At the micron-scale, HNP solutions showed great potential for enhancing oil recovery in TMS through wettability modification but not interfacial tension xviii reduction, whereas APTES-modified nanoparticle dispersions showed promising EOR potential through both mechanisms. At the core scale, coreflood experiment and compositional simulation showed that up to 30% of oil-in-place can be recovered with CO2 EOR in TMS. The nano-to-micron scale mechanisms of silica-based nanofluids translated into a notable decrease in capillary pressure, an increase in oil relative permeability and a decrease in water relative permeability. However, the strongly-water state in TMS masked the synergistic effects of nanoparticle-assisted CO2 EOR and thus helped revealed the initial wetting state as an important EOR screening criterion for shale oil reservoirs.

The objective of this work is to demonstrate the application of multivariate statistical tools in unconventional reservoir fluid characterization using publicly available PVT data and design a fluid characterization workflow. Several oil and gas properties were predicted for fluids in Eagle Ford groups, namely Eagle ford 1 (ef1), Eagle Ford 2 (ef2), Eagle Ford Shale (efs) and Eagle Ford (ef). Principal component analyses (PCA) was first used for dimensionality reduction and selection of components that account for large variation of the dataset. Next, multivariate regression analyses were used to predict PVT properties as a function of reservoir fluid composition using gathered reports. Major steps in regression are trends observation, correlation of trend parameters with composition, model reconstruction and calibration. PCA results show that virtually all original variables are required to account for substantial variation within the PVT datasets. It is also noticed that the first 3 principal components account for 84% of dataset variability. Regression models of different fluid properties including gas-oil ratio (GOR), oil formation volume factor, gas density, gas viscosity, API, saturation pressure developed for Eagle Ford Shale formation fluids demonstrate a superior level of accuracy on average (R sq. = 0.8-0.94) and it was revealed that most properties can be determined directly from mole percentage of hydrocarbon fractions. A strong correlation between initial reservoir pressure and formation true vertical depth (TVD) was identified from the PVT data. Consequently, oil formation volume factor and GOR maps covering areas where analysed data were collected from with R sq. above 0.8 were designed. A user-friendly reservoir fluid characterization workflow was proposed and can be applied to any type of reservoir fluid window- black oil, dry gas or condensate - in a liquid-rich basin. Exceptions were identified and discussed. Reliable estimation of reservoir fluid properties has a tremendous impact on different facets of unconventional reservoir field development: drilling, completion, reservoir management, and economic analyses. Current understanding of shale PVT is inadequate due to difficulty in obtaining representative fluid samples before (low permeability) and after (large produced water volumes) hydraulic fracturing. Thus, this study builds on recent efforts in understanding shale PVT behavior and potentially eliminating need for rigorous fluid sampling using multivariate statistical methods.