With the rapid development of Artificial Intelligence (AI) technology, the complex and critical field of oilfield reservoir characterization has welcomed innovative solutions. This paper explores the application of AI in reservoir characterization. First, it introduces the basic concepts of reservoir characterization and the challenges of traditional methods. Then, it delves into how AI technology optimizes data integration, enhances the accuracy of geological and geophysical data interpretation, predicts key reservoir parameters, constructs and simulates 3D geological models, conducts parameter sensitivity analysis and risk assessment, and provides decision support for real-time monitoring and optimization. Through multiple case studies, the paper demonstrates the significant impact of AI technology in improving the accuracy and efficiency of reservoir characterization. Finally, the paper looks forward to the future development trends and challenges of AI in reservoir characterization, aiming to provide valuable insights for research and practice in this field.

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With the rapid development of big data technology, the oil industry is embracing new opportunities for oil well optimization. This paper aims to explore the big data-driven oil well optimization technologies and their applications. First, the importance of big data technology in oil well optimization is outlined, with an introduction to methods of data collection and management, including the acquisition and processing of sensor data, production data, and geological data. Next, an in-depth analysis of data analysis techniques and optimization model construction is provided, focusing on the application of data mining, machine learning, and deep learning in production optimization, reservoir management, and predictive maintenance. Then, specific examples demonstrate the practical effectiveness of big data technology in real-time monitoring, production parameter optimization, reservoir development, cost control, and environmental monitoring. Finally, the paper discusses the challenges and future development directions in the process of big data-driven oil well optimization. Through a systematic exploration of these topics, this paper aims to provide valuable insights and practical references for the application of big data in the oil industry.

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With the continuous growth of global energy demand, shale oil, as a significant unconventional oil and gas resource, has attracted widespread attention. The evaluation of shale oil mobility plays a crucial role in its development, directly impacting the economic viability and development efficiency of the resource. This paper explores the latest research progress on shale oil mobility evaluation, focusing on various aspects from geological characteristics to integrated assessment methods. Firstly, it introduces the geological characteristics of shale oil reservoirs, including porosity, permeability, rock mechanical properties, and organic matter content. Then, it analyzes the physicochemical properties of shale oil fluids and their flow behavior in nanopores. Next, it discusses advanced mobility evaluation methods in detail, such as Nuclear Magnetic Resonance technology, X-ray Computed Tomography technology, microfluidics, and numerical simulations coupled with Computational Fluid Dynamics. Finally, the paper looks ahead to future research directions, highlighting the necessity of conducting innovative research in interdisciplinary fields to further improve the accuracy and efficiency of shale oil mobility evaluation. This paper aims to provide a valuable reference for scientific research and technological applications in shale oil development.

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Carbonated water flooding (CWF), as an emerging enhanced oil recovery technology, holds significant importance in oilfield development. This paper explores the basic principles and mechanisms of CWF, experimental and simulation studies, field applications, advantages, and challenges, as well as its technological development and future outlook. First, the definition and research background of CWF is introduced, highlighting its unique advantages in improving oil mobility and displacement efficiency. Second, the progress of experimental and simulation research both domestically and internationally is analyzed, demonstrating the adaptability and effectiveness of CWF under different geological conditions. Then, through multiple case studies, the effects of CWF in practical applications are discussed, summarizing the successful experiences and lessons learned from failures. Although CWF shows significant advantages in environmental protection and enhanced oil recovery, it still faces challenges in CO2 capture and transportation, formation reactions and corrosion issues, and economic feasibility. Finally, the development direction and industrial prospects of CWF technology are anticipated, with suggestions for further research and practical application. This paper aims to provide systematic reference and guidance for researchers and engineers in related fields, promoting the in-depth study and widespread application of CWF technology.

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The issue of sand production during oil extraction significantly impacts oilfield production efficiency and shortens equipment lifespan. To effectively address this problem, various sand control technologies in oil production have emerged and continuously evolved. This paper systematically introduces the basic concepts and mechanisms of sand control in oil extraction and provides a detailed overview of major technical methods, including mechanical sand control, chemical sand control, and integrated sand control, along with their applications. Additionally, the paper discusses the types and characteristics of materials used for sand control, summarizing the evaluation criteria and monitoring methods for sand control effectiveness. By analyzing typical cases of sand control in oilfields, it explores the selection and effectiveness of sand control technologies under different reservoir conditions. Finally, the paper identifies the current challenges faced by sand control technology in oil extraction and looks ahead to future development directions, including the development of new materials, exploration of new processes, and the application of intelligent and digital technologies in sand control. This paper aims to provide valuable references for further research and application of sand control technology in oilfield production.

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With the growth of the global population and the impact of climate change, agricultural research faces unprecedented challenges. Artificial intelligence (AI), with its powerful data processing and analytical capabilities, is rapidly transforming traditional methods in agricultural research. This article explores the applications and developments of AI in agricultural research, focusing on three key areas: gene editing, crop improvement, and data analysis. First, the article introduces the foundational AI technologies, including machine learning, data analysis, computer vision, and robotics. It then discusses in detail the application of AI in gene editing, highlighting how AI can enhance the efficiency and accuracy of gene editing processes. Next, the article examines the role of AI in crop improvement, where AI algorithms optimize gene selection and predict crop performance. Additionally, the application of AI in agricultural data analysis is thoroughly explored, with a focus on data-driven decision support and the implementation of precision agriculture. The use of AI in pest and disease monitoring, control, and adaptation to climate change is also addressed in depth. Finally, the article summarizes the current challenges AI faces in agricultural research applications and offers insights into future development directions. This article aims to provide researchers and practitioners with a comprehensive perspective on how AI is driving innovation and progress in agricultural research.

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The application of gene editing technology in agriculture has rapidly advanced, particularly demonstrating significant potential in enhancing crop yields. This paper introduces the basic principles of gene editing technology and its primary tools, including CRISPR-Cas9, TALEN, and ZFN. It further explores specific applications of gene editing in improving photosynthetic efficiency, enhancing crop stress resistance, and optimizing nutrient utilization. The successful implementation of gene editing in crop improvement is highlighted through practical cases involving rice, wheat, and maize. The paper also analyzes the technical challenges, ethical concerns, and safety issues associated with gene editing technology, proposing corresponding solutions. Finally, it forecasts the future development trends of gene editing technology, suggesting that with new tools and methods and advancements in precision breeding and multi-gene editing techniques, the application of gene editing in agriculture holds vast potential. This paper aims to provide a comprehensive reference for researchers and practitioners to further advance the application and development of gene editing technology in agriculture.

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As global agricultural modernization accelerates, the importance of soil data analysis in improving crop yields, optimizing resource use, and protecting the environment has become increasingly evident. This article explores the applications and development trends of Artificial Intelligence (AI) in soil data analysis. First, it introduces methods for soil quality monitoring using sensor technology and the role of AI in processing and analyzing this data to achieve real-time soil condition assessments. Next, it discusses AI-based intelligent fertilizer management, highlighting how AI provides precise fertilization recommendations and optimizes fertilizer use efficiency. The article then analyzes the application of AI in soil health prediction, including long-term monitoring, trend analysis, and intelligent decision support. Finally, it looks ahead to the future development trends of AI in this field, analyzing the challenges and opportunities it faces. The application of AI in soil data analysis has not only brought significant transformations to agricultural production but also offers new pathways for achieving sustainable agricultural development.

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Rice crop rotation systems play a significant role in improving soil health and crop yield. This paper aims to explore the application of big data technology in optimizing rice crop rotation systems, to enhance their efficiency through data-driven decision models and optimization algorithms. Firstly, the paper reviews the current status of traditional rice crop rotation systems, analyzing their advantages and disadvantages, and their impact on soil health and rice yield. Then, it discusses the application of big data in agriculture, including data collection, processing, and analysis methods. The paper goes on to analyze in detail the strategies for rotating rice with other crops, explaining the effects of different rotation patterns on soil health and crop yield. Additionally, the paper proposes optimization strategies for crop rotation systems based on big data, introducing data-driven rotation decision models and the design of intelligent rotation systems. Through a virtual case study, the specific application of rotating rice with legumes is simulated, demonstrating the potential and effectiveness of big data technology in optimizing crop rotation systems. Finally, the paper discusses the challenges in data collection and processing, the accuracy and feasibility of the models, and provides an outlook on the future research directions for intelligent rotation systems.

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Integrating big data analysis and intelligent breeding technology in crop molecular breeding is a crucial direction for modern agricultural science and technology development. This paper provides a detailed introduction to the basic concepts and technological development of crop molecular breeding, explores the application of big data analysis in genomic selection, association analysis, and genome editing, and explains the significant role of artificial intelligence, machine learning, the Internet of Things, and sensor technology in intelligent breeding. By analyzing the design and implementation of big data platforms and precision breeding systems, this paper demonstrates the necessity and advantages of their integrated application while discussing the current challenges and future development directions. The combination of intelligent breeding technology and big data analysis enhances breeding efficiency and precision and provides new possibilities and developmental paths for future crop breeding.

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Crop nutritional omics is a scientific field focused on studying the genetic basis of nutrients in crops and the regulatory mechanisms governing them. The goal is to uncover the molecular foundation of nutrients through multi-omics technologies, to enhance crop nutritional value via genetic improvement or breeding techniques. This paper systematically reviews the progress in crop nutritional omics research, including genome-wide association studies, quantitative trait loci analyses, and metabolomics studies, while delving deeply into the metabolic pathways of nutrients. Furthermore, the paper elaborates on nutritional traits' multi-level molecular regulatory mechanisms, encompassing gene expression regulation, post-translational modifications, and metabolic regulation. The influence of environmental factors on nutritional traits is also a key area of research, with gene-environment interactions being analyzed to elucidate how environmental factors affect nutrient synthesis and accumulation. Finally, the paper discusses the application prospects of nutritional omics research in molecular breeding, genetic improvement, optimizing crop varieties, and ensuring food security. The paper aims to provide an academic reference for future studies on crop nutritional omics and the regulatory mechanisms of dietary traits.

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Nitrogen is an essential nutrient for plant growth and agricultural production. Soil nitrogen addition plays a critical role in increasing crop yields and improving soil fertility, but it also raises concerns about environmental pollution and ecological imbalances. This paper summarizes the main research progress on soil nitrogen addition in recent years, covering various methods such as chemical nitrogen fertilizers, organic nitrogen fertilizers, and biological nitrogen fixation. The effects of nitrogen fertilizers on soil health, microbial communities, and nitrogen use efficiency are discussed, along with the latest technologies for reducing nitrogen losses and environmental pollution. Despite significant advances in research, challenges such as the overuse of nitrogen fertilizers, regional disparities, and economic policy factors remain to be addressed. Future research should focus on developing green nitrogen fertilizer technologies, improving nitrogen use efficiency, and enhancing interdisciplinary collaboration and policy support to achieve sustainable nitrogen management. This paper provides scientific insights and reference directions for research and practice in the related fields.

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This article explores the important role of nitrogen fertilizer in crop rotation systems, the influencing factors, and strategies for improving nitrogen use efficiency. First, the article introduces the key role of nitrogen fertilizer in crop growth, including providing essential nitrogen nutrients, enhancing soil fertility, and strengthening crop resistance to diseases. Second, it provides an overview of the basic concepts and principles of crop rotation, emphasizing the importance of designing rotation systems to optimize nitrogen balance and increase soil organic matter content. The article further analyzes in detail the comprehensive effects of nitrogen fertilizer on soil fertility, crop growth, and pest and disease control within rotation systems. Moreover, it discusses strategies such as proper fertilization methods, suitable crop selection, and scientific soil management techniques to improve nitrogen use efficiency and promote sustainable agricultural development. Finally, the article points out directions for future research and practices. This article aims to provide scientific nitrogen fertilizer management recommendations for agricultural production, fostering the sustainable development of crop production.

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This paper systematically explores the definition, development, and current application of rice intercropping systems at both global and regional levels, with a focus on analyzing the ecological benefits in maintaining ecosystem balance, enhancing biodiversity, improving soil health, and managing water resources. By increasing biodiversity and improving soil health, rice intercropping effectively enhances the stability and sustainability of ecosystems. Additionally, the paper provides a detailed overview of key cultivation techniques, pest and disease control strategies, fertilizer and water resource management, as well as recent technological innovations and practical application cases. Through a comprehensive review of relevant literature, the paper projects future research directions and trends in rice intercropping, emphasizing the importance of interdisciplinary collaboration and international exchange. Future research should focus on multidisciplinary integration, combining fields such as ecology, soil science, plant pathology, and agricultural economics to gain a comprehensive understanding and optimization of rice intercropping systems. This paper aims to provide a scientific basis and practical guidance for the further promotion and optimization of rice intercropping systems, contributing to the sustainable development of global agriculture.

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This article explores the current state of development in ecological agriculture, key technologies and methods, impacts and benefits, as well as the challenges and solutions it faces. First, it introduces the concept and principles of ecological agriculture, emphasizing its importance in environmental protection, food security, and sustainable development. Next, it analyzes the application and effectiveness of key technologies and methods such as organic farming, diversified agriculture, water resource management, and soil conservation. Then, it examines the environmental and economic impacts of ecological agriculture, including pollution reduction, improved quality of agricultural products, and job creation. The article also discusses the technical challenges, policy support, and social awareness issues in the development of ecological agriculture, providing corresponding solutions and recommendations. Finally, it summarizes the achievements and shortcomings in the development of ecological agriculture and looks ahead to future development directions. This article aims to provide a reference for the further advancement of ecological agriculture and to promote the transition of agriculture towards a more ecological and sustainable path.

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Climate change is having a profound impact on global water resources, encompassing various aspects such as changes in precipitation patterns, rising temperatures, glacier melt, and sea-level rise. This article aims to systematically explore the effects of climate change on the water cycle, glaciers and snow cover, coastal water resources, and water quality. It also analyzes the challenges that extreme weather events, induced by climate change, pose to water resource management, highlighting the limitations of traditional water management methods in addressing climate change. Furthermore, the article discusses the application of integrated water resource management, advanced technologies, and innovative approaches in adapting to and mitigating the impacts of climate change. By summarizing current research and forecasting future research directions, this article provides a reference for developing scientifically sound and effective water resource management strategies to address the complex challenges brought about by climate change.

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This paper systematically summarizes the latest progress in delta wetland research, focusing on its ecological characteristics, biodiversity, ecosystem functions, ecosystem services, and the threats and conservation measures it faces. First, the paper discusses the formation mechanisms and fundamental characteristics of delta wetlands. It then delves into the importance of wetland biodiversity to its ecosystem, analyzing the functions of wetlands in hydrological regulation, biogeochemical cycles, and climate regulation. Based on this, the paper highlights the significance of delta wetlands to human society and explores the ecosystem services they provide. However, the increasing impact of human activities on delta wetlands has led to degradation and destruction, necessitating effective conservation and management measures. Finally, the paper reviews the current research hotspots and future development directions, aiming to promote the sustainable management and conservation of delta wetlands.

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Temperature and precipitation, as critical climate system components, directly influence ecosystems, agriculture, water resource management, and various aspects of human society. Understanding these change patterns and driving mechanisms is crucial for predicting future climate change and developing effective response strategies. This paper systematically reviews and analyzes the existing literature, comprehensively assessing the spatiotemporal evolution of temperature and precipitation at both global and regional scales and their primary driving factors. This paper begins by summarizing the interannual variations, long-term trends, and seasonal temperature and precipitation patterns, discussing the changing trends in extreme temperature and precipitation events. It then analyzes the main factors influencing temperature and precipitation changes, including atmospheric circulation patterns, oceanic phenomena, human activities, and natural factors. This paper also evaluates the major methodological issues in current research, exploring the advantages and disadvantages of data sources, analytical methods, and the selection of spatiotemporal scales. The review reveals significant spatial and temporal heterogeneity in temperature and precipitation changes, driven by multiple factors. Future research should enhance the understanding of interactions among these factors, improve methodological approaches, and increase the accuracy of climate models. This paper provides a comprehensive reference framework for researchers in related fields and offers scientific evidence for policymaking and climate adaptation practices.

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