The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated....The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.展开更多
The large karst geothermal field of Xiong County,which is located to the south of geothermal field in Niutuozhen,features huge geothermal resources and favorable condition for development and utilization.Because of th...The large karst geothermal field of Xiong County,which is located to the south of geothermal field in Niutuozhen,features huge geothermal resources and favorable condition for development and utilization.Because of the long-term extensive production,the pressure of geothermal reservoir continues to decline and some geothermal wells even face the danger of scrapping.To relieve the fall in pressure of geothermal reservoir and achieve the sustainable development and utilization of geothermal field in the long run,reinjection experiment is conducted in the geothermal field of Xiong County and a three-dimensional hydrothermal coupled numerical model was constructed.The reinjection experiment showed that the mode of one production well and one reinjection well can be achieved in this geothermal field.The numerical simulation is used to forecast and compare the change in pressure field and temperature field under different production and reinjection modes and concludes that the most opotimized production and utilization mode is the concentrated production-reinjection mode,and the most opotimized production-reinjection combination mode is the shallow production and shallow reinjection mode which can ensure the sustainable development and utilization of geothermal resources in the long run.展开更多
The blockage induced by particle migration and deposition is one of the main reasons for the decrease of reinjection capacity in the porous geothermal reservoir with a low and medium temperature.In this paper,a new dr...The blockage induced by particle migration and deposition is one of the main reasons for the decrease of reinjection capacity in the porous geothermal reservoir with a low and medium temperature.In this paper,a new drilled geothermal well in Xining basin China is taken as an example to investigate the formation blockage risk due to the movable clay and sand particles in pores.The physical properties of the reservoir rocks were analyzed,a series of pumping and reinjection tests were conducted,and the longterm reinjection performance of the well was predicted by numerical simulation based on the test fitting.The results show that the geothermal reservoir rocks are argillaceous and weakly cemented sandstones with a content of movable clay and sand particles up to 0.18–23.42 wt.%.The well presented a high productivity of 935–2186 m3?d-1 at a pressure difference of 0.7–1.62 MPa in the pumping tests associated with a large amount of clay and sand particles produced out,while in the reinjection test,only a low injectivity of 240–480 m3?d-1 was observed at an injection pressure of 0.2–0.6 MPa with the clay and sand particles near the wellbore move into deep.According to the prediction,under conditions of a blockage risk,the injectivity of the well will start to decline after 100 days of injection,and in the third year,it will decrease by 59.00%–77.09%.The influence of invasion of pretreated suspended particles and scale particles can be neglected.Under conditions of a high blockage risk,the injectivity of the well will decrease significantly in the first 20–30 days,with a decline of 75.39%–78.96%.Generally,the higher the injection pressure or rate,the greater the decrease in injectivity of the well caused by particle blockage.Pump lifting is an effective measure to remove the well blockage which can be used regularly.展开更多
In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide p...In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide problem, which greatly restricts the exploitation and utilization of geothermal resources. Based on a large amount of experiments and researches, the reinjection research on the tail water of Xianyang No.2 well, which is carried out by combining the application of hydrogeochemical simulation, clogging mechanism research and the reinjection experiment, has achieved breakthrough results. The clogging mechanism and indoor simulation experiment results show: Factors affecting the tail water reinjection of Xianyang No.2 well mainly include chemical clogging, suspended solids clogging, gas clogging, microbial clogging and composite clogging, yet the effect of particle migration on clogging has not been found; in the process of reinjection, chemical clogging was mainly caused by carbonates(mainly calcite), silicates(mainly chalcedony), and a small amount of iron minerals, and the clogging aggravated when the temperature rose; suspended solids clogging also aggravated when the temperature rose, which showed that particles formed by chemical reaction had a certain proportion in suspended solids.展开更多
To study the mechanism of bio-clogging in a porous medium during the reinjection of geothermal water and to improve reinjection efficiency, an indoor one-dimensional reinjection experiment was conducted based on the g...To study the mechanism of bio-clogging in a porous medium during the reinjection of geothermal water and to improve reinjection efficiency, an indoor one-dimensional reinjection experiment was conducted based on the geological model of the geothermal reinjection demonstration project in Dezhou City. The biological process of porous media clogging was investigated by analyzing the variation of permeability within the medium, the main indexes of nutrient salts, and the content of extracellular polymeric substances (EPS). High-throughput sequencing, based on 16S rRNA, was used to analyze the characteristics and succession of microbial communities during the reinjection of geothermal water. The results of the study show that significant bio-clogging occurs during the reinjection of geothermal water, with an increase in the heterogeneity of the thermal reservoir medium, and a decrease in permeability. The extent of clogging gradually reduces with an increase in seepage path. Thus, thermal reservoir clogging is more serious closer to the water inlet. With an increase in the duration of reinjection, the permeability of the porous medium undergoes three stages: “rapid”, “decline-slow”, and “decrease-stable”. The results show that the richness and diversity of the bacterial community increase and decrease, respectively, during the reinjection process. Bacterial community succession occurs, and the bacterial communities mainly include the Proteobacteria and Bacteroidetes phyla. <em>Pseudomonas</em> and <em>Devosia</em> are respectively the dominant bacteria in the early and late stages of geothermal water reinjection.展开更多
By the end of 2002, there are about 219 production wells (including 12 reinjection wells) in Tianjin. The annual production rate is 1.5×10 7 m 3 and the reinjection rate is 1.66×10 6 m 3. The main side effec...By the end of 2002, there are about 219 production wells (including 12 reinjection wells) in Tianjin. The annual production rate is 1.5×10 7 m 3 and the reinjection rate is 1.66×10 6 m 3. The main side effect anticipated from reinjection is the cooling of the reservoir. It is necessary to estimate the thermal breakthrough time in different distances between injection production wells. This paper describes the 2 D mass and heat transfer in the heterogeneous fractured rocks. The equations that arise for each grid block must be linearized. The main reinjection model is simulated by a program of the TOUGH2 to analyze the change of the temperature field and predict the pressure and heat break through. The tracer test is very important for understanding the transportation pathway and transport channel/space in the doublet system, and estimating the possible cooling resulted from the injection processes.展开更多
As we all know, cyclic gas injection is one of the most effective development methods to improve condensate oil recovery. When dealing with the calculation of the reserves, the injection-production differences and wat...As we all know, cyclic gas injection is one of the most effective development methods to improve condensate oil recovery. When dealing with the calculation of the reserves, the injection-production differences and water influx create great influence on the accuracy. Based on the existing research, we proposed a new material balance equation which considered the differences of composition between produced and injected fluids and the effect of water influx, and a solution was provided in this paper. The results of the method are closer to the actual situation because they are built on the law of conservation of mass, and the using of curve fitting method can not only avoid the use of water influx coefficient but also obtain the water influx rate and reserves at the same time. The YH-23 gas condensate reservoir is taking as a typical subject to do the research, which has been exploited by cycle gas injection for 14 years. Three different methods are used to calculate the reserves, and the results show that the method proposed in this paper has minimum error of 2.96%.展开更多
This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions,which is crucial for the sustainable geother...This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions,which is crucial for the sustainable geothermal energy management.With advancing exploitation of geothermal resources deepens,precise understanding of this mechanism becomes paramount for devising effective reinjection strategies,optimizing reservoir utilization,and bolstering the economic viability of geothermal energy development.The article presents a comprehensive review of temperature field evolution across diverse heterogeneous thermal reservoirs under low-temperature tailwater reinjection conditions,and analyzes key factors influ-encing this evolution.It evaluates existing research methods,highlighting their strengths and limitations.The study identifies gaps in the application of rock seepage and heat transfer theories on a large scale,alongside the need for enhanced accuracy in field test results,particularly regarding computational effi-ciency of fractured thermal reservoir models under multi-well reinjection conditions.To address these shortcomings,the study proposes conducting large-scale rock seepage and heat transfer experiments,coupled with multi-tracer techniques for field testing,aimed at optimizing fractured thermal reservoir models'computational efficiency under multi-well reinjection conditions.Additionally,it suggests integrat-ing deep learning methods into research endeavors.These initiatives are of significance in deepening the understanding of the evolution process of the temperature field in deep thermal reservoirs and enhancing the sustainability of deep geothermal resource development.展开更多
Mid-deep geothermal reinjection technology is crucial for the sustainable development of geothermal resources,which has garnered significant attention and rapid growth in recent years.Currently,various geothermal rein...Mid-deep geothermal reinjection technology is crucial for the sustainable development of geothermal resources,which has garnered significant attention and rapid growth in recent years.Currently,various geothermal reinjection technologies lag behind,lacking effective integration to address issues like low reinjection rates and thermal breakthrough.This paper reviews the basic principles and development history of mid-deep geothermal reinjection technology,focusing on various technical methods used in the process and analyzing their applicability,advantages,and disadvantages under different geological conditions.It highlights the unique challenges posed by deep geothermal resources,including high temperature,high pressure,high stress,chemical corrosion,and complex geological structures.Additionally,it addresses challenges in equipment selection and durability,system stability and operation safety,environmental impact,and sustainable development.Finally,the paper explores future directions for mid-deep geothermal reinjection technology,highlighting key areas for further research and potential pathways for technological innovation.This comprehensive analysis aims to accelerate the advancement of geothermal reinjection technology,offering essential guidance for the efficient reinjection and sustainable development of geothermal resources.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning secur...An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.展开更多
基金supported by the National Natural Science Foundation of China(No.52090041).
文摘The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.
基金supported by Study on the Sustainable Development and Utilization of Geothermal Resource of Xiong County in Niutuozhen Geothermal Field,North China
文摘The large karst geothermal field of Xiong County,which is located to the south of geothermal field in Niutuozhen,features huge geothermal resources and favorable condition for development and utilization.Because of the long-term extensive production,the pressure of geothermal reservoir continues to decline and some geothermal wells even face the danger of scrapping.To relieve the fall in pressure of geothermal reservoir and achieve the sustainable development and utilization of geothermal field in the long run,reinjection experiment is conducted in the geothermal field of Xiong County and a three-dimensional hydrothermal coupled numerical model was constructed.The reinjection experiment showed that the mode of one production well and one reinjection well can be achieved in this geothermal field.The numerical simulation is used to forecast and compare the change in pressure field and temperature field under different production and reinjection modes and concludes that the most opotimized production and utilization mode is the concentrated production-reinjection mode,and the most opotimized production-reinjection combination mode is the shallow production and shallow reinjection mode which can ensure the sustainable development and utilization of geothermal resources in the long run.
基金supported by the Basic Research Program Project of Qinghai Province(No.2020-ZJ-758)the Special Fund on the Exploration of Clean Energy and Mineral Products in Qinghai Province(20181317146sh 007)partially financed by the General Project of Shandong Natural Science Foundation(ZR2020ME090)。
文摘The blockage induced by particle migration and deposition is one of the main reasons for the decrease of reinjection capacity in the porous geothermal reservoir with a low and medium temperature.In this paper,a new drilled geothermal well in Xining basin China is taken as an example to investigate the formation blockage risk due to the movable clay and sand particles in pores.The physical properties of the reservoir rocks were analyzed,a series of pumping and reinjection tests were conducted,and the longterm reinjection performance of the well was predicted by numerical simulation based on the test fitting.The results show that the geothermal reservoir rocks are argillaceous and weakly cemented sandstones with a content of movable clay and sand particles up to 0.18–23.42 wt.%.The well presented a high productivity of 935–2186 m3?d-1 at a pressure difference of 0.7–1.62 MPa in the pumping tests associated with a large amount of clay and sand particles produced out,while in the reinjection test,only a low injectivity of 240–480 m3?d-1 was observed at an injection pressure of 0.2–0.6 MPa with the clay and sand particles near the wellbore move into deep.According to the prediction,under conditions of a blockage risk,the injectivity of the well will start to decline after 100 days of injection,and in the third year,it will decrease by 59.00%–77.09%.The influence of invasion of pretreated suspended particles and scale particles can be neglected.Under conditions of a high blockage risk,the injectivity of the well will decrease significantly in the first 20–30 days,with a decline of 75.39%–78.96%.Generally,the higher the injection pressure or rate,the greater the decrease in injectivity of the well caused by particle blockage.Pump lifting is an effective measure to remove the well blockage which can be used regularly.
基金funded by National Science Foundation Project in 2015 (No.41472221)
文摘In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide problem, which greatly restricts the exploitation and utilization of geothermal resources. Based on a large amount of experiments and researches, the reinjection research on the tail water of Xianyang No.2 well, which is carried out by combining the application of hydrogeochemical simulation, clogging mechanism research and the reinjection experiment, has achieved breakthrough results. The clogging mechanism and indoor simulation experiment results show: Factors affecting the tail water reinjection of Xianyang No.2 well mainly include chemical clogging, suspended solids clogging, gas clogging, microbial clogging and composite clogging, yet the effect of particle migration on clogging has not been found; in the process of reinjection, chemical clogging was mainly caused by carbonates(mainly calcite), silicates(mainly chalcedony), and a small amount of iron minerals, and the clogging aggravated when the temperature rose; suspended solids clogging also aggravated when the temperature rose, which showed that particles formed by chemical reaction had a certain proportion in suspended solids.
文摘To study the mechanism of bio-clogging in a porous medium during the reinjection of geothermal water and to improve reinjection efficiency, an indoor one-dimensional reinjection experiment was conducted based on the geological model of the geothermal reinjection demonstration project in Dezhou City. The biological process of porous media clogging was investigated by analyzing the variation of permeability within the medium, the main indexes of nutrient salts, and the content of extracellular polymeric substances (EPS). High-throughput sequencing, based on 16S rRNA, was used to analyze the characteristics and succession of microbial communities during the reinjection of geothermal water. The results of the study show that significant bio-clogging occurs during the reinjection of geothermal water, with an increase in the heterogeneity of the thermal reservoir medium, and a decrease in permeability. The extent of clogging gradually reduces with an increase in seepage path. Thus, thermal reservoir clogging is more serious closer to the water inlet. With an increase in the duration of reinjection, the permeability of the porous medium undergoes three stages: “rapid”, “decline-slow”, and “decrease-stable”. The results show that the richness and diversity of the bacterial community increase and decrease, respectively, during the reinjection process. Bacterial community succession occurs, and the bacterial communities mainly include the Proteobacteria and Bacteroidetes phyla. <em>Pseudomonas</em> and <em>Devosia</em> are respectively the dominant bacteria in the early and late stages of geothermal water reinjection.
文摘By the end of 2002, there are about 219 production wells (including 12 reinjection wells) in Tianjin. The annual production rate is 1.5×10 7 m 3 and the reinjection rate is 1.66×10 6 m 3. The main side effect anticipated from reinjection is the cooling of the reservoir. It is necessary to estimate the thermal breakthrough time in different distances between injection production wells. This paper describes the 2 D mass and heat transfer in the heterogeneous fractured rocks. The equations that arise for each grid block must be linearized. The main reinjection model is simulated by a program of the TOUGH2 to analyze the change of the temperature field and predict the pressure and heat break through. The tracer test is very important for understanding the transportation pathway and transport channel/space in the doublet system, and estimating the possible cooling resulted from the injection processes.
文摘As we all know, cyclic gas injection is one of the most effective development methods to improve condensate oil recovery. When dealing with the calculation of the reserves, the injection-production differences and water influx create great influence on the accuracy. Based on the existing research, we proposed a new material balance equation which considered the differences of composition between produced and injected fluids and the effect of water influx, and a solution was provided in this paper. The results of the method are closer to the actual situation because they are built on the law of conservation of mass, and the using of curve fitting method can not only avoid the use of water influx coefficient but also obtain the water influx rate and reserves at the same time. The YH-23 gas condensate reservoir is taking as a typical subject to do the research, which has been exploited by cycle gas injection for 14 years. Three different methods are used to calculate the reserves, and the results show that the method proposed in this paper has minimum error of 2.96%.
基金funded by the National Nature Science Foundation of China(No.42272350)Scientific research project of Hunan Institute of Geology(No.HNGSTP202211)+2 种基金Hunan Province key research and development project(No.2022SK2070)Geological survey project of Department of Natural Resources of Shanxi Province(No.Jinfencai[2021-0009]G009-C05)the Foundation of Shanxi Key Laboratory for Exploration and Exploitation of Geothermal Resources(No.SX202202).
文摘This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions,which is crucial for the sustainable geothermal energy management.With advancing exploitation of geothermal resources deepens,precise understanding of this mechanism becomes paramount for devising effective reinjection strategies,optimizing reservoir utilization,and bolstering the economic viability of geothermal energy development.The article presents a comprehensive review of temperature field evolution across diverse heterogeneous thermal reservoirs under low-temperature tailwater reinjection conditions,and analyzes key factors influ-encing this evolution.It evaluates existing research methods,highlighting their strengths and limitations.The study identifies gaps in the application of rock seepage and heat transfer theories on a large scale,alongside the need for enhanced accuracy in field test results,particularly regarding computational effi-ciency of fractured thermal reservoir models under multi-well reinjection conditions.To address these shortcomings,the study proposes conducting large-scale rock seepage and heat transfer experiments,coupled with multi-tracer techniques for field testing,aimed at optimizing fractured thermal reservoir models'computational efficiency under multi-well reinjection conditions.Additionally,it suggests integrat-ing deep learning methods into research endeavors.These initiatives are of significance in deepening the understanding of the evolution process of the temperature field in deep thermal reservoirs and enhancing the sustainability of deep geothermal resource development.
基金funded by the National Nature Science Foundation of China(No.42272350)Hunan Provincial Key R&D Program(2022SK 2070)the Foundation of Shanxi Key Laboratory for Exploration and Exploitation of Geothermal Resources(No.SX202202).
文摘Mid-deep geothermal reinjection technology is crucial for the sustainable development of geothermal resources,which has garnered significant attention and rapid growth in recent years.Currently,various geothermal reinjection technologies lag behind,lacking effective integration to address issues like low reinjection rates and thermal breakthrough.This paper reviews the basic principles and development history of mid-deep geothermal reinjection technology,focusing on various technical methods used in the process and analyzing their applicability,advantages,and disadvantages under different geological conditions.It highlights the unique challenges posed by deep geothermal resources,including high temperature,high pressure,high stress,chemical corrosion,and complex geological structures.Additionally,it addresses challenges in equipment selection and durability,system stability and operation safety,environmental impact,and sustainable development.Finally,the paper explores future directions for mid-deep geothermal reinjection technology,highlighting key areas for further research and potential pathways for technological innovation.This comprehensive analysis aims to accelerate the advancement of geothermal reinjection technology,offering essential guidance for the efficient reinjection and sustainable development of geothermal resources.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金The National Natural Science Foundation of China(No.60403027,60773191,70771043)the National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z403)
文摘An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.
