New molecular and macroscopic understandings of novel green chemicals based on Xanthan Gum and bio-surfactants for enhanced oil recovery.

This research investigates the interactions between a novel environmentally friendly chemical fluid consisting of Xanthan gum and bio-based surfactants, and crude oil. The surfactants, derived from various leaves using the spray drying technique, were characterized using Fourier-transform infrared (FTIR) spectroscopy, zeta potential analysis, Dynamic light scattering, and evaluation of critical micelle concentration. Static emulsion tests were conducted to explore the emulsification between crude oil and the polymer-surfactant solution. Analysis of the bulk oil FTIR spectra revealed that saturated hydrocarbons and light aromatic hydrocarbons exhibited a higher tendency to adsorb onto the emulsion phase. Furthermore, the increased presence of polar hydrocarbons in emulsion phases generated by polar surfactants confirmed the activation of electrostatic forces in fluid-fluid interactions. Nuclear magnetic resonance spectroscopy showed that the xanthan solution without surfactants had a greater potential to adsorb asphaltenes with highly fused aromatic rings, while the presence of bio-based surfactants reduced the solution's ability to adsorb asphaltenes with larger cores. Microfluidic tests demonstrated that incorporating surfactants derived from Morus nigra and Aloevera leaves into the xanthan solution enhanced oil recovery. While injection of the xanthan solution resulted in a 49.8% recovery rate, the addition of Morus nigra and Aloevera leaf-derived surfactants to the xanthan solution increased oil recovery to 58.1% and 55.8%, respectively.

Assessment of heavy oil recovery mechanisms using in-situ synthesized CeO2 nanoparticles.

This project investigated the impact of low-temperature, in-situ synthesis of cerium oxide (CeO2) nanoparticles on various aspects of oil recovery mechanisms, including changes in oil viscosity, alterations in reservoir rock wettability, and the resulting oil recovery factor. The nanoparticles were synthesized using a microemulsion procedure and subjected to various characterization analyses. Subsequently, these synthesized nanoparticles were prepared and injected into a glass micromodel, both in-situ and ex-situ, to evaluate their effectiveness. The study also examined the movement of the injected fluid within the porous media. The results revealed that the synthesized CeO2 nanoparticles exhibited a remarkable capability at low temperatures to reduce crude oil viscosity by 28% and to lighten the oil. Furthermore, the addition of CeO2 nanoparticles to the base fluid (water) led to a shift in the wettability of the porous medium, resulting in a significant reduction in the oil drop angle from 140° to 20°. Even a minimal presence of CeO2 nanoparticles (0.1 wt%) in water increased the oil production factor from 29 to 42%. This enhancement became even more pronounced at a concentration of 0.5 wt%, where the oil production factor reached 56%. Finally, it was found that the in-situ injection, involving the direct synthesis of CeO2 nanoparticles within the reservoir using precursor salts solution and reservoir energy, led to an 11% enhancement in oil production efficiency compared to the ex-situ injection scenario, where the nanofluid is prepared outside the reservoir and then injected into it.

An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation.

The lithology log, an integral component of the master log, graphically portrays the encountered lithological sequence during drilling operations. In addition to offering real-time cross-sectional insights, lithology logs greatly aid in correlating and evaluating multiple sections efficiently. This paper introduces a novel workflow reliant on an enhanced weighted average ensemble approach for producing high-resolution lithology logs. The research contends with a challenging multiclass imbalanced lithofacies distribution emerging from substantial heterogeneities within subsurface geological structures. Typically, methods to handle imbalanced data, e.g., cost-sensitive learning (CSL), are tailored for issues encountered in binary classification. Error correcting output code (ECOC) originates from decomposition strategies, effectively breaking down multiclass problems into numerous binary subproblems. The database comprises conventional well logs and lithology logs obtained from five proximate wells within a Middle Eastern oilfield. Utilizing well-known machine learning (ML) algorithms, such as support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and extreme gradient boosting (XGBoost), as baseline classifiers, this study aims to enhance the accurate prediction of underground lithofacies. Upon recognizing a blind well, the data from the remaining four wells are utilized to train the ML algorithms. After integrating ECOC and CSL techniques with the baseline classifiers, they undergo evaluation. In the initial assessment, both RF and SVM demonstrated superior performance, prompting the development of an enhanced weighted average ensemble based on them. The comprehensive numerical and visual analysis corroborates the outstanding performance of the developed ensemble. The average Kappa statistic of 84.50%, signifying almost-perfect agreement, and mean F-measures of 91.04% emphasize the robustness of the designed ensemble-based workflow during the evaluation of blind well data.

Synthesis and physicochemical properties of dual-responsive acrylic acid/butyl acrylate cross-linked nanogel systems.

HYPOTHESIS: A cross-linked amphiphilic nanogel containing a high mole% of hydrophilic pH-responsive moiety can provide enhanced functionality regarding stimuli-responsiveness, water-dispersibility, hydrophobic substance loading, and structural stability under harsh environmental conditions. These nanogels could be synthesized using a one-pot procedure for large-scale applications. Moreover, the interplay of various interaction forces in these colloidal systems is being investigated. EXPERIMENTS: Model nanogels consisting of acrylic acid-butyl acrylate-ethylene glycoldimethacrylate were synthesized using an emulsion copolymerization via a seeded semi-batch process under an acidic condition. The structures were assessed by Fourier transform infrared spectroscopy and potentiometric-conductometric titrations. Zeta potential, field-emission scanning electron microscopy, and transmission electron microscopy were used to evaluate the dispersion stability, size distribution, and structural distribution, respectively. Their stimuli-responsive behavior was studied by combining static and dynamic light scattering and titration analyses. FINDINGS: Monodisperse nanospheres of approximately 150 nm were successfully prepared by implementing a one-pot practical pathway. These nanogels displayed a dual thermo- and pH-responsive behavior, reflecting the high efficiency of physical cross-linking make it ideal for drug delivery and oil industry applications. Moreover, a novel symmetric pH-activated morphology transformation behavior was revealed. Accordingly, a compositional distribution was proposed and assessed by exploring the polymerization process.