Search Results (105)

As an important unconventional oil and gas resource, tight oil exploration and development is of great significance to ensure energy supply under the background of continuous growth of global energy demand. Low-porosity and low-permeability reservoirs are characterized by tight rock properties, poor physical properties, and complex pore structure, and as a result the fine calculation of logging reservoir parameters faces great challenges. In addition, the crude oil in this area has high viscosity, the formation water salinity is low, and the oil reservoir resistivity shows significant spatial variability in the horizontal direction, which further increases the difficulty of oil and water reservoir identification and affects the accuracy of oil saturation calculation. Targeting the above problems, the Nutcracker Optimization Algorithm (NOA) was used to optimize the hyperparameters of the random forest classification model, and then the optimal hyperparameters were input into the random forest model, and the conventional logging curve and oil test data were combined to identify and classify the reservoir fluids, with the final accuracy reaching 94.92%. Compared with the traditional Hingle map intersection method, the accuracy of this method is improved by 14.92%, which verifies the reliability of the model for fluid identification of low-porosity and low-permeability reservoirs in the research block and provides reference significance for the next oil test and production test layer in this block. Full article

The bottom-simulating reflector (BSR) serves as an important seismic indicator for identifying gas hydrate-bearing sediments. This review synthesizes global BSR observations and demonstrates that spatial relationships among BSRs, free gas, and gas hydrates frequently deviate from one-to-one correspondence. Moreover, our analysis reveals that more than 35% of global BSRs occur shallower than the bases of gas hydrate stability zones, especially in deepwater regions, suggesting that the BSRs more accurately represent the interface between the gas hydrate occurrence zone and the underlying free gas zone. BSR morphology is influenced by geological settings, sediment properties, and seismic acquisition parameters. We find that ~70–80% of BSRs occur in fine-grained, grain-displacive sediments with hydrate lenses/nodules, while coarse-grained pore-filling sediments host <20%. BSR interpretation remains challenging due to limitations in traditional P-wave seismic profiles and conventional amplitude versus offset (AVO) analysis, which hinder accurate fluid identification. To address these gaps, future research should focus on frequency-dependent AVO inversion based on viscoelastic theory, multicomponent full-waveform inversion, improved anisotropy assessment, and quantitative links between rock microstructure and elastic properties. These innovations will shift BSR research from static feature mapping to dynamic process analysis, enhancing hydrate detection and our understanding of hydrate–environment interactions. Full article

The tight sandstone gas reservoirs in the LX area of the Ordos Basin are characterized by low porosity, poor permeability, and strong heterogeneity, which significantly complicate fluid type identification. Conventional methods based on petrophysical logging and core analysis have shown limited effectiveness in this region, often resulting in low accuracy of fluid identification. To improve the precision of fluid property identification in such complex tight gas reservoirs, this study proposes a hybrid deep learning model named ResViTNet, which integrates ResNet (residual neural network) with ViT (vision transformer). The proposed method transforms multi-dimensional logging data into thermal maps and utilizes a sliding window sampling strategy combined with data augmentation techniques to generate high-dimensional image inputs. This enables automatic classification of different reservoir fluid types, including water zones, gas zones, and gas–water coexisting zones. Application of the method to a logging dataset from 80 wells in the LX block demonstrates a fluid identification accuracy of 97.4%, outperforming conventional statistical methods and standalone machine learning algorithms. The ResViTNet model exhibits strong robustness and generalization capability, providing technical support for fluid identification and productivity evaluation in the exploration and development of tight gas reservoirs. Full article

The understanding and application of fluid-type inerters by scholars have been on the rise. However, due to their intricate multiphase mechanical properties, existing models still have considerable room for improvement. This study presents two fractional-order models and conducts parameter identification by integrating them with classical experimental data. The first model is an independent fractional-order model. In comparison with traditional models, it demonstrates significantly higher fitting accuracy in frequency regions beyond the ultra-low frequency range. The second model is a segmented fractional-order model, which determines segments according to critical frequencies. Although this model enhances the overall fitting accuracy, it also leads to increased complexity. To tackle this complexity issue, a rough design strategy is proposed to minimize the critical frequency. Research indicates that under such a strategy, the inertial effect dominates the behavior of the fluid inerter. Even when the independent fractional-order model is used, a high fitting accuracy can be achieved. Consequently, by designing the structural parameters and fluid medium of the fluid inerter based on the rough design strategy, the model can be simplified. Moreover, compared with traditional nonlinear inerter models, the transfer function and eigenvalue analysis methods can be effectively applied. This enables the acquisition of more comprehensive theoretical research results, thereby greatly facilitating theoretical analysis. Full article

Background: Nanomaterial-based biosensors represent a transformative advancement in oral health diagnostics and therapeutics, offering superior sensitivity and selectivity for early disease detection compared to conventional methods. Their applications span prosthetic dentistry, where they enable the precise monitoring of dental implants, and theranostics for conditions such as dental caries, oral cancers, and periodontal diseases. These innovations promise to enhance proactive oral healthcare by integrating detection, treatment, and preventive strategies. Objectives: This review comprehensively examines the role of nanomaterial-based biosensors in dental theranostics, with a focus on prosthetic applications. It emphasizes their utility in dental implant surveillance, the early identification of prosthesis-related complications, and their broader implications for personalized treatment paradigms. Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science for studies published between 2010 and early 2025. Keywords included combinations of “nanomaterials”, “biosensors”, “dentistry”, “oral health”, “diagnostics”, “therapeutics”, and “theranostics”. Articles were selected based on their relevance to nanomaterial applications in dental biosensors and their clinical translation. Results: The review identified diverse classes of nanomaterials—such as metallic nanoparticles, carbon-based structures, and quantum dots—whose unique physicochemical properties enhance biosensor performance. Key advancements include the ultra-sensitive detection of biomarkers in saliva and gingival crevicular fluid, the real-time monitoring of peri-implant inflammatory markers, and cost-effective diagnostic platforms. These systems demonstrate exceptional precision in detecting early-stage pathologies while improving operational efficiency in clinical settings. Conclusions: Nanomaterial-based biosensors hold significant promise for revolutionizing dental care through real-time implant monitoring and early complication detection. Despite challenges related to biocompatibility, scalable manufacturing, and rigorous clinical validation, these technologies may redefine oral healthcare by extending prosthetic device longevity, enabling personalized interventions, and reducing long-term treatment costs. Future research must address translational barriers to fully harness their potential in improving diagnostic accuracy and therapeutic outcomes. Full article

This study systematically analyzed the color characteristics, microscopic inclusions (including fluid and mineral inclusions), spectral properties, and chemical composition of emerald samples from Kafubu, Zambia using infrared spectroscopy, UV–visible spectroscopy, Raman spectroscopy, and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The results were then compared with research data on emeralds from Afghanistan, Brazil, China, Colombia, Ethiopia, Madagascar, Russia, and the United States. The result establishes a global classification framework for emerald origins based on chromophores (Cr, V, Fe), categorizing deposits into two distinct groups: low-Fe regions and high-Fe regions. For high-Fe type IA emeralds, particularly those from Zambia and Madagascar exhibiting exceptionally similar Fe and Mg concentrations, a multi-element discrimination approach was developed. Using microscopic infrared testing to magnify and analyze the characteristic peaks related to OD in the range of 2550–2800 cm⁻¹, it can be classified as HDO-dominant, and the high alkali metal element content in Zambian emeralds can be reflected by the absence of the HDO vOD absorption peak at 2685 cm⁻¹. A further in-depth analysis of the trace elements in Zambian emeralds can provide a basis for inferring the possible rich ore geology for subsequent mining and provide more effective reference data for the identification of the origin of emeralds. Full article

Colitis in equines has high morbidity and mortality rates, which severely affects the development of the equine-breeding industry. With the issuance of antibiotic bans, there is an urgent need for healthier and more effective alternatives. In recent years, probiotics have been widely used as microbial feed additives in animal husbandry, playing a crucial role in preventing and treating diarrhea and regulating host immune function. In this study, we isolated and screened a strain with rapid and stable acid production using bromocresol purple, litmus milk coloration tests, and acid production performance assessments. Based on morphological characteristics, physiological and biochemical properties, and 16S rDNA identification, the strain was identified as Pediococcus pentosaceus and named M6. The Pediococcus pentosaceus M6 exhibited stable growth and tolerance to high temperatures, acid and bile salt concentrations, and simulated gastrointestinal fluid environments. The M6 strain demonstrated good antibacterial effects against Escherichia coli, Staphylococcus aureus, and Salmonella. The M6 strain did not produce hemolysis zones on Columbia blood agar plates, indicating its high safety, and was found to be insensitive to 12 antibiotics, including cephalexin and neomycin. Additionally, intervention in mice with dextran sulfate sodium (DSS)-induced colitis alleviated weight loss and shortened colon length. To a certain extent, it regulated the expression of inflammatory cytokines and the gut microbiota within the body and reduced inflammatory cell infiltration and intestinal barrier damage. In summary, the isolated Pediococcus pentosaceus M6 strain exhibited excellent probiotic properties and could alleviate DSS-induced colitis in mice, suggesting its potential application value as a probiotic in animal husbandry. Full article

The inherent complexity of cancer proliferation and malignancy cannot be addressed by the conventional approach of relying on high doses of a single powerful anticancer agent, which is associated with poor efficacy, higher toxicity, and the development of drug resistance. Multiple drug therapy (MDT) rationally designed to target tumor heterogeneity, block alternative survival pathways, modulate the tumor microenvironment, and reduce toxicities would be a viable solution against cancer. Liposomes are the most suitable carrier for anticancer MDT due to their ability to encapsulate both hydrophilic and hydrophobic agents, biocompatibility, and controlled release properties; however, an adequate manufacturing method is important for effective co-encapsulation. Microfluidics involves the manipulation of fluids at the microscale for the controlled synthesis of liposomes with desirable properties. This work critically reviews the use of microfluidics for the synthesis of anticancer MDT liposomes. MDT success not only relies on the identification of synergistic dose combinations of the anticancer modalities but also warrants the loading of multiple therapeutic entities within liposomes in optimal ratios, the protection of the drugs by the nanocarrier during systemic circulation, and the synchronous release at the target site in the same pattern as confirmed in preliminary efficacy studies. Prospects have been identified for the bench-to-bedside translation of anticancer MDT liposomes using microfluidics. Full article

Recent advancements in analytical strategies have enabled the efficient extraction and characterization of bioactive compounds from agri-food bio-residues, emphasizing green chemistry and circular economy principles. This review highlights the valorization of several agri-food bio-residues for the extraction of high-value-added bioactive compounds, particularly polyphenols, tocopherols, carotenoids, and fatty acids, as a biorefinery approach. To this end, the adoption of environmentally friendly extraction technologies is essential to improve performance, reduce energy consumption, and minimize costs. This study therefore examines emerging methodologies such as supercritical fluid extraction, pressurized liquid extraction, pulsed electric fields, and matrix solid-phase dispersion, highlighting their advantages and limitations. Additionally, the chemical characterization of these bioactive compounds is explored through spectrophotometric and high-resolution chromatographic techniques, crucial for their accurate identification and quantification. This is complemented by an analysis of bioactivity assays evaluating antioxidant, antimicrobial, anticancer, neuroprotective, and anti-inflammatory properties, with a focus on their applications in the food, pharmaceutical, and cosmetic industries. However, the analytical control of toxic compounds, such as alkaloids, in these bio-residues is undoubtedly needed. Ultimately, this approach not only promotes sustainability but also contributes to the development of eco-friendly solutions in various industries. Full article

A comparison between the gel properties of blends of kappa- and iota-carrageenans (K+Is) and hybrid carrageenans (KIs) with equivalent chemical compositions is here presented. The objective is to assess under which conditions hybrid carrageenans are valuable alternative to blends of kappa- and iota-carrageenans for gelling applications and to contribute to the identification of phase-separated structures or co-aggregated helices. Phase states constructed in sodium chloride and in potassium chloride confirm that KIs build gels under a much narrower range of ionic strength and polysaccharide concentration. Hybrid carrageenans displayed salt specificity, forming gels in KCl but not in NaCl, highlighting their limited gelling potential in Na⁺ environments. A two-step gelation mechanism was found in both systems at lower ionic strengths and when iota carrageenan is the major component. The shear elastic moduli of KI gels are overall smaller than those of blends, but the opposite is observed at lower ionic strengths in KCl and in systems richer in iota-carrageenans. The nonlinear elastic properties of gels do not relate to the use of blends or hybrid carrageenans for their formulation. Instead, larger contents in iota-carrageenans lead to gels able to sustain larger strains before yielding to a fluid state. However, these gels are more prone to strain softening, whereas strain hardening is measured in gels containing more kappa-carrageenan, irrespective of their blend or hybrid structure. Full article

Geophysical logging curves are crucial for oil and gas field exploration and development, and curve reconstruction techniques are a key focus of research in this field. This study proposes an inversion model for deep resistivity curves in marine carbonate reservoirs, specifically the Mishrif Formation of the Halfaya Field, by integrating a deep learning model called CNN-GRU-ATT, which combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the Attention Mechanism (ATT). Using logging data from the marine carbonate oil layers, the reconstructed deep resistivity curve is compared with actual measurements to determine reservoir fluid properties. The results demonstrate the effectiveness of the CNN-GRU-ATT model in accurately reconstructing deep resistivity curves for carbonate reservoirs within the Mishrif Formation. Notably, the model outperforms alternative methods such as CNN-GRU, GRU, Long Short-Term Memory (LSTM), Multiple Regression, and Random Forest in new wells, exhibiting high accuracy and robust generalization capabilities. In practical applications, the response of the inverted deep resistivity curve can be utilized to identify the reservoir water cut. Specifically, when the model-inverted curve exhibits a higher response compared to the measured curve, it indicates the presence of reservoir water. Additionally, a stable relative position between the two curves suggests the presence of a water layer. Utilizing this method, the oil–water transition zone can be accurately delineated, achieving a fluid property identification accuracy of 93.14%. This study not only introduces a novel curve reconstruction method but also presents a precise approach to identifying reservoir fluid properties. These findings establish a solid technical foundation for decision-making support in oilfield development. Full article

A significant part of modern natural sciences aims to establish a model-based approach to describe the behavior of physical systems and forecast their dynamics in different scenarios. The successful application of model-based analysis of transport phenomena is driven by several components, such as the consistency of a model and the uncertainty associated with its parameters. The unsatisfactory results of simulations can be caused by an improper choice of numerical methods used, but more importantly, it can be a result of wrong assumptions while establishing the model and a poor choice of closure parameters such as physical properties of fluids. This motivated the development of a hybrid approach that combines model identification directly from the data and subsequent real-time parameter estimation, which eventually minimizes the uncertainty of the developed model. This essentially brings a new model-based approach for an optimal simulation of physical phenomena by incorporating stringent interactions between all the stages of the modeling process. The identification of the governing equation from the data is achieved by a regression technique, while the model refinement is performed using the extended Kalman filter algorithm. The obtained in such a way model is then applied for control-oriented analysis. This paper discusses the deployment of such an integrated approach on a step-to-step basis and demonstrates its application to the problem of a single-phase oil inflow to the producing well. Full article

Optimizing the drilling process is critical for the exploration of natural resources. However, there are several mechanic parameters that continuously interact with formation properties, hindering the optimization process. Rate of penetration (ROP) and mechanical specific energy (MSE) are considered two key performance indicators that allow the identification of ideal conditions to enhance the drilling process. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a 2D analysis of parameters as a function of depth, response surface methodology (RSM), and multi-objective optimization. The results show that the RSM method and multi-objective optimization provide better results when compared with 2D analysis of parameters as a function of depth. The RSM method can be used as a tool to analyze the effects of the independent drilling mechanical parameters (WOB, RPM, FLOW, and TOR) on the response variables (ROP and MSE) with a 95% confidence level. Through multi-objective optimization, it was possible to concomitantly achieve an ROP of approximately 22 ft/h and MSE of nearly 11 kpsi using the values of WOB, RPM, FLOW, and TOR of about 11 klb, 109 rev/min, 803 gpm, and 3 klb-ft, respectively. Using high WOB values, i.e., from the mean value up to the maximum value of approximately 43 klb, reflects a low ROP and most likely indicates an operation beyond the foundering point. High FLOW promotes a more efficient hole cleaning and higher rates of cuttings transport, thus preventing eventual in situ drill-bit sticking. Flow adjustment also ensures an adequate balance of dynamic bottom hole pressure, in addition to controlling the force impact force of the drilling fluid in contact with the rock being drilled, expressing importance in terms of efficiency and rock penetration. Finally, it is important to mention that the results of this research are not only applicable to hydrocarbon exploration but also to geothermal and natural hydrogen exploration. Values analyzed and presented with decimal precision should be logically focused as integers when in industrial application. Full article

Tight sandstone reservoirs are characterized by fine-grained rock particles, a high clay content, and a complex interplay between the electrical properties and gas content. These factors contribute to low-contrast reservoirs, where the logging responses of the gas and water layers are similar, resulting in traditional logging interpretation charts exhibiting a low accuracy in the fluid-type classification. This inadequacy fails to meet the fluid identification needs of the study area’s reservoirs and severely restricts the exploration and development of unconventional oil and gas resources. To address this challenge, this study proposes a fluid identification method based on Bayesian-optimized Support Vector Machine (SVM) to enhance the accuracy and efficiency of the fluid identification in low-contrast reservoirs. Firstly, through a sensitivity analysis of the logging responses, sensitive logging parameters such as the natural gamma, compensated density, compensated neutron, and compensated sonic logs are selected as input data for the model. Subsequently, Bayesian optimization is employed to automatically search for the optimal combination of hyperparameters for the SVM model. Finally, an SVM model is established using the optimized hyperparameters to classify and identify the following four fluid types: water layers, gas layers, gas–water layers, and dry layers. The proposed method is applied to fluid identification in the study area, and comparative experiments are conducted with the K-Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost models. The classification performance of each model is systematically evaluated using metrics such as the accuracy, recall, and F1-score. The experimental results indicate that the SVM model outperforms the other models in fluid identification, achieving an average accuracy of 91.41%. This represents improvements of 16.94%, 4.39%, and 8.30% over the KNN, RF, and AdaBoost models, respectively. These findings validate the superiority of the SVM model for fluid identification in the study area and provide an efficient and feasible solution for fluid identification in tight sandstone reservoirs. Full article

The growing demand for plant-based protein and natural food ingredients has further fueled interest in exploring hemp seeds (Cannabis sativa L.) as a sustainable source of and nutrition. In addition to the content of proteins and healthy fats (linoleic acid and alpha-linolenic acid), hemp seeds are rich in phytochemical compounds, especially terpenoids, polyphenols, and phytosterols, which contribute to their bioactive properties. Scientific studies have shown that these compounds possess significant antioxidant, antimicrobial, and anti-inflammatory effects, making hemp seeds a promising ingredient for promoting health. Since THC (tetrahydrocannabinol) and CBD (cannabidiol) are found only in traces, hemp seeds can be used in food applications because the psychoactive effects associated with cannabis are avoided. Therefore, the present article reviews the scientific literature on traditional and modern extraction methods for obtaining active substances that meet food safety standards, enabling the transformation of conventional foods into functional foods that provide additional health benefits and promote a balanced and sustainable diet. Also, the identification methods of biologically active compounds extracted from hemp seeds and their bioactivity were evaluated. Mechanical pressing extraction, steam distillation, solvent-based methods (Soxhlet, maceration), and advanced techniques such as microwave-assisted and supercritical fluid extraction were evaluated. Identification methods such as high-performance liquid chromatography (HPLC) and mass spectrometry (MS) allowed for detailed chemical profiling of cannabinoids, terpenes, and phenolic substances. Optimizing extraction parameters, including solvent type, temperature, and time, is crucial for maximizing yield and purity, offering the potential for developing value-added foods with health benefits. Full article