This article explores the widespread prospects of artificial intelligence (AI) application in oilfield exploration and development in the era of intelligence. The article begins by introducing the rise of AI in various fields, emphasizing the urgent need for technological innovation in the oilfield exploration and development industry. Subsequently, the article delves into the challenges faced by oilfield exploration, including complex geological conditions, high costs, and the complexity of data processing and interpretation. It elaborates on the application value of AI in these aspects. The discussion encompasses data analysis and interpretation in seismic exploration, data management and optimization, automated drilling technologies, predictive maintenance, and equipment monitoring, as well as environmental protection and sustainable development. Through a comprehensive and in-depth discussion, this article provides readers with a comprehensive outlook on the prospects of AI applications in oilfield exploration and development.

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This paper explores the characteristics of water influx in carbonate reservoirs and analyzes the relationship between water-driving forces and water invasion. It begins by introducing carbonate reservoirs’ fundamental concepts, distribution, and formation conditions. Subsequently, it elaborates on the manifestations of water influx, influencing factors, and the mechanisms underlying water invasion. It provides a thorough analysis of the geological and physical reasons for water influx issues. In terms of water influx diagnosis and monitoring, the paper discusses various technological approaches, including diagnostic techniques and monitoring tools, to offer a comprehensive assessment of water influx. Furthermore, the paper discusses methods for controlling and managing water influx, emphasizing the importance of water injection management in carbonate reservoirs. It proposes a series of recommendations for optimizing water injection strategies. Finally, the paper outlines future research directions and development trends in the field.

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As a common type of oil field, reservoirs with high water cuts have attracted widespread attention in the petroleum industry due to their unique characteristics in residual oil distribution. This paper begins by providing readers with a comprehensive understanding of the formation reasons for high water cut period reservoirs. It then delves into the impact of water dynamics and geological features on the formation of residual oil, revealing their mechanisms through specific case studies. In analyzing the distribution characteristics of residual oil in high water cut periods, attention is paid to the spatial distribution patterns and heterogeneity within the reservoir, showcasing the diverse nature of residual oil in these reservoirs. Furthermore, the paper explores the specific mechanisms through which production operations and rock properties influence the distribution of residual oil, offering theoretical support for optimizing production and recovery strategies. Finally, the paper introduces methods and technologies for evaluating residual oil in high water cut period reservoirs, along with discussions on potential avenues for optimizing recovery strategies. This paper aims to provide oilfield managers, researchers, and practitioners with a comprehensive understanding of the residual oil distribution characteristics in high water cut period reservoirs, facilitating more effective and sustainable oil field development.

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This article provides a comprehensive review of recent research on the enrichment characteristics of terrestrial shale oil. Firstly, it explores the mechanisms of organic matter enrichment, pore structure, reservoir characteristics, and the influence of water and gas on shale oil enrichment. Subsequently, it discusses the characteristics of source rocks, diagenesis, reservoir features, and the relationship between stratigraphic structure and shale oil enrichment. Furthermore, it summarizes the progress made in the research on terrestrial shale oil. In conclusion, the paper discusses the limitations of existing studies and proposes future research directions and prospects. The content of this paper not only comprehensively understands the enrichment characteristics of terrestrial shale oil but also lays the foundation and provides directions for further exploration in related fields.

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With the rapid development of artificial intelligence (AI) technology, its widespread application across various industries has garnered considerable attention. This article aims to explore the potential applications and impact of AI in future agricultural production. It begins by introducing the successful applications of AI in different domains, providing readers with a contextual understanding. Subsequently, it reviews traditional agricultural production models, emphasizing the positive influence of AI on agricultural practitioners and global food supply, highlighting the challenges and opportunities faced by the agricultural sector. Furthermore, it delves into the key components of smart agriculture and their potential roles in soil monitoring, crop forecasting, and enhancing production efficiency. Finally, in conjunction with the discussion on the potential applications of AI in agriculture, the article underscores the prospective contributions of AI to future agricultural sustainability and global food security. Through this comprehensive exploration, the article aims to provide readers with a profound understanding of the prospects of AI in future agriculture.

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This paper systematically explores key areas of agricultural sustainable development, encompassing soil health and conservation, water resource management, biodiversity preservation, climate change adaptation and mitigation, and agricultural technological innovation. In terms of soil health and conservation, the paper discusses sustainable farming practices, nutrient management, and optimal use of agricultural chemicals to ensure the long-term agricultural productivity of the soil. The section on water resource management focuses on efficient water utilization, water-saving irrigation technologies, and strategies to prevent water source pollution. Biodiversity preservation emphasizes diverse planting in agricultural ecosystems and reducing disturbances to wild flora and fauna. The section on climate change adaptation and mitigation investigates adaptive strategies within agricultural systems to cope with climate change, the breeding of resilient crop varieties, and technologies and methods to reduce greenhouse gas emissions. Finally, agricultural technological innovation focuses on emerging agricultural technologies, including precision agriculture, digital management, and the development of sustainable agricultural practices. By integrating research in these areas, this paper provides a comprehensive framework for agricultural sustainable development, emphasizing its overall impact on soil, water resources, biodiversity, climate, and technology. This comprehensive study aims to offer valuable guidance for achieving the health, eco-friendliness, and socio-economic sustainability of agricultural systems.

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This article explores the interrelationships between degraded grassland vegetation and soil characteristics. The discussion primarily revolves around three aspects: the correlation between grassland vegetation and soil characteristics, indicators and assessment methods for degraded grassland vegetation and soil characteristics, and factors and mechanisms influencing degradation. Firstly, a thorough analysis of the correlation between degraded grassland vegetation and soil characteristics is conducted, revealing their complex and interconnected dynamics. Secondly, concerning indicators and assessment methods for degraded grassland vegetation and soil characteristics, the article systematically summarizes the primary tools and approaches currently employed to measure vegetation health and soil conditions. Lastly, in discussing the factors and mechanisms influencing degradation, an in-depth analysis of the causes leading to the degradation of grassland vegetation and soil is presented. Through multifaceted exploration, this article provides profound insights into understanding and effectively managing the intricate relationship between degraded grassland vegetation and soil.

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Using big data in smart agriculture represents a significant technological revolution in the agricultural sector. This paper delves into the primary application areas of big data in smart agriculture, encompassing precision agriculture, intelligent farming machinery, and sustainable agriculture. To begin with, big data technology offers comprehensive data support for decision-makers in agriculture. Through real-time monitoring and extensive data analysis, agricultural producers can gain more precise insights into soil conditions, crop growth status, and other crucial information. Furthermore, implementing big data in precision agriculture facilitates intelligent agricultural production by integrating precision fertilization, irrigation, and pest and disease monitoring, thereby enhancing production efficiency. The advancement of intelligent farming machinery also plays a pivotal role in enhancing the intelligence and efficiency of field operations. Moreover, big data technology contributes to sustainable agriculture by providing scientific support for agricultural decisions through data analysis on climate change, resource utilization, and other pertinent aspects, thereby promoting the transition towards environmentally friendly agricultural practices. Looking ahead, with continuous innovation and widespread adoption of big data technology, smart agriculture is poised for significant development opportunities.

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In recent years, the application of Artificial Intelligence (AI) in the agricultural sector has fundamentally transformed traditional agricultural practices, particularly in soil management. This article explores various applications of AI in soil management and its role in driving smart agriculture. By analyzing existing literature and case studies, this article elucidates the potential of AI technologies such as machine learning, remote sensing, and robotics in optimizing soil health, increasing crop yields, and reducing environmental risks. Furthermore, it discusses the challenges and opportunities faced in the widespread application of AI in soil management and proposes strategies to maximize its advantages in agriculture. This article aims to provide more scientific and intelligent solutions for agricultural production, thereby promoting sustainable agricultural development.

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This article reviews the impact of climate change on rice production and discusses adaptation strategies. Firstly, it introduces the effects of global warming trends, changes in precipitation and temperature, and increased frequency of extreme weather events on the rice growing environment. Secondly, it explores the impact of climate change on rice yield and quality, including direct effects, quality implications, and rice resilience and resistance. Subsequently, it proposes rice cultivation strategies to address climate change, including breeding stress-tolerant varieties, improving planting structure and techniques, and introducing technological measures to enhance production efficiency. Finally, it anticipates the ongoing impact trends of climate change on rice and suggests innovative directions for rice cultivation technologies to adapt to climate change, emphasizing the importance of international cooperation and action in addressing the challenges of climate change. This article provides valuable insights for addressing the challenges posed by climate change to rice production.

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