Global climate change has intensified the frequency of extreme heat events, posing severe threats to crop yield, quality, and food security, making the breeding of heat-tolerant crops a key frontier in agricultural research. Although traditional breeding and marker-assisted selection have achieved progress in heat tolerance improvement, their applications remain limited due to complex genetic backgrounds, long breeding cycles, and insufficient mapping precision. In recent years, genome editing technologies, particularly the CRISPR/Cas system, have provided new breakthroughs for enhancing crop heat tolerance owing to their efficiency, precision, and flexibility. This paper systematically reviews the key molecular mechanisms underlying crop responses to heat stress, including heat signal perception, heat shock protein networks, transcriptional regulation hubs, reactive oxygen species metabolism, and phytohormone pathways, and summarizes potential editable targets. On this basis, it highlights major strategies and representative applications of genome editing for heat tolerance, covering the enhancement of endogenous defense pathways, regulation of core transcription factors, and optimization of physiological and developmental processes. Furthermore, it evaluates the empowering potential of cutting-edge editing technologies—such as promoter engineering, multiplex gene editing, genome-wide screening libraries, base and prime editing, and epigenome editing—in enabling “precise design” of heat tolerance. Finally, considering challenges such as target discovery, editing efficiency, off-target control, trait trade-offs, and biosafety, it emphasizes the need for interdisciplinary integration and technological convergence in future research. This paper aims to establish a systematic framework linking “molecular mechanisms—editing strategies—technological innovation—application challenges,” thereby providing theoretical support and technical references for molecular design breeding of heat-tolerant crops.

抄録全体を表示

In the context of global climate warming, heat stress has become a critical environmental factor threatening crop growth and food security. In recent years, the research paradigm has shifted from traditional aboveground phenotypic regulation to the functional analysis of underground systems, with root–microbe interactions increasingly recognized as a core biological mechanism underlying crop heat tolerance. This paper systematically reviews the impacts of heat stress on root structural development, root exudate composition, and rhizosphere microbial community assembly, revealing the multilayered mechanisms by which root–microbe interactions enhance water and nutrient uptake, regulate phytohormone signaling pathways, activate antioxidant defense systems, and induce systemic tolerance responses. Particular attention is given to the synergistic adaptation mechanisms of functional microbial groups—including arbuscular mycorrhizal fungi, plant growth-promoting rhizobacteria, endophytic fungi and bacteria, and dark septate endophytes—under different ecological conditions, elucidating the regulatory roles of their metabolites and signaling molecules in heat tolerance responses. By integrating multi-omics and systems biology approaches, this paper proposes the construction of a “rhizosphere core interaction network” to identify key regulatory nodes of heat tolerance, emphasizing the application potential of second-genome-based molecular breeding, synthetic microbial consortia design, and rhizosphere microecological engineering in enhancing crop climate resilience. This paper aims to establish a systematic framework for understanding crop heat tolerance “from molecules to ecosystems,” providing theoretical foundations and technological pathways for the development of resilient agriculture under climate change.

抄録全体を表示

Against the backdrop of worsening global malnutrition and increasing agricultural production pressures, improving the nutritional quality of crops has become a key measure to ensure food security and achieve sustainable agricultural development. This paper systematically reviews the latest progress in applying gene editing technology and big data analysis to crop nutritional improvement, with a particular focus on the potential, strategies, and challenges of their integrated use. First, it introduces the sources, types, and advanced analytical methods of big data in agriculture, and highlights the importance of data-driven decision-making in precision farming. Second, it reviews the current applications and strategies of gene editing technologies, such as the CRISPR/Cas system, in enhancing key nutritional components of crops, including vitamins, minerals, and antioxidants. The paper then explains the research workflow of integrating gene editing with big data, covering high-throughput gene target screening, optimisation of editing strategies, and experimental validation, and presents representative cases that demonstrate the successful application of this integration in improving crop nutritional quality. Finally, it analyses experimental challenges, ethical debates, and regulatory issues in the process of technological integration, while providing a forward-looking perspective on future directions in technology optimisation, interdisciplinary collaboration, and policy improvement. This paper aims to offer comprehensive theoretical and methodological references for agricultural researchers and practitioners, thereby promoting the deeper application and continuous innovation of gene editing and big data technologies in the field of crop nutritional enhancement.

抄録全体を表示

Against the backdrop of intensified global climate change and increasing water scarcity, the development of water-saving and drought-resistant crop varieties has become a strategic task to ensure agricultural sustainability and food security. This paper systematically reviews the progress and integration pathways of gene editing and traditional breeding technologies for breeding water-saving, drought-resistant crops. First, it outlines the basic principles of mainstream gene editing technologies such as CRISPR/Cas9, TALEN, and ZFN, and highlights their key roles in functional analysis and precise improvement of drought-related genes. Second, it summarizes the typical achievements and limitations of traditional breeding methods, including hybrid breeding, mutation breeding, and marker-assisted selection, in the development of drought-tolerant and water-saving varieties. On this basis, the paper further discusses strategies and practical cases of synergistic application between the two categories of technologies, emphasizing their advantages in accelerating breeding cycles and improving genetic enhancement efficiency. Meanwhile, the field performance and adaptability of different water-saving and drought-resistant crop varieties under diverse ecological conditions are evaluated, with a particular focus on the supporting role of high-throughput phenomics and big data analysis in breeding decision-making. Finally, future research priorities are envisioned, including multi-gene synergistic editing, the construction of intelligent breeding platforms, interdisciplinary technology integration, and international scientific collaboration. This paper aims to provide theoretical foundations and technical support for the efficient breeding of water-saving and drought-resistant crops, thereby promoting agricultural green transformation and enhancing the resilience of global food systems.

抄録全体を表示

Climate change has become a key factor affecting global agricultural production and the sustainable utilization of water resources. This paper systematically reviews the mechanisms by which climate change influences agricultural water resources and the corresponding regulation strategies, aiming to comprehensively assess the challenges to the balance of agricultural water supply and demand and to explore effective approaches for adaptation and mitigation. First, it analyzes the impacts of rising temperatures, evolving precipitation patterns, and the increasing frequency of extreme climate events on the global water cycle. Second, it highlights the dual effects of climate change on the supply and demand of agricultural water resources, including changes in the availability of surface water and groundwater, as well as the dynamic adjustment of crop water requirements. By comparing regional case studies, the paper reveals differentiated responses in arid and semi-arid areas, temperate and tropical regions, and areas dependent on glacier and snowmelt water under climate change. Furthermore, it reviews the critical role of adaptive measures such as water-saving irrigation, the breeding of drought- and stress-resistant crops, and integrated water resources management policies in addressing climate change. Finally, it summarizes future projections of climate change impacts on agricultural water resources, identifies current research gaps, and proposes future directions in multi-source data integration, model refinement, and interdisciplinary research. This paper provides valuable scientific reference for the sustainable development of agricultural water resources.

抄録全体を表示

With the widespread application of big data technology in agriculture, the development and optimization of wheat growth models are entering a new stage of data-driven and intelligent approaches. This paper systematically reviews the progress in the construction and optimization of wheat growth models under the big data environment. First, it analyzes the key role of big data in model development, covering the diversification of data sources, methods of data preprocessing and integration, as well as the advantages of big data-driven models in expanding spatiotemporal scales and improving prediction accuracy. Second, it reviews the structural characteristics of current mainstream wheat growth models, parameterization and calibration methods, model validation and uncertainty analysis techniques, and summarizes strategies and practical approaches for model optimization. Then, it discusses the application performance of wheat growth simulation under different ecological and management conditions, emphasizing its practical value in precision agriculture, crop regulation, and agricultural decision support, while also pointing out challenges such as insufficient model generalization capacity, uneven data quality, and the complexity of technological integration. Finally, it looks ahead to future research trends, including multi-model coupling and integration, real-time dynamic simulation based on artificial intelligence, and the construction of interdisciplinary, open, and shared data platforms. This paper aims to provide systematic references for wheat growth modeling and application research, promoting the deep integration and innovative development of big data technology in agricultural system modeling.

抄録全体を表示

Under the increasingly severe pressures of global climate change and agricultural sustainable development, biochar has become a research hotspot in soil improvement due to its multiple ecological benefits, including improving soil structure, enhancing soil fertility, promoting carbon sequestration, and mitigating greenhouse gas emissions. In recent years, the rapid development of big data technology has provided strong technical support for the scientific application and mechanism analysis of biochar. This paper systematically reviews the research progress on the application of big data in biochar-based soil improvement, with a particular focus on its key roles in soil physicochemical property assessment, biochar formulation optimization, application strategy development, and effect monitoring. Studies have shown that big data analysis facilitates precise regulation and spatiotemporal optimization of biochar application, significantly enhancing its effectiveness in improving soil nutrient supply, activating soil microbial communities, and increasing water retention capacity. Furthermore, this paper conducts a multidimensional evaluation of the environmental and economic benefits of biochar application, while summarizing current research bottlenecks such as regional imbalance in data acquisition, difficulties in multi-source data integration, and insufficient model generality. Finally, it explores the future potential of integrating big data and biochar in advancing intelligent agriculture, carbon neutrality strategies, and sustainable land management. This paper contributes to promoting the evolution of soil improvement technologies toward digitalization, intelligence, and green low-carbon development.

抄録全体を表示

Against the backdrop of ongoing urbanization and increasingly severe climate change, urban water resource management faces multiple challenges, including supply–demand imbalances and rising system complexity. Traditional scheduling systems, constrained by delayed response and limited intelligent optimization capacity, can no longer meet the comprehensive requirements of modern cities for safety, real-time performance, and efficiency in water supply systems. This paper systematically reviews the theoretical foundations, key methodologies, and practical advances of intelligent scheduling systems for urban water resources that integrate big data and artificial intelligence (AI). First, it analyzes the basic models of urban water resource scheduling and the limitations of traditional approaches, highlighting their inadequacy in adapting to nonlinear and time-varying complex environments. Second, it explores the role of big data technologies in water use behavior modeling, demand forecasting, and decision support, with a focus on the key applications of AI algorithms such as machine learning and deep learning in scheduling optimization, system perception, and intelligent control. Subsequently, the paper concentrates on intelligent optimization strategies for urban water supply networks, proposing innovative technological pathways including multi-source data fusion, coordinated control, and resilient scheduling to enhance system stability and robustness. Finally, it summarizes the current challenges of data acquisition, model generalization, and system integration. It looks ahead to the future directions of intelligent scheduling systems in achieving efficient and sustainable management of urban water resources. This paper aims to provide theoretical support and technical references for the construction of a new generation of intelligent water resource scheduling systems.

抄録全体を表示

Against the backdrop of increasingly scarce global agricultural resources and intensified climate change, efficient dynamic management of farmland water and temperature has become a key link in achieving sustainable agricultural development. With its powerful capabilities in data collection, transmission, storage, and intelligent analysis, big data technology is increasingly serving as an important support for promoting precision agricultural management. This paper systematically reviews the current research status and technical requirements of farmland water and temperature management, with a particular focus on the integrated application and optimization strategies of big data in precision irrigation and thermal environment regulation. Through analyses of typical application cases, the effectiveness of big data in improving agricultural water-use efficiency, enhancing crop adaptability, and strengthening stress resistance is demonstrated. Additionally, this study examines the adaptive management strategies required to address the uncertainties associated with climate change. It highlights the potential of data-driven dynamic regulation in enhancing agricultural system resilience. Finally, the technical bottlenecks related to data integration, multi-source heterogeneous information processing, and intelligent decision-making are summarized, and future research directions are proposed. This paper aims to provide both theoretical support and practical reference for the in-depth application and promotion of big data technology in agriculture.

抄録全体を表示

Climate change exerts profound impacts on hydrological processes and the spatiotemporal distribution of water resources, and accurately assessing these impacts has become a core issue in global sustainable water resource management and climate adaptation strategies. This study focuses on big data-driven methods for coupling climate models and hydrological models, aiming to enhance the simulation and prediction of water resource evolution under climate change. First, the fundamental principles of typical climate models and hydrological models are systematically reviewed, and the critical role of big data technologies in multi-source data integration, model input optimization, and parameter inversion is analyzed. Second, coupling strategies of climate and hydrological models under one-way driving and two-way feedback frameworks are explored in depth, and their effectiveness in improving simulation accuracy and regional adaptability is evaluated. Through case studies of typical river basins, the coupled models are verified for their effectiveness in characterizing runoff changes, water resource availability, and supply-demand imbalance risks under climate change. Finally, challenges in model uncertainty quantification, data assimilation, and spatiotemporal scale consistency are highlighted, and the future potential of artificial intelligence and deep learning technologies in climate–hydrological coupling simulations is discussed. The findings are expected to provide theoretical and technical support for watershed water resource planning, risk assessment, and climate adaptation policy-making.

抄録全体を表示

Climate change, characterized by frequent extreme weather events, rising temperatures, and abnormal spatiotemporal distribution of precipitation, has become a major challenge to global agricultural sustainability, significantly affecting crop yield, quality, and stability. Enhancing crop adaptability to climate change is therefore a core issue for ensuring food security and ecological resilience. In recent years, big data technologies, with their advantages in multi-source data integration, rapid computation, and intelligent analysis, have provided new theoretical frameworks and practical approaches for agricultural adaptability research. This study systematically analyzes the key mechanisms by which climate change impacts crop growth, evaluates typical applications of big data in environmental monitoring, adaptive prediction model construction, disaster risk assessment, and precision management, and highlights research progress in data acquisition, algorithm optimization, and interdisciplinary integration. Meanwhile, it identifies persistent bottlenecks such as insufficient data standardization, poor regional applicability, and inadequate policy coordination. Looking ahead, building open and shared data platforms, developing high-precision crop–climate coupled models, and advancing the application of intelligent decision-support systems are essential to fully leverage the strategic value of big data in enhancing crop climate adaptability and promoting sustainable agricultural production.

抄録全体を表示

Nitrogen fertilizer is not only a key input for improving rice yield and regulating grain quality but also a crucial factor influencing its stress resistance. Based on recent research progress, this study systematically elucidates the mechanisms and regulatory effects of nitrogen fertilizer on rice lodging resistance, pest and disease resistance, and drought tolerance. Evidence indicates that appropriate nitrogen application can significantly enhance rice lodging resistance and drought tolerance by strengthening stem mechanical properties, improving root system architecture, and increasing water-use efficiency; meanwhile, nitrogen levels exert dual effects on pest and disease resistance, where rational application enhances plant defense capacity, whereas excessive application may weaken resistance and increase the risk of pest and disease outbreaks. In the context of global climate change and increasingly frequent extreme weather events, optimizing nitrogen management is of great importance for improving rice stress resistance and stabilizing yield, while also reducing environmental burdens and promoting sustainable agriculture. This study aims to provide theoretical support for scientific decision-making in rice nitrogen fertilization and to indicate future directions for in-depth research on stress resistance regulation mechanisms and green fertilization strategies.

抄録全体を表示

With the rapid advancement of gene editing technologies, CRISPR/Cas9 has emerged as one of the most promising tools for crop improvement, demonstrating broad application potential in the genetic enhancement of staple crops such as potato. However, its practical applications still face key challenges, including unavoidable off-target effects, the complexity of target site screening, and unstable editing efficiency. The integration of artificial intelligence (AI) provides new approaches and solutions to these issues. This paper systematically reviews the application progress of AI in CRISPR/Cas9 technology, with a focus on its roles and advantages in regulating key genes of potato starch biosynthesis pathways, designing multi-gene targets, and predicting off-target effects. Research indicates that AI-based genomic data mining and feedback-driven editing optimization mechanisms can significantly enhance the accuracy of target site screening and the overall efficiency of gene editing. Finally, this paper discusses the potential of deep integration between AI and gene editing technologies in promoting precision potato breeding and data-driven agricultural improvement strategies, offering new references and insights for the future development of intelligent molecular breeding.

抄録全体を表示

Long-term continuous cropping of potato commonly leads to soil degradation and replanting obstacles, with soil microbial communities playing a crucial ecological role in this process. In this study, field experiments were conducted with treatments of fallow, 1-year, 3-year, and 5-year continuous cropping, and MiSeq high-throughput sequencing was employed to systematically analyze rhizosphere soil microbial communities. The results showed that continuous cropping for 3–5 years significantly reduced α-diversity indices such as Ace, Chao, and Shannon, indicating declines in soil microbial diversity and ecological stability. In terms of community composition, the relative abundance of Proteobacteria, Actinobacteria, and Acidobacteria increased in bacterial communities, with significant enrichment of genera such as Arthrobacter and Streptomyces; in fungal communities, pathogenic Fusarium sharply increased, while beneficial fungi such as Chaetomium and Hanseniaspora markedly decreased, and Chytridiomycota approached the detection limit. These findings reveal the dynamic succession characterized by the accumulation of harmful microbes and the loss of beneficial ones in potato continuous cropping soils. This study systematically elucidates the response patterns of soil microbial communities under different durations of continuous cropping, uncovering the microbial ecological mechanisms underlying potato replanting obstacles, and provides a scientific basis for understanding and mitigating these obstacles as well as theoretical support for microbe-mediated soil remediation and sustainable potato cultivation.

抄録全体を表示

Salinization is a global issue that constrains farmland productivity and ecosystem stability, yet the community patterns and functional differentiation mechanisms of soil prokaryotes under saline conditions remain insufficiently understood. In this study, soil samples were collected across different land use types (farmland and wasteland) and salinization gradients, and their physicochemical properties, prokaryotic community composition, and potential functional traits were comprehensively analyzed. The results showed that prokaryotic community diversity in farmland soils was significantly higher than in wastelands, with distinct distributions of unique taxa between land use types. Soil electrical conductivity, pH, and soil organic matter were identified as key drivers of community differentiation, with synergistic effects observed among multiple environmental factors. Functional prediction further indicated that farmland soils were enriched in functional groups related to nitrogen cycling and plant growth promotion, while wasteland soils harbored halophilic archaea and organic matter decomposers, reflecting contrasting microbial adaptation strategies and ecological functions across niches. Based on these findings, this study proposes an ecological framework of “salinization–land use–microbial feedback” to elucidate the diversity patterns, environmental driving mechanisms, and functional responses of prokaryotic communities in saline soils. The novelty of this work lies in the dual comparison of land use types and salinization gradients, revealing the environmental adaptability and functional response patterns of microbial communities, thereby providing new microbiological insights for saline soil ecological restoration and sustainable farmland management.

抄録全体を表示

Peanut (Arachis hypogaea L.) is a major oilseed and cash crop worldwide, but its yield and quality are often limited by drought and salt stress. Understanding the evolutionary features and functional mechanisms of stress-responsive genes is essential for developing stress-tolerant cultivars. The ASR (Abscisic acid, Stress and Ripening) gene family, which encodes plant-specific small molecular binding proteins, has been reported to play key roles in stress response and signal transduction in various crops. In this study, we conducted a genome-wide identification of the ASR gene family in peanut and characterized their evolutionary traits through analyses of physicochemical properties, gene structures, conserved domains, promoter cis-acting elements, and phylogenetic relationships. Transcriptome data under drought and salt treatments were further integrated to examine expression dynamics. The results showed that the peanut ASR gene family contains multiple members with high hydrophilicity and stability, structurally diverse yet evolutionarily conserved domains, and promoters enriched with cis-elements related to drought, ABA, and salt stress. Several members displayed marked tissue-specific induction under stress conditions, suggesting potential regulatory roles in environmental adaptation. Overall, our findings highlight the coexistence of evolutionary conservation and functional diversification within the peanut ASR gene family and underscore their transcriptional regulatory potential under abiotic stress, providing theoretical insights and genetic resources for elucidating stress-response mechanisms and advancing molecular breeding of stress-resilient peanut cultivars.

抄録全体を表示

Rice, as one of the most important staple crops worldwide, relies heavily on its light energy utilization efficiency for yield formation. However, the regulatory mechanisms of the rhizosphere oxygen environment on rice photosynthesis and physiological metabolism remain poorly understood. In this study, three ecological rice types—tillering-stage rice, deepwater rice, and upland rice—were subjected to rhizosphere saturated dissolved oxygen (RSDO) treatment under hydroponic conditions, with natural conditions as the control, to systematically analyze changes in photosynthetic characteristics, chlorophyll fluorescence parameters, photosynthetic pigment content, and growth indicators. The results showed that RSDO significantly decreased maximum photochemical efficiency, actual photochemical efficiency, electron transport rate, and photochemical quenching coefficient, while increasing non-photochemical quenching and intercellular CO₂ concentration, indicating suppressed actual photochemical efficiency accompanied by enhanced non-photochemical energy dissipation. Moreover, chlorophyll a, chlorophyll b, and total chlorophyll contents decreased markedly, whereas carotenoid content and relative conductivity increased, reflecting inhibited pigment synthesis and reduced cell membrane stability. In terms of growth performance, shoot dry weight and leaf area index were significantly reduced in rice and deepwater rice, while upland rice exhibited stronger adaptability. Overall, these findings demonstrate that rhizosphere oxygen saturation at the tillering stage markedly suppresses light energy utilization and pigment biosynthesis in rice leaves, thereby hindering early plant growth, although upland rice maintains higher resilience. This study elucidates the influence of rhizosphere oxygen environment on rice photosynthetic and physiological responses, providing important theoretical insights for oxygen-nutrition cultivation strategies and breeding of low-oxygen-tolerant rice varieties.

抄録全体を表示

Amid the ongoing transformation of the global energy system and the sustained advancement of the low-carbon transition, deep, ultra-deep, and deep-sea oil and gas resources are increasingly becoming key domains supporting future energy security and technological innovation. In recent years, significant progress has been achieved in petroleum geological theories, particularly in hydrocarbon accumulation dynamics, reservoir structural evolution, fluid migration mechanisms, and multi-scale structural analysis, providing new scientific foundations for the prediction, evaluation, and precise development of oil and gas under complex geological conditions. Meanwhile, continuous breakthroughs in drilling and completion, hydraulic fracturing, deep-sea drilling, and intelligent production technologies have enabled safe and efficient operations under high-temperature, high-pressure, high-stress, and strongly heterogeneous environments. However, challenges such as multi-field coupling effects in deep formations, intensified reservoir heterogeneity, engineering risks under extreme conditions, and the dual constraints of economic feasibility and carbon reduction remain critical barriers to sustainable development in this field. This paper systematically reviews recent advances in geological theories and engineering technologies for deep, ultra-deep, and deep-sea oil and gas exploration and development, analyzes the key challenges currently faced, and proposes strategic directions for future research, including the integrated enhancement of geological theory systems and the synergistic innovation of intelligent and green engineering technologies. The study aims to provide theoretical support and practical insights for advancing the scientific foundation and technological upgrading of oil and gas exploration and development.

抄録全体を表示

As a core approach for the efficient development of tight oil and gas, shale gas, and coalbed methane, volumetric stimulation technology has become a critical technological foundation supporting global energy transition and strategic resource security. This paper systematically reviews the evolution of volumetric stimulation theory, from early two-dimensional fracture propagation models to the development and refinement of the complex fracture network theory, revealing the coupled control mechanisms of geological structure, in-situ stress distribution, and fluid–rock interactions across multiple scales on fracture evolution. At the engineering level, it summarizes the formation and application of efficient stimulation technologies such as optimized staged fracturing design, temporary plugging and diversion fracturing, and simultaneous fracturing, while evaluating recent advances in fracture network characterization techniques, including microseismic monitoring, distributed fiber-optic sensing, and tracer-based inversion. Furthermore, this paper focuses on the scientific issues and technical challenges associated with emerging directions such as deep and ultra-deep reservoir stimulation, non-aqueous fracturing fluid systems, intelligent fracturing operations, and AI-driven decision support. It is argued that future development of volumetric stimulation technologies will be driven by “geology–engineering integration” and “data-driven intelligent optimization,” promoting high-efficiency, low-carbon, and sustainable unconventional oil and gas development. Through a systematic review of theoretical evolution, engineering innovation, and frontier trends, this paper aims to provide strategic insights and practical references for advancing scientific research and technological innovation in the field of volumetric stimulation for unconventional oil and gas.

抄録全体を表示

Natural gas hydrate, as an important type of unconventional clean energy, has attracted widespread attention due to its enormous resource potential, high energy density, and low-carbon characteristics. In recent years, with continuous breakthroughs in exploration and pilot production technologies in deep-sea and permafrost regions, a number of significant phased achievements have been made internationally in natural gas hydrate exploitation. This paper systematically reviews the accumulation mechanisms, occurrence characteristics, and distribution patterns of natural gas hydrates, with a particular focus on evaluating mainstream exploitation technologies, including depressurization, thermal stimulation, chemical inhibition, and gas replacement, in terms of their fundamental principles, engineering practices, and applicable conditions. Furthermore, it explores the application prospects of big data and artificial intelligence in intelligent exploitation, while providing an in-depth analysis of key scientific and engineering challenges such as geological disturbance, environmental risks, and production stability. A comparative analysis of representative international pilot projects is conducted to summarize the adaptability and effectiveness of different technological approaches. On this basis, the paper looks ahead to future development directions, including green and low-carbon exploitation, synergistic technologies for carbon sequestration, and the pathways toward commercialization. The aim is to provide a systematic reference for the continuous innovation and interdisciplinary integration of natural gas hydrate exploitation technologies, as well as theoretical support and practical insights for related industrialization efforts.

抄録全体を表示

Natural gas hydrate, as a strategic energy resource with abundant reserves, wide distribution, and clean, low-carbon attributes, is increasingly becoming a crucial focus for global energy transition and energy security. Predominantly occurring in deep-sea sediments and permafrost regions, its exploration is constrained by complex geological conditions and extreme environments, leading to significant technical challenges and barriers. In recent years, with the continuous integration of geoscience, exploration engineering, and information technology, the exploration technology system for natural gas hydrate has been gradually refined and rapidly advanced, encompassing key processes such as geological surveys, geophysical detection, geochemical analysis, drilling and sampling, as well as intelligent data processing. This paper systematically reviews the core technologies and recent progress in natural gas hydrate exploration, with emphasis on breakthroughs in methods such as bottom-simulating reflectors identification, controlled-source electromagnetic imaging, geochemical anomaly monitoring, and pressure-retained coring, and further elaborates on the frontier applications of big data and artificial intelligence in sweet spot identification, seismic data processing, and resource evaluation. By comparing typical exploration practices in countries such as China, Japan, and the United States, this study summarizes international mainstream technological pathways and future development trends, while highlighting major challenges that remain in heterogeneous data integration, hydrate content prediction, and drilling disturbance control. The research aims to provide systematic references for the efficient exploration and scientific evaluation of natural gas hydrate resources and to promote the establishment of an intelligent and integrated exploration framework.

抄録全体を表示

As an important component of unconventional oil and gas resources, the efficient development of shale oil has long been constrained by key challenges such as strong reservoir heterogeneity and difficulties in identifying effective oil-bearing zones. The sweet spot, as a high-quality target in shale oil exploration and development, plays a critical role in enhancing productivity, optimizing development strategies, and reducing costs, making its accurate prediction highly significant. In recent years, with the rapid advancement of big data analytics and artificial intelligence, machine learning has achieved remarkable progress in sweet spot prediction, covering aspects such as multi-source geological and engineering data integration, feature extraction and selection, as well as model construction and optimization. This paper systematically reviews the applications of supervised learning, unsupervised learning, deep learning, and hybrid approaches in sweet spot prediction, summarizes the main challenges in data quality control, model generalization, and result interpretability, and analyzes the current bottlenecks limiting technological advancement, including insufficient geological consistency constraints, inadequate handling of imbalanced samples, and the lack of spatiotemporal coupling mechanisms. Furthermore, this paper explores frontier directions such as the deep integration of artificial intelligence with geological prior knowledge, cross-regional model development driven by federated learning, and the construction of end-to-end intelligent prediction systems. The aim is to provide a systematic review and future development reference for shale oil sweet spot prediction research, thereby promoting theoretical innovation and practical applications in this field.

抄録全体を表示

Oilfield reservoir characterization and resource evaluation are critical processes in oil and gas exploration and development, directly influencing the assessment of resource potential, optimization of development plans, and accuracy of production forecasting. In recent years, with the deep integration of multi-source geological data and the extensive application of artificial intelligence and big data technologies, significant advances have been achieved in theoretical methods, technical systems, and engineering practices. This paper systematically reviews the core technological progress in reservoir characterization, including core experimental analysis, fine well-logging interpretation, seismic inversion, and digital rock modeling, and summarizes the main technical systems of resource evaluation along with their practical applications and effectiveness in both conventional and unconventional oil and gas development. Moreover, it emphasizes the integrated application of big data and artificial intelligence in reservoir characterization and resource evaluation, provides an in-depth analysis of the major technical challenges under complex reservoir conditions, and discusses future development directions and research frontiers. The findings of this paper can offer theoretical support and technical guidance for efficient oil and gas exploration, development, and intelligent geological modeling.

抄録全体を表示

The second member of the Funing Formation in the Qintong Depression of the Subei Basin is an important potential target for lacustrine shale oil exploration in eastern China; however, systematic understanding of its lithofacies types, reservoir characteristics, and sweet spot distribution remains limited, thereby constraining accurate resource evaluation and exploration deployment. In this study, the second member of the Paleogene Funing Formation was investigated using an integrated approach that combines petrology, geochemistry, mineralogy, physical property analysis, and brittleness index evaluation, leading to the establishment of a ternary lithofacies classification system based on “organic matter abundance–structural characteristics–lithology.” On this basis, the shale reservoir features and sweet spot distribution were systematically characterized. The results show that the shales of this interval are generally characterized by low organic matter abundance, moderate to low thermal maturity, relatively balanced mineral composition, and complex pore structure, which can be further divided into six typical lithofacies. Among them, the medium-organic layered dolomitic shale exhibits superior reservoir quality and fracturability, and is thus identified as the most favorable lithofacies. These findings not only provide scientific support for the selection of favorable areas and efficient exploration and development of shale oil in the Qintong Depression, but also offer a reference framework and methodology for shale oil resource evaluation and sweet spot prediction in similar lacustrine basins.

抄録全体を表示

Noble gases, due to their inertness and conservative behavior, have been widely applied in studies of deep Earth material cycling and planetary sciences, yet their utilization in the determination of natural gas origins and the elucidation of evolutionary mechanisms remains relatively limited. The Songliao Basin, as one of the most important faulted basins in eastern China, holds significant scientific and resource implications for understanding natural gas formation mechanisms. In this study, comprehensive abundances and isotopic compositions of noble gases were systematically measured from deep natural gas samples in the Songliao Basin, with analyses performed using advanced noble gas mass spectrometry and rigorous data quality control. The results show that noble gas abundances exhibit an inverted V-shaped distribution trend decreasing from light to heavy elements, while the ratios of ³He/⁴He, ²⁰Ne/²²Ne, ²¹Ne/²²Ne, and ⁴⁰Ar/³⁶Ar are markedly higher than atmospheric values; isotopes of Kr and Xe also display excesses relative to the atmosphere, indicating mantle-derived gas input. Integrated geochemical evidence demonstrates that deep natural gas in the Songliao Basin possesses prominent crust–mantle mixed-source inorganic origins, with varying contributions of mantle components across different structural units. This study not only reveals the compositional characteristics and genetic mechanisms of noble gases in the Songliao Basin but also provides new evidence and methodological references for the application of noble gas geochemistry in natural gas origin determination, type differentiation, and hydrocarbon resource evaluation.

抄録全体を表示