Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

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.

Progress and prospect of mid-deep geothermal reinjection technology

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.

Study on the geothermal production and reinjection mode in Xiong County

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.

Formation blockage risk analysis of geothermal water reinjection:Rock property analysis,pumping and reinjection tests,and long-term reinjection prediction

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.

Clogging mechanism in the process of reinjection of used geothermal water: A simulation research on Xianyang No.2 reinjection well in a super-deep and porous geothermal reservoir

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.

An Experimental Study on Bio-Clogging in Porous Media during Geothermal Water Reinjection

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. Pseudomonas and Devosia are respectively the dominant bacteria in the early and late stages of geothermal water reinjection.

Reinjection Tests in Bedrock Geothermal Reservoir of Tianjin

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.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

A New Method for the Dynamic Reserves of Gas Condensate Reservoir Using Cyclic Gas Injection Based on the Effects of Reinjection Ratio and Water Influx

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%.

EGSNet:An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

Multi-level access control model for tree-like hierarchical organizations

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.

新型化学回注沼气分布式供能系统性能研究

在传统化学回热分布式供能系统中,汽化水需要消耗大量中高温烟气余热,降低了系统的制冷潜力,大量中高温烟气余热将贬值为水的低温潜热,为此,该文提出一种新型化学回注沼气分布式供能系统。将部分烟气回注重整器为甲烷重整反应提供大量的氧化剂(O_(2)、H_(2)O和CO_(2))和热量,重整器内部发生甲烷三重整反应,通过回热空气有效回收另一部分烟气热量。在设计工况下,新系统提高122.40 kW(13.17%)的发电量和538.85 kW(160.52%)的制冷量,(火用)效率提高16.90%。化学回热系统在为沼气池保温方面更具有优势,新系统与化学回热系统所能满足的最低环境温度分别为9.90和7.44℃,当环境温度为7.44℃时,与化学回热系统相比,新系统(火用)效率仍能提高7.41%。研究结果可为沼气在分布式供能领域的运用提供一种新的技术方案。

Shape retrieval using multi-level included angle functions-based Fourier descriptor

An effective shape signature namely multi-level included angle functions MIAFs is proposed to describe the hierarchy information ranging from global information to local variations of shape.Invariance to rotation translation and scaling are the intrinsic properties of the MIAFs.For each contour point the multi-level included angles are obtained based on the paired line segments derived from unequal-arc-length partitions of contour.And a Fourier descriptor derived from multi-level included angle functions MIAFD is presented for efficient shape retrieval.The proposed descriptor is evaluated with the standard performance evaluation method on three shape image databases the MPEG-7 database the Kimia-99 database and the Swedish leaf database. The experimental results of shape retrieval indicate that the MIAFD outperforms the existing Fourier descriptors and has low computational complexity.And the comparison of the MIAFD with other shape description methods also shows that the proposed descriptor has the highest precision at the same recall value which verifies its effectiveness.

Seasonal Dynamics of Nitrogen and Phosphorus in Water and Sediment of A Multi-level Ditch System in Sanjiang Plain,Northeast China

The multi-level ditch system developed in the Sanjiang Plain,Northeast China has sped up water drainage process hence transferred more pollutants from farmlands into the rivers of this region.Understanding the seasonal dynamics of nitrogen (N) and phosphorus (P) transportation in the ditch system and the role of different ditch size is thus crucial for water pollution control of the rivers in the Sanjiang Plain.In this study,an investigation was conducted in the Nongjiang watershed of the Sanjiang Plain to study the nutrient variation and the correlation between water and sediments in the ditch system in terms of ditch level.Water and sediments samples were collected in each ditch level in growing season at regular intervals (once per month),and TN,NO 3--N,NH 4+-N,TP,and PO 4 3--P were analyzed.The results show that nutrient contents in water were higher in June and July,especially in July,the contents of TN and TP were 3.21mg/L and 0.84mg/L in field ditch,4.04mg/L and 1.06mg/L in lateral ditch,2.46mg/L and 0.70mg/L in branch ditch,1.92mg/L and 0.63mg/L in main ditch,respectively.In August and September,the nutrient contents in the water were relatively lower.The peak value of nutrient in ditch water had been moving from the field ditch to the main ditch over time,showing a remarkable impact of ditch system on river water environment.The nutrient transfer in ditch sediments could only be found in rainfall season.Nutrient contents in ditch sediment had effect on that in ditch water,but nutrients in ditch water and sediments had different origination.Ditch management in terms of the key fac-tors is hence very important for protecting river water environment.