Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Application of Dynamic Programming Method in Optimization of Water Distribution Pipe Network

With a water-supply network by dynamic programming. The minimal as an example, the network was optimized annual discounted costs were taken as an objective function and node pressure etc. as constraint conditions. The alternative pipe diameters were optimized as per enumeration method and the group allowing objective function with the least values would be the optimized one. It is proved the optimized pipe network reduced by 11.49% in terms of cost and the optimized ben- efits proved much significant.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Mechanical properties and acoustic emission characteristics of soft rock with different water contents under dynamic disturbance

Uniaxial compression tests and cyclic loading acoustic emission tests were conducted on 20%,40%,60%,80%,dry and saturated muddy sandstone by using a creep impact loading system to investigate the mechanical properties and acoustic emission characteristics of soft rocks with different water contents under dynamic disturbance.The mechanical properties and acoustic emission characteristics of muddy sandstones at different water contents were analysed.Results of experimental studies show that water is a key factor in the mechanical properties of rocks,softening them,increasing their porosity,reducing their brittleness and increasing their plasticity.Under uniaxial compression,the macroscopic damage characteristics of the muddy sandstone change from mono-bevel shear damage and‘X’type conjugate bevel shear damage to a roadway bottom-drum type damage as the water content increases.Dynamic perturbation has a strengthening effect on the mechanical properties of samples with 60%and less water content,and a weakening effect on samples with 80%and more water content,but the weakening effect is not obvious.Macroscopic damage characteristics of dry samples remain unchanged,water samples from shear damage and tensile–shear composite damage gradually transformed into cleavage damage,until saturation transformation monoclinic shear damage.The evolution of acoustic emission energy and event number is mainly divided into four stages:loading stage(Ⅰ),dynamic loading stage(Ⅱ),yield failure stage(Ⅲ),and post-peak stage(Ⅳ),the acoustic emission characteristics of the stages were different for different water contents.The characteristic value of acoustic emission key point frequency gradually decreases,and the damage degree of the specimen increases,corresponding to low water content—high main frequency—low damage and high water content—low main frequency—high damage.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Effects of water tables and nitrogen application on soil bacterial community diversity, network structure, and function in an alpine wetland, China

Nitrogen deposition and water tables are important factors to control soil microbial community structure.However,the specific effects and mechanisms of nitrogen deposition and water tables coupling on bacterial diversity,abundance,and community structure in arid alpine wetlands remain unclear.The nitrogen deposition(0,10,and 20 kg N/(hm^(2)•a))experiments were conducted in the Bayinbulak alpine wetland with different water tables(perennial flooding,seasonal waterlogging,and perennial drying).The 16S rRNA(ribosomal ribonucleic acid)gene sequencing technology was employed to analyze the changes in bacterial community diversity,network structure,and function in the soil.Results indicated that bacterial diversity was the highest under seasonal waterlogging condition.However,nitrogen deposition only affected the bacterial Chao1 and beta diversity indices under seasonal waterlogging condition.The abundance of bacterial communities under different water tables showed significant differences at the phylum and genus levels.The dominant phylum,Proteobacteria,was sensitive to soil moisture and its abundance decreased with decreasing water tables.Although nitrogen deposition led to changes in bacterial abundance,such changes were small compared with the effects of water tables.Nitrogen deposition with 10 kg N/(hm^(2)•a)decreased bacterial edge number,average path length,and robustness.However,perennial flooding and drying conditions could simply resist environmental changes caused by 20 kg N/(hm^(2)•a)nitrogen deposition and their network structure remain unchanged.The sulfur cycle function was dominant under perennial flooding condition,and carbon and nitrogen cycle functions were dominant under seasonal waterlogging and perennial drying conditions.Nitrogen application increased the potential function of part of nitrogen cycle and decreased the potential function of sulfur cycle in bacterial community.In summary,composition of bacterial community in the arid alpine wetland was determined by water tables,and diversity of bacterial community was inhibited by a lower water table.Effect of nitrogen deposition on bacterial community structure and function depended on water tables.

Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core.The NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time.Recent advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship.In this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the NAS.The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)model.The forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,respectively.Finally,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency.Corrected by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

Simulation and Prediction for Groundwater Dynamics Based on RBF Neural Network

Based on MATLAB, a new model-BRF network model is founded to be used in groundwater dynamic simulation and prediction. It is systematically studied about the training sample set, testing sample set, the pretreatment of the original data, neural network construction, training, testing and evaluating the entire process. A favorable result is achieved by applying the model to simulate and predict groundwater dynamics, which shows this new method is precise and scientific.

Multi-head neural networks for simulating particle breakage dynamics

The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics.Unlike conventional methods,the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size,predicting values on arbitrarily fine grids without the need for model retraining.The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes.The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill.The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Flow Dynamics during the Hydrocarbon Exploitation for Prevention and Management of Water Venues in Oil Field: A Study Case of Crystal Field in Badila/Chad

The southern part of the Lake Chad basin is under the gas and oil petroleum industry due to its hydrocarbon potential for about twenty years. This project stands out as the main challenges of the hydrocarbon production and the management of fluxes particularly the groundwater venues. A comprehensive study is thus conducted to develop a dynamic and analytic model for diagnosing the production performances with a particular view on the management of groundwater venues. The three main concerned reservoirs subdivided on subunits evidence their proper characteristics. The porous media, their densities, the internal flows and the water injection techniques such as water flooding were thus adopted. The oil viscosity variability within the reservoirs creates different levels of mobility between water and oil, highlighting the challenges of water management. The material balance model and the behavior of the well analysis were taken in consideration within the identified aquifer, emphasizing the importance of keeping the pressure through injection. The control of water productions, the management of the reservoir, the well strategical position and the specific completions lead to the model functioning. In addition, the CO log and the Pulsed Neutron indicate their limitations as a result of the water salinity and the porosity of the aquifer. The management of groundwater venues at Badila requires various approaches throughout the lifetime of the Crystal field such as the data acquisition and remediation actions and prevention, under a permanent monitoring of the dynamic fluxes in the reservoirs.

Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease

BACKGROUND Diabetic kidney disease(DKD)is the primary cause of end-stage renal disease.The Astragalus-Coptis drug pair is frequently employed in the management of DKD.However,the precise molecular mechanism underlying its therapeutic effect remains elusive.AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.METHODS The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform.The targets were predicted using the SwissTargetPrediction database,while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database.DKD targets were acquired from the GeneCards,Online Mendelian Inheritance in Man database,and DisGeNET databases,with common targets identified through the Venny platform.The protein-protein interaction network and the“disease-active ingredient-target”network of the common targets were constructed utilizing the STRING database and Cytoscape software,followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients.Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichments were performed using the DAVID database.The tissue and organ distributions of key targets were evaluated.PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets.Finally,molecular dynamics(MD)simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified.There were 273 common targets between DKD and the Astragalus-Coptis drug pair.Through protein-protein interaction network topology analysis,we identified 9 core active ingredients and 10 key targets.GO and KEGG pathway enrichment analyses revealed that Astragalus-Coptis drug pair treatment for DKD involves various biological processes,including protein phosphorylation,negative regulation of apoptosis,inflammatory response,and endoplasmic reticulum unfolded protein response.These pathways are mainly associated with the advanced glycation end products(AGE)-receptor for AGE products signaling pathway in diabetic complications,as well as the Lipid and atherosclerosis.Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets.Notably,the quercetin-AKT serine/threonine kinase 1(AKT1)and quercetin-tumor necrosis factor(TNF)protein complexes exhibited exceptional stability.CONCLUSION This study demonstrated that DKD treatment with the Astragalus-Coptis drug pair involves multiple ingredients,targets,and signaling pathways.We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus-Coptis drug pair on DKD.Furthermore,we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF,providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Exploring the molecular mechanism of action of curcumin for the treatment of diabetic retinopathy,using network pharmacology,molecular docking,and molecular dynamics simulation

Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.