Experimental investigation of the sequence injection effect of sea water and smart water into an offshore carbonate reservoir for enhanced oil recovery.

This study explores enhanced oil recovery (EOR) strategies, with a focus on carbonate reservoirs constituting over 60% of global oil discoveries. While "smart water" injection proves effective in EOR for carbonate reservoirs, offshore application challenges arise due to impractical volumes for injection. To address this, we propose a novel continuous injection approach, systematically investigating it on a laboratory scale using the Iranian offshore reservoir, Sivand. Thirty-six contact angle tests and twelve flooding experiments are meticulously conducted, with key ions, potassium, and sulfate, playing pivotal roles. Optimal wettability alteration is observed at 4 times potassium ion concentration in 0-2 times sulfate concentrations, driven by ionic strength and charge interactions. Conversely, at 3-5 times sulfate concentrations, the optimal contact angle shifts to 2 times potassium ion concentration, suggesting a mechanism change linked to increasing sulfate ion ionicity. A significant wettability alteration, evidenced by a 132.8° decrease, occurs in seawater with a twofold concentration of potassium ions and a fivefold concentration of sulfate ions. Micromodel experiments introduce an innovative alternation of smart water and seawater injections. The first scenario, smart water followed by seawater injection, reveals negligible post-seawater injection oil recovery changes. In contrast, the second scenario yields a maximum recovery of 7.9%. The first scenario, however, boasts superior overall sweep efficacy, reaching approximately 43%. This research expands understanding of smart water and seawater injection in EOR, presenting a viable solution for optimizing offshore carbonate reservoir recovery. The insights contribute to evolving EOR methodologies, emphasizing tailored strategies for varying reservoir conditions.

Transport and retention of silica nanoparticles in glass-bead columns: effects of particle size, type, and concentration of ionic species.

Silica nanoparticles (SiO2 NPs) have garnered substantial attention as versatile additives in saline fluids, finding application in areas like environmental remediation, wastewater treatment, enhanced oil recovery, and carbon geo-sequestration. Despite their potential, the intricate interaction between electrolyzed nanoparticles and porous media remains inadequately researched in these contexts. This study delves into the pivotal yet underexplored aspect of silica nanoparticle absorption behavior within porous media, a key determinant of their practical effectiveness. The research focuses on silica particles with dimensions of 10 nm and 50 nm, synthesized via hydrolysis and condensation of tetraethyl orthosilicate (TEOS) in methanol. Employing packed glass bead columns as a surrogate for porous media, the study unravels the complex mechanisms governing nanoparticle transport and deposition. Comprehensive investigations encompass variations in particle sizes, ionic strength, and ionic species, resulting in the examination of 48 distinct flooding scenarios. UV/Vis spectrophotometry is used to quantify nanoparticle concentrations in effluents, elucidating their transport behavior within the porous media. Concurrently, pressure drop alterations across the media serve as indicators of particle plugging and changes in permeability. Intriguingly, specific conditions involving a nanofluid comprising 50 nm silica nanoparticles and 10,000 ppm of magnesium chloride exhibit pronounced permeability reduction, offering potential insights for optimizing applications. Particularly noteworthy is the unique reduction in silica particle retention on glass bead surfaces as salinity increases, especially in the presence of magnesium sulfate. A concentration of 5000 ppm magnesium sulfate induces a log-jamming mechanism, resulting in an amplified final-to-intermediate permeability ratio. Experimental outcomes align with observations from scanning electron microscopy, improving understanding of porous media retention mechanisms. This study contributes to a deeper understanding of interactions between nanoparticles and porous media, paving the way for enhanced application strategies.

An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies.

The present study evaluates the drilling fluid density of oil fields at enhanced temperatures and pressures. The main objective of this work is to introduce a set of modeling and experimental techniques for forecasting the drilling fluid density via various intelligent models. Three models were assessed, including PSO-LSSVM, ICA-LSSVM, and GA-LSSVM. The PSO-LSSVM technique outperformed the other models in light of the smallest deviation factor, reflecting the responses of the largest accuracy. The experimental and modeled regression diagrams of the coefficient of determination (R2) were plotted. In the GA-LSSVM approach, R2 was calculated to be 0.998, 0.996 and 0.996 for the training, testing and validation datasets, respectively. R2 was obtained to be 0.999, 0.999 and 0.998 for the training, testing and validation datasets, respectively, in the ICA-LSSVM approach. Finally, it was found to be 0.999, 0.999 and 0.999 for the training, testing and validation datasets in the PSO-LSSVM method, respectively. In addition, a sensitivity analysis was performed to explore the impacts of several variables. It was observed that the initial density had the largest impact on the drilling fluid density, yielding a 0.98 relevancy factor.