Pressure transient characteristics of non-uniform conductivity fractured wells in viscoelasticity polymer flooding based on oil-water two-phase flow

Polymer flooding in fractured wells has been extensively applied in oilfields to enhance oil recovery.In contrast to water,polymer solution exhibits non-Newtonian and nonlinear behavior such as effects of shear thinning and shear thickening,polymer convection,diffusion,adsorption retention,inaccessible pore volume and reduced effective permeability.Meanwhile,the flux density and fracture conductivity along the hydraulic fracture are generally non-uniform due to the effects of pressure distribution,formation damage,and proppant breakage.In this paper,we present an oil-water two-phase flow model that captures these complex non-Newtonian and nonlinear behavior,and non-uniform fracture characteristics in fractured polymer flooding.The hydraulic fracture is firstly divided into two parts:high-conductivity fracture near the wellbore and low-conductivity fracture in the far-wellbore section.A hybrid grid system,including perpendicular bisection(PEBI)and Cartesian grid,is applied to discrete the partial differential flow equations,and the local grid refinement method is applied in the near-wellbore region to accurately calculate the pressure distribution and shear rate of polymer solution.The combination of polymer behavior characterizations and numerical flow simulations are applied,resulting in the calculation for the distribution of water saturation,polymer concentration and reservoir pressure.Compared with the polymer flooding well with uniform fracture conductivity,this non-uniform fracture conductivity model exhibits the larger pressure difference,and the shorter bilinear flow period due to the decrease of fracture flow ability in the far-wellbore section.The field case of the fall-off test demonstrates that the proposed method characterizes fracture characteristics more accurately,and yields fracture half-lengths that better match engineering reality,enabling a quantitative segmented characterization of the near-wellbore section with high fracture conductivity and the far-wellbore section with low fracture conductivity.The novelty of this paper is the analysis of pressure performances caused by the fracture dynamics and polymer rheology,as well as an analysis method that derives formation and fracture parameters based on the pressure and its derivative curves.

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

Simulation of Two-Phase Flowback Phenomena in Shale Gas Wells

The gas-water two-phaseflow occurring as a result of fracturingfluidflowback phenomena is known to impact significantly the productivity of shale gas well.In this work,this two-phaseflow has been simulated in the framework of a hybrid approach partially relying on the embedded discrete fracture model(EDFM).This model assumes the region outside the stimulated reservoir volume(SRV)as a single-medium while the SRV region itself is described using a double-medium strategy which can account for thefluid exchange between the matrix and the micro-fractures.The shale gas adsorption,desorption,diffusion,gas slippage effect,fracture stress sensitivity,and capillary imbibition have been considered.The shale gas production,pore pressure distribution and water saturation distribution in the reservoir have been simulated.The influences of hydraulic fracture geometry and nonorthogonal hydraulic fractures on gas production have been determined and discussed accordingly.The simulation results show that the daily gas production has an upward and downward trend due to the presence of a large amount of fracturingfluid in the reservoir around the hydraulic fracture.The smaller the angle between the hydraulic fracture and the wellbore,the faster the daily production of shale gas wells decreases,and the lower the cumulative production.Nonplanar fractures can increase the control volume of hydraulic fractures and improve the production of shale gas wells.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Arbitrary High-Order Fully-Decoupled Numerical Schemes for Phase-Field Models of Two-Phase Incompressible Flows

Due to the coupling between the hydrodynamic equation and the phase-field equation in two-phase incompressible flows,it is desirable to develop efficient and high-order accurate numerical schemes that can decouple these two equations.One popular and efficient strategy is to add an explicit stabilizing term to the convective velocity in the phase-field equation to decouple them.The resulting schemes are only first-order accurate in time,and it seems extremely difficult to generalize the idea of stabilization to the second-order or higher version.In this paper,we employ the spectral deferred correction method to improve the temporal accuracy,based on the first-order decoupled and energy-stable scheme constructed by the stabilization idea.The novelty lies in how the decoupling and linear implicit properties are maintained to improve the efficiency.Within the framework of the spatially discretized local discontinuous Galerkin method,the resulting numerical schemes are fully decoupled,efficient,and high-order accurate in both time and space.Numerical experiments are performed to validate the high-order accuracy and efficiency of the methods for solving phase-field models of two-phase incompressible flows.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Identifying the enhancement mechanism of Al/MoO_(3) reactive multilayered films on the ignition ability of semiconductor bridge using a one-dimensional gas-solid two-phase flow model

Energetic Semiconductor bridge(ESCB)based on reactive multilayered films(RMFs)has a promising application in the miniature and intelligence of initiator and pyrotechnics device.Understanding the ignition enhancement mechanism of RMFs on semiconductor bridge(SCB)during the ignition process is crucial for the engineering and practical application of advanced initiator and pyrotechnics devices.In this study,a one-dimensional(1D)gas-solid two-phase flow ignition model was established to study the ignition process of ESCB to charge particles based on the reactivity of Al/MoO_(3) RMFs.In order to fully consider the coupled exothermic between the RMFs and the SCB plasma during the ignition process,the heat release of chemical reaction in RMFs was used as an internal heat source in this model.It is found that the exothermal reaction in RMFs improved the ignition performance of SCB.In the process of plasma rapid condensation with heat release,the product of RMFs enhanced the heat transfer process between the gas phase and the solid charge particle,which accelerated the expansion of hot plasma,and heated the solid charge particle as well as gas phase region with low temperature.In addition,it made up for pressure loss in the gas phase.During the plasma dissipation process,the exothermal chemical reaction in RMFs acted as the main heating source to heat the charge particle,making the surface temperature of the charge particle,gas pressure,and gas temperature rise continuously.This result may yield significant advantages in providing a universal ignition model for miniaturized ignition devices.

A proposal for detecting weak electromagnetic waves around 2.6μm wavelength with Sr optical clock

Infrared signal detection is widely used in many fields.Due to the detection principle,however,the accuracy and range of detection are limited.Thanks to the ultra stability of the^(87)Sr optical lattice clock,external infrared electromagnetic wave disturbances can be responded to.Utilizing the ac Stark shift of the clock transition,we propose a new method to detect infrared signals.According to our calculations,the theoretical detection accuracy in the vicinity of its resonance band of 2.6μm can reach the order of 10-14W,while the minimum detectable signal of common detectors is on the order of 10^(-10)W.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

Numerical modeling and parametric sensitivity analysis of heat transfer and two-phase oil and water flow characteristics in horizontal and inclined flowlines using OpenFOAM

Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.

A two-phase type-curve method with multiscale fluid transport mechanisms in hydraulically fractured shale reservoirs

The quantitative understanding of hydraulic fracture(HF)properties guides accurate production forecasts and reserve estimation.Type curve is a powerful technique to characterize HF and reservoir properties from flowback and long-term production data.However,two-phase flow of water and hydrocarbon after an HF stimulation together with the complex transport mechanisms in shale nanopores exacerbate the nonlinearity of the transport equation,causing errors in type-curve analysis.Accordingly,we propose a new two-phase type-curve method to estimate HF properties,such as HF volume and permeability of fracture,through the analysis of flowback data of multi-fractured shale wells.The proposed type curve is based on a semianalytical solution that couples the two-phase flow from the matrix with the flow in HF by incorporating matrix influx,slippage effect,stress dependence,and the spatial variation of fluid properties in inorganic and organic pores.For the first time,multiple fluid transport mechanisms are considered into two-phase type-curve analysis for shale reservoirs.We analyze the flowback data from a multi-fractured horizontal well in a shale gas reservoir to verify the field application of the proposed method.The results show that the fracture properties calculated by the type-curve method are in good agreement with the long-time production data.

Pore-Scale Investigation of Coupled Two-Phase and Reactive Transport in the Cathode Electrode of Proton Exchange Membrane Fuel Cells

A three-dimensional multicomponent multiphase lattice Boltzmann model(LBM)is established to model the coupled two-phase and reactive transport phenomena in the cathode electrode of proton exchange membrane fuel cells.The gas diff usion layer(GDL)and microporous layer(MPL)are stochastically reconstructed with the inside dynamic distribution of oxygen and liquid water resolved,and the catalyst layer is simplifi ed as a superthin layer to address the electrochemical reaction,which provides a clear description of the fl ooding eff ect on mass transport and performance.Diff erent kinds of electrodes are reconstructed to determine the optimum porosity and structure design of the GDL and MPL by comparing the transport resistance and per-formance under the fl ooding condition.The simulation results show that gradient porosity GDL helps to increase the reactive area and average concentration under fl ooding.The presence of the MPL ensures the oxygen transport space and reaction area because liquid water cannot transport through micropores.Moreover,the MPL helps in the uniform distribution of oxygen for an effi cient in-plane transport capacity.Crack and perforation structures can accelerate the water transport in the assembly.The systematic perforation design yields the best performance under fl ooding by separating the transport of liquid water and oxygen.

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes.

Detecting While Accessing:A Semi-Supervised Learning-Based Approach for Malicious Traffic Detection in Internet of Things

In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.

Study on two-phase cloud dispersion from liquefied CO_(2) release

The knowledge of two-phase cloud dispersion mechanism from HLG(hazardous liquefied gas) release is the prerequisite for accurate assessment and precise rescue of such accidents. In this paper, an experiment of two-phase cloud dispersion from liquefied CO_(2) hole release is performed. The source terms, such as vapour mass fraction, release velocity and mean droplet diameter, are calculated based on thermodynamic theory. Taking phase transition of CO_(2) droplets to gas into account, CFD(computational fluid dynamics) model for two-phase cloud dispersion is established. The predicted cloud temperatures at the downstream agree well with the experimental data, with the maximum relative error of 5.8% and average relative error of 2.3%. The consequence distances in the downstream direction and in the crosswise direction calculated through two-phase model are larger than those through single-phase model,with the relative differences of 57.8% and 53.6% respectively. CO_(2) concentration calculated by twophase model is smaller in the vicinity of release hole, and larger beyond 0.135 m downstream. A smaller leakage rate results in a lower CO_(2) concentration and a higher cloud temperature.

Liquid-liquid two-phase flow in a wire-embedded concentric microchannel: Flow pattern and mass transfer performance

In this work, flow pattern and mass transfer of liquid-liquid two-phase flow in a wire-embedded concentric microchannel are studied using toluene-water system. Droplet flow, slug flow, oval flow and annular flow are observed in the wire-embedded concentric microchannel. The effects of embedded wires and physical properties on flow patterns are investigated. The embedded wire insert is conducive to the formation of annular flow. The flow pattern distribution regions are distinguished by the Caaq(capillary number)±We_(org)(Weber number) flow pattern map. When Weorg<0.001, slug flow is the main flow pattern, and when Weorg>0.1, annular flow is the main flow pattern. Oval flow and droplet flow are between We_(org)= 0.001-0.1, and oval flow is transformed into droplet flow with the increase of Caaq. The effect of flow rate, phase ratio, initial acetic acid concentration, insert shape and flow patterns on mass transfers are studied. Mass transfer process is enhanced under annular flow conditions, the volumetric mass transfer coefficient is up to 0.36 s^(-1) because of the high interfacial area and interface renewal rate of annular flow.

Simulation of gas–liquid two-phase flow in a flow-focusing microchannel with the lattice Boltzmann method

A lattice Boltzmann method for gas–liquid two-phase flow involving non-Newtonian fluids is developed. Bubble formation in a flow-focusing microchannel is simulated by the method. The influences of flow rate ratio, surface tension,wetting properties, and rheological characteristics of the fluid on the two-phase flow are analyzed. The results indicate that the flow pattern transfers from slug flow to dry-plug flow with a sufficiently small capillary number. Due to the presence of three-phase contact lines, the contact angle has a more significant effect on the dry-plug flow pattern than on the slug flow pattern. The deformation of the front and rear meniscus of a bubble in the shear-thinning fluid can be explained by the variation of the capillary number. The reduced viscosity and increased contact angle are beneficial for the drag reduction in a microchannel. It also demonstrates the effectiveness of the current method to simulate the gas–liquid two-phase flow in a microchannel.

Comprehensive modeling of frictional pressure drop during carbon dioxide two-phase flow inside channels using intelligent and conventional methods

Environmentally friendly nature of CO_(2),associated with its safety and high efficiency,has made it a widely used working fluid in heat exchangers.Since CO_(2)has strange thermophysical features,specific models are required to estimate its two-phase characteristics,particularly frictional pressure drop(FPD).Herein,a widespread dataset,comprising 1195 experimental samples for two-phase FPD of CO_(2)was adopted from 10 sources to fulfill this requirement.The literature correlations failed to provide satisfactory precisions and exhibited the average absolute relative errors(AAREs)between 29.29% and 67.69% from the analyzed data.By inspiring the theoretical method of Lockhart and Martinelli,three intelligent FPD models were presented,among which the Gaussian process regression approach surpassed the others with AARE and R^(2)values of 5.48% and 98.80%,respectively in the test stage.A novel simple correlation was also derived based on the least square fitting method,which yielded opportune predictions with AARE of 19.76% for all data.The truthfulness of the newly proposed models was assessed through a variety of statistical and visual analyses,and the results affirmed their high reliability over a broad range of conditions,channel sizes and flow patterns.Furthermore,the novel models performed favorably in describing the physical attitudes corresponding to two-phase FPD of CO_(2).Eventually,the importance of operating factors in controlling the FPD was discussed through a sensitivity analysis.

Characterisation and separation of infectious bursal disease virus-like particles using aqueous two-phase systems

Infectious bursal disease(IBD)causes considerable economic losses in the commercial poultry industry worldwide.The principal way to control IBD virus(IBDV),the causative agent of IBD,is still through vaccination programs.Virus-like particles(VLPs)are recognised as a safe and potent recombinant vaccine platform.This research work explores the characterisation and separation of infectious bursal disease virus-like particles(IBD-VLPs)from crude feedstock.Various characteristics were studied with highperformance size-exclusion chromatography(HP-SEC),sodium dodecyl sulphate–polyacrylamide gel electrophoresis(SDS-PAGE)and transmission electron microscopy(TEM)analyses.Subsequently,the separation of IBD-VLPs using polyethylene glycol(PEG)/sodium citrate-based aqueous two-phase systems(ATPSs)was conducted and optimised.Moreover,a scale-up study of the best ATPS constituted of 15%PEG 6000,11%sodium citrate and 10%crude feedstock was performed to compare the separation performance of IBD-VLPs with and without centrifugation-assisted.The results indicated that the optimised ATPS with centrifugation-assisted for both 5 g and 50 g systems showed good recovery of IBDVLPs of>97%in the interphase between the PEG-rich top and salt-rich bottom phases.These optimised systems also showed high removal efficiencies of impurities of>95%.The results demonstrated that aqueous two-phase extraction could be a promising technology for efficient VLPs separation.

Experimental Investigation of Regular or Wavy Two-Phase Flow in a Manifold

An experimental study was conducted to investigate the properties of stratified regular or wavy two-phase flow in two parallel separators located after a manifold.A total of 103 experiments with various gas and liquid velocity combinations in three inlet pipes were conducted,including 77 groups of outlet pipe resistance symmetry and 26 groups of outlet pipe resistance asymmetry trials.The experimental results have revealed that when the gas-liquid flow rate is low,the degree of uneven splitting is high,and“extreme”conditions are attained.When the superficial gas velocity is greater than that established in the extreme case,the direction of the liquid-phase displacement is reversed,while that of the gas remains unchanged.Thus,the degree of gas phase bias tends to be mitigated with an increase in the gas velocity,while the uneven splitting degree of liquid approaches 10%.Finally,varying the gas-phase outlet pipe resistance is shown to effectively change the gas-liquid two-phase flow distribution.