Development of Nanofluids for the Inhibition of Formation Damage Caused by Fines Migration: Effect of the interaction of Quaternary Amine (CTAB) and MgO Nanoparticles

Simultaneous Detection of Carbon Quantum Dots as Tracers for Interwell Connectivity Evaluation in a Pattern with Two Injection Wells

This study aimed to develop and implement a nanotechnology-based alternative to traditional tracers used in the oil and gas industry for assessing interwell connectivity. A simple and rapid hydrothermal protocol for synthesizing carbon quantum dots (CQDs) using agroindustry waste was implemented. Three commercial CQDs were employed (CQDblue, CQDgreen, and CQDred); the fourth was synthesized from orange peel (CQDop). The CQDs from waste and other commercials with spherical morphology, nanometric sizes less than 11 nm in diameter, and surface roughness less than 3.1 nm were used. These tracers demonstrated high colloidal stability with a negative zeta potential, containing carbonyl-type chemical groups and unsaturations in aromatic structures that influenced their optical behavior. All materials presented high colloidal stability with negative values of charge z potential between -17.8 and -49.1. Additionally, individual quantification of these tracers is feasible even in scenarios where multiple CQDs are present in the effluent with a maximum percentage of interference of 15.5% for CQDop in the presence of the other three nanotracers. The CQDs were injected into the field once the technology was insured under laboratory conditions. Monitoring the effluents allowed the determination of connectivity for five first-line producer wells. This study enables the application of CQDs in the industry, particularly in fields where the arrangement of injector and producer wells is intricate, requiring the use of multiple tracers for a comprehensive description of the system.

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models' predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand-brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

Use of Nanoparticles in Completion Fluids as Dual Effect Treatments for Well Stimulation and Clay Swelling Damage Inhibition: An Assessment of the Effect of Nanoparticle Chemical Nature

The objective of this study is to evaluate the role of nanoparticles with different chemical structures in completion fluids (CF) in providing a positive dual effect for well stimulation and clay swelling damage inhibition. Six types of commercial (C) or synthesized (S) nanoparticles have been incorporated into a commercial completion fluid. Doses varied between 100 and 500 mg·L^-1. CF-nanoparticles were evaluated by fluid-fluid, fluid-nanoparticle, and fluid-rock interactions. The adsorption isotherms show different degrees of affinity, which impacts on the reduction of the interfacial tension between the CF and the reservoir fluids. Fluid-fluid interactions based on interfacial tension (IFT) measurements suggest that positively charged nanoparticles exhibit high IFT reductions. Based on contact angle measurements, fluid-rock interactions suggest that ZnO-S, SiO₂-C, SiO₂-S, and ZrO₂ can adequately promote water-wet rock surfaces compared with other nanomaterials. According to the capillary number, ZnO-S and MgO-S have a higher capacity to reduce both interfacial and surface restrictions for crude oil production, suggesting that completion fluid with nanoparticles (NanoCF) can function as a stimulation agent. The clay swelling inhibition test in the presence of ZnO-S-CTAB and MgO-S-CTAB nanoparticles showed a 28.6% decrease in plastic viscosity (PV), indicating a reduction in clay swelling. The results indicate that a high-clay environment can meet the completion fluid's requirements. They also indicate that the degree of clay swelling inhibition of the nanoparticles depends on their chemical nature and dosage. Finally, displacement tests revealed that CF with nanoparticles increased the oil linear displacement efficiency.

Power from Geothermal Resources as a Co-product of the Oil and Gas Industry: A Review

The increase in the global demand for energy and fossil fuel dependency is hindering efforts to reduce greenhouse gas (GHG) emissions. Geothermal resources supplement this increase in energy demand with reduced emissions because of their availability, base-load production profile, and climatic independence. Despite these advantages, the development of geothermal energy is limited because of different reasons such as subsurface exploration risk and high upfront capital cost for drilling and facility construction. However, similarities in infrastructure and operations between the oil and gas industry and the geothermal industry can optimize expense and development when exploiting geothermal resources. Thus, in this review, we present recent advances and applications of geothermal power systems in the oil and gas industry starting from the fundamentals and basic principles of geothermal energy and the organic Rankine cycle (ORC). These applications include the use of geothermal resources via abandoned wells, active wells, and paired thermal enhanced oil recovery processes with injection for fluid heating and energy production. Abandoned wells are alternatives that reduce costs in geothermal energy-use projects. The use of geothermal resources via active wells allows the valorization of a resource, such as the production of water, which is considered a byproduct of production activities in an oilfield. The use of thermally enhanced oil recovery processes enhances the energy conditions of fluids produced in the field, improving geothermal systems with fluids at higher temperatures. Finally, an overview is presented of the challenges and opportunities of geothermal energy in the oil industry where the requirement to improve the usage of technologies, such as the ORCs, with the working fluids used in the cycles, is highlighted. Furthermore, the importance of environmental studies and use of novel tools, such as nanotechnology, to improve the efficiency of geothermal energy usage is highlighted.

Experimental Study on the Stability of a Novel Nanocomposite-Enhanced Viscoelastic Surfactant Solution as a Fracturing Fluid under Unconventional Reservoir Stimulation

Fe₃O₄@ZnO nanocomposites (NCs) were synthesized to improve the stability of the wormlike micelle (WLM) network structure of viscoelastic surfactant (VES) fracturing fluid and were characterized by Fourier transform infrared spectrometry (FT-IR), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), X-ray diffraction (XRD) and vibrating sample magnetometry (VSM). Then, an NC-enhanced viscoelastic surfactant solution as a fracturing fluid (NC-VES) was prepared, and its properties, including settlement stability, interactions between NCs and WLMs, proppant-transporting performance and gel-breaking properties, were systematically studied. More importantly, the influences of the NC concentration, shear rate, temperature and pH level on the stability of NC-VES were systematically investigated. The experimental results show that the NC-VES with a suitable content of NCs (0.1 wt.%) shows superior stability at 95 °C or at a high shear rate. Meanwhile, the NC-VES has an acceptable wide pH stability range of 6-9. In addition, the NC-VES possesses good sand-carrying performance and gel-breaking properties, while the NCs can be easily separated and recycled by applying a magnetic field. The temperature-resistant, stable and environmentally friendly fracturing fluid opens an opportunity for the future hydraulic fracturing of unconventional reservoirs.

Fine Migration Control in Sandstones: Surface Force Analysis and Application of DLVO Theory

Formation damage caused by fine migration and straining is a well-documented phenomenon in sandstone reservoirs. Fine migration and the associated permeability decline have been observed in various experimental studies, and this phenomenon has been broadly explained by the analysis of surface forces between fines and sand grains. The Derjaguin-Landau-Verwey-Overbeek (DLVO) theory is a useful tool to help understand and model the fine release, migration, and control phenomena within porous media by quantifying the total interaction energy of the fine-brine-rock (FBR) system. Fine migration is mainly caused by changes in the attractive and repulsive surface forces, which are triggered by mud invasion during drilling activity, the utilization of completion fluid, acidizing treatment, and water injection into the reservoir during secondary and tertiary recovery operations. Increasing pH and decreasing water salinity collectively affect the attractive and repulsive forces and, at a specific value of pH, and critical salt concentration (CSC), the total interaction energy of the FBR system (V _T) shifts from negative to positive, indicating the initiation of fine release. Maintaining the system pH, setting the salinity above the CSC, tuning the ionic composition of injected water, and using nanoparticles (NPs) are practical options to control fine migration. DLVO modeling elucidates the total interaction energy between fines and sand grains based on the calculation of surface forces of the system. In this context, zeta potential is an important indicator of an increase or decrease in repulsive forces. Using available data, two correlations have been developed to calculate the zeta potential for sandstone reservoirs in high- and low-salinity environments and validated with experimental values. Based on surface force analysis, the CSC is predicted by the DLVO model; it is in close agreement with the experimental value from the literature. The critical pH value is also estimated for alkaline flooding. Model results confirm that the application of NPs and the presence of divalent ions increase the attractive force and help to mitigate the fine migration problem. Hence, a new insight into the analysis of quantified surface forces is presented in current research work by the practical application of the DLVO theory to model fine migration initiation under the influence of injection water chemistry.

Cardanol /SiO2 Nanocomposites for Inhibition of Formation Damage by Asphaltene Precipitation/Deposition in Light Crude Oil Reservoirs. Part II: Nanocomposite Evaluation and Coreflooding Test

This study aims to evaluate the behavior of Cardanol/SiO2 nanocomposites in the inhibition of the asphaltene damage based on the coreflooding test at reservoir conditions. The nanocomposite design was performed in Part I (https://doi.org/10.1021/acs.energyfuels.0c01114), leading to SiO2 nanoparticles functionalized with different mass fractions of cardanol on the surface of 5 (5CSN), 7 (7CSN), and 9% (9CSN). In this part of the study, the nanocomposite/reservoir fluid interactions were evaluated through interfacial tension measurements and nanocomposite/rock surface interactions using water imbibition and contact angle measurements. Results showed that the designed nanocomposite leads to a reduction of interfacial tension of 82.6, 61.7, and 51.4% for 5CSN, 7CSN, and 9CSN regarding silica support (SN). Whereas, the reduction of the Si-OH functional groups from SiO2 nanoparticles due to the increase of the cardanol content affects the effectiveness of the wettability alteration for 7CSN and 9CSN. Nevertheless, when 5CSN is evaluated, the system is altered from an oil-wet to a mixed-wet state. Coreflooding tests at reservoir conditions were performed to evaluate the oil recovery after asphaltene damage, after damage removal and nanofluid injection, and after induction of a second asphaltene damage to check inhibition. Results show that the selected nanocomposites at a dosage of 300 mg·L-1 enhance the oil recovery in comparison with the baseline conditions via the reduction of the interfacial/surface forces at the pore scale and wettability alteration. It is worth to remark that this improvement remains after the second asphaltene damage induction, which proves the high inhibitory capacity of the designed nanocomposite for the asphaltene precipitation/deposition. Also, the use of the nanocomposites favors the oil recovery more than 50% compared to the asphaltene damage scenario.