| Title | Keywords | ||
|---|---|---|---|
| Author | Authorship | ||
| Corresponding Author | Funds | ||
| DOI | Column | ||
| Summary | |||
| Timeframe | - | ||
| Title | Keywords | ||
|---|---|---|---|
| Author | Authorship | ||
| Corresponding Author | Funds | ||
| DOI | Column | ||
| Summary | |||
| Timeframe | - | ||
[Objective] Carbonate reservoirs have become strategic targets for reserve expansion in China and the Middle East, driven by the dual goals of reducing carbon emissions and ensuring energy security. However, their significant heterogeneity, complex pore structures, and wettability changes present considerable challenges to the efficiency of CO2-based enhanced oil recovery(EOR). At the pore level, the interaction of capillary forces, viscous forces, and the evolution of multiphase interfaces causes unstable displacement fronts and severely limits sweep efficiency in low-permeability areas. [Methods] To tackle these issues, this study aims to reveal the pore-scale multiphase seepage mechanisms of brine–CO2 displacement in carbonate-coated heterogeneous porous media. This provides a microscopic foundation for optimizing CO2 flooding parameters and enhancing sweep performance in actual carbonate reservoirs. A heterogeneous pore network was constructed using a microfluidic chip, and calcium carbonate was coated in situ to simulate authentic carbonate reservoir surfaces and wettability. A series of visualization experiments were conducted at a controlled temperature(40 ℃). CO2 foam flooding and brine flooding at different injection rates were compared. A CCD imaging system was used to capture pore-scale evolution of oil, water, and gas phases, and gas saturation and residual oil distributions were quantified through image processing. To improve the accuracy of residual oil characterization, the ResNet152 deep neural network was trained on 2 885 labeled microfluidic sub-images from CO2 flooding, CO2–water alternating flooding, and brine flooding. Using weighted cross-entropy loss, AdamW optimization, and learning rate scheduling, the model achieved high classification accuracy for dispersed, mixed, and heterogeneous residual oil. [Results] Results showed that flooding performance was strongly affected by injection rate and pore-structure heterogeneity. At moderate flow rates(0.5–3 μL·min–1), CO2 foam greatly improved sweep efficiency, nearly eliminating residual oil saturation. Foam viscosity and the Jamin effect effectively suppressed viscous fingering and prevented preferential flow through high-permeability channels, forcing the displacing phase into low-permeability areas. Conversely, at very low injection rates(0.1 μL·min–1), foam instability caused large dispersed gas bubbles, limiting gas saturation to 25%, and hindered oil droplet mobilization, resulting in a high residual oil saturation of 42%. Gas saturation displayed a parabolic relationship with flow rate, with the maximum(93%) at 1 μL·min–1, where bubble size was smallest, and foam stability was optimal. Deep-learning-based oil classification also showed that brine flooding and CO2–water alternating flooding primarily produced dispersed residual oil, whereas surfactant-assisted CO2 flooding created a mixture of dispersed(49%), mixed(36%), and heterogeneous(14%) oil, reflecting foam instability and uneven sweep in highly heterogeneous zones. The model achieved a validation accuracy of 93%, confirming its effectiveness in pore-scale residual oil identification. [Conclusions] This study clarifies the mechanisms underlying brine–CO2 displacement in carbonate-coated heterogeneous media. Calcium carbonate coating increases hydrophobicity, delays breakthrough in high-permeability pathways, and significantly enhances sweep in low-permeability zones, reducing residual oil by up to 28%. CO2 foam flooding is highly sensitive to injection rate, with moderate flow rates producing stable foam, high gas saturation, and efficient oil mobilization, whereas very low or high rates reduce displacement stability. By combining microfluidic visualization and deep-learning image analysis, this research offers microscopic insights for optimizing CO2 flooding conditions and provides technical guidance for deploying CO2-based EOR in Middle Eastern carbonate reservoirs. The findings also support international cooperation under the Belt and Road Initiative and contribute to global efforts in the low-carbon, efficient development of carbonate oilfields.
[Objective] Biomass conversion and utilization technology is an important component of the curriculum in the major of New Energy Science and Engineering. At present, there are relatively few teaching experiments related to bioenergy in universities, and it is therefore necessary to explore and develop relevant experimental teaching activities. To improve teaching quality and enhance students' practical abilities, an experiment on the co-pyrolysis of biomass and waste plastics was designed, and the corresponding teaching experimental design was developed. [Methods] A thermogravimetric analyzer is an important instrument for studying the relationship between the mass of raw materials and temperature or time. Thermogravimetric analysis is one of the main methods for investigating the co-pyrolysis characteristics and synergistic effects of biomass and waste plastics. It is easy to operate and has a high level of experimental safety. In this study, a thermogravimetric analyzer was employed to design a teaching experiment on the co-pyrolysis of biomass and waste plastics. Waste polypropylene and Nannochloropsis were selected as the experimental raw materials, and the thermal weight loss characteristics of their individual pyrolysis and co-pyrolysis at different mixing ratios were investigated. The characteristic pyrolysis temperatures at different stages were determined. The synergistic effect of co-pyrolysis at different mixing ratios was analyzed by calculating the curve overlap ratio. The influence of different mixing ratios on the pyrolysis kinetic mechanism was discussed, and the reaction models and activation energies under different conditions were clarified. [Results] The results showed that the TG/DTG curves of the mixed samples were not equal to the sum of the TG/DTG curves of the two individual raw materials, although microalgae and plastic dominated different temperature ranges during pyrolysis. When the ratio of plastic to microalgae was 1∶2, 1∶1, and 2∶1, the differences between the experimental curves and the calculated curves were relatively large. At these ratios, the overlap ratio values were smaller, indicating that the synergistic interaction between plastic and microalgae was more significant. As the proportion of microalgae increased, the activation energy of co-pyrolysis showed a trend of first decreasing and then increasing. At the ratio of 1∶1, the synergistic effect was strongest, and the activation energy was lowest. The reaction models at different ratios also varied, including nucleation models, diffusion models, reaction order models, and phase boundary reaction models. These results indicate significant changes in the reaction control steps during the co-pyrolysis process. The ratio of raw materials not only influenced the energy required for pyrolysis but also substantially altered the fundamental mechanism of the pyrolysis reaction. [Conclusions] This study introduces the co-pyrolysis technology of biomass and waste plastics as a cutting-edge research topic into undergraduate experimental teaching and achieves close integration between teaching experiments and course content. Through this experiment, students can independently search the literature to understand the latest research frontiers in biomass energy, learn to operate a thermogravimetric analyzer, and conduct co-pyrolysis thermogravimetric experiments under different conditions. Students can also effectively process experimental data and, by combining experimental results with course knowledge and literature analysis, study the kinetics and synergistic effects of co-pyrolysis. This process can improve students' ability to analyze complex problems.
[Objective] The Middle East is a key region for oil and gas within the Belt and Road Initiative, but it faces challenges because most oil fields lack optimized fracturing designs. To achieve the desired stimulation results, high-displacement fracturing fluid operations are often used, which can lead to significant groundwater contamination from broken gel fluid. Visual plate experiments are commonly employed to optimize fracturing parameters; however, they are time-consuming and require substantial material, which hinders the development of green and intelligent oilfields. Therefore, this study aimed to develop an efficient machine learning model to predict the equilibrium proppant bank height within primary fractures and to use the output to inversely optimize the fracturing fluid displacement rate. [Methods] After a comprehensive review of existing literature, a dataset was established, and data standardization was carried out. By evaluating predictive performance using five-fold cross-validation and a test set, four machine learning algorithms were compared, and the best predictive model was selected. A method leveraging this optimal model to improve both the plate experiment process and the fracturing fluid displacement rate was then proposed. [Results] The random forest model showed the best predictive performance, achieving a coefficient of determination of 0.947, a root mean square error of 3.62, and a mean absolute error of 2.27 on the test set. For the validation set with 21 samples, the absolute error was within 1.50 cm, with a fitted curve slope of 1.07 and an intercept of-0.81. The inversely designed, optimized fracturing fluid displacement rate was significantly lower than the empirical rate, saving at least 106.2 m3 of fracturing fluid per hour while still ensuring effective fracture filling. [Conclusions] This study not only addresses current limitations of plate experiments but also offers guidance for designing fracturing fluid displacement rates in field operations within the Middle East.
[Objective] The rapid growth of the “Belt and Road” initiative has significantly boosted international oil and gas cooperation in resource-rich regions such as the Middle East, Central Asia, and Africa. However, these international projects often encounter challenges like complex geological environments, remote operation and maintenance needs, and varying technical standards. In this context, accelerating digital and intelligent transformation is crucial to help oilfields reduce costs, improve efficiency, and develop innovative industrial models. Traditional development methods struggle to handle the multi-source, high-dimensional data produced in modern oilfields. To promote the smart development of global oilfields, it is essential to build a digital-intelligent integrated cloud platform that combines data-driven decision-making, digital twin technology, and cross-disciplinary expertise. [Methods] We created a multi-module integrated cloud platform utilizing the interdisciplinary resources of the China–Saudi Petroleum Energy Belt and Road Joint Laboratory. The platform's architecture is based on a cloud-native framework, comprising a data layer, an AI algorithm layer, and a control service layer. It incorporates back-end microservices, an Oracle database for reliable data management, and a progressive front-end framework for interactive visualization. The core technology consists of four specialized modules that perform intelligent analysis of artificial-lift operating conditions, optimize artificial-lift design, assess oilfield production performance, and run virtual simulations. By integrating these modules, the platform forms a seamless intelligent workflow from real-time condition diagnosis to production optimization and comprehensive performance evaluation. [Results] The platform has been successfully implemented in several major international projects, most notably within the Ahdab Oilfield in Iraq. Using the first module, the platform demonstrated its ability to analyze block-wide conditions and identify specific issues, such as gas lock and supply shortages, with high accuracy. In the Ahdab field, which faces challenges like high water cut and production stability issues, the platform completed 156 well-cycle control measures. These optimizations led to a total oil increase of 110 700 tons, effectively achieving objectives related to boosting oil production, controlling water production, and reducing operational costs. Additionally, the evaluation module proved effective in identifying low-performing wells by ranking them based on standardized multidimensional scores, enabling engineers to implement targeted geological and technical interventions. Moreover, the platform has served as a key tool for international collaboration, supporting 27 national-level research projects and training over 1 000 petroleum professionals from partner countries such as Uganda.[ Conclusions]This study established a robust technological foundation for smart oilfield development. By integrating big data analytics, AI, and cloud computing, the platform bridges the gap between theoretical oilfield development and practical engineering applications. The successful deployment at the Ahdab Oilfield offers a replicable and scalable model for oilfields in Belt and Road partner countries. It enhances production management and decision-making efficiency and promotes international scientific cooperation. Future updates will aim to expand the platform's application across the entire oil and gas value chain and further explore the use of digital twins in global energy transformation.
[Objective] The rapid development of urban underground infrastructure in China has generated an urgent demand for engineering professionals proficient in safe and efficient blasting technologies. However, traditional teaching approaches for underground engineering blasting are constrained by three major limitations: safety risks that restrict practical experimentation, the inability to visualize dynamic processes such as stress wave propagation, and a significant gap between theoretical instruction and engineering practice. To address these challenges, this study proposes an innovative experimental teaching design that integrates field measurement with numerical simulation. The objective is to establish a safe, visualized, and practice-oriented teaching framework that enhances students' theoretical understanding, practical competence, and innovative problem-solving ability, thereby cultivating high-level talents capable of meeting the requirements of modern engineering practice. [Methods] A six-stage teaching methodology was designed to construct a closed-loop system of “Field Measurement → Model Prediction → Numerical Simulation → Optimization Design → Practical Verification.” The process begins with theoretical instruction covering electronic detonator technology, the Sadovsky vibration velocity prediction formula, safety assessment procedures, and the principles of LS-DYNA numerical simulation. Subsequently, students participate in case-based group design activities using real engineering cases, during which blasting schemes are developed and presented, followed by defense sessions under instructor guidance on key issues such as cut-hole configuration and delay timing. In the third stage, virtual simulation is introduced for iterative optimization, enabling students to repeatedly test and adjust parameters such as charge structure and initiation sequence within a risk-free environment. This is followed by field measurement and data modeling at an actual tunnel site, where students deploy vibration monitoring systems and use the collected data to determine site-specific parameters for the Sadovsky prediction model. The fifth stage involves numerical simulation and mechanistic visualization using LS-DYNA, in which field measurement data are used to validate three-dimensional models and visualize stress wave propagation and structural dynamic responses. Finally, the closed loop is completed through optimization design and field validation. Based on insights from simulation and measurement, students develop vibration control strategies by optimizing parameters such as charge weight per hole and inter-hole delay, and the effectiveness of the optimized designs is verified through comparative analysis of field vibration data. [Results] The implementation of this integrated teaching model produced significant improvements across several aspects of student learning. Students showed notable enhancement in mastering core blasting engineering competencies, including data acquisition, predictive modeling, numerical simulation, and dynamic design optimization. The visualization capabilities provided by numerical simulation fundamentally improved the understanding of complex mechanical processes and effectively bridged the gap between theoretical learning and engineering practice. The complete iterative cycle from virtual design to field verification fostered systematic engineering thinking and substantially improved students' problem-solving and innovation abilities. From a pedagogical perspective, the model overcame traditional limitations by reducing safety risks and lowering experimental costs through the integration of virtual simulation. The combined use of field experiments and virtual environments created a flexible and comprehensive learning platform that enables multi-parameter investigations that cannot be achieved through field experiments alone. At the same time, the teaching model promoted faculty development by encouraging instructors to strengthen industry–academia collaboration and enhance their practical engineering expertise. The process of transforming real engineering cases into teaching modules also improved curriculum design and pedagogical innovation, supporting the transition of instructors from knowledge transmitters to mentors guiding engineering practice and innovation. [Conclusions] This study develops and validates a collaborative experimental teaching model integrating field measurement and numerical simulation for underground engineering blasting education. By constructing a rigorous closed-loop teaching framework and achieving deep integration between empirical data and virtual simulation, the model effectively addresses the longstanding challenges of safety constraints, lack of visualization, and limited practical relevance in conventional teaching approaches. The proposed approach significantly enhances students' systematic knowledge acquisition, practical application ability, and technological innovation capacity, while simultaneously promoting the development of industry-oriented faculty with strong practical backgrounds. This teaching paradigm provides a scalable and replicable framework for cultivating high-level, application-oriented blasting engineering professionals and supports the objectives of emerging engineering education in meeting the demands of modern infrastructure development.
[Objective] In higher education, laboratory-based courses play a critical role in bridging theoretical knowledge and practical competence. However, students often encounter substantial challenges in independently mastering complex experimental procedures and resolving unexpected errors during laboratory sessions. Traditional instructional models, which rely heavily on instructor guidance and static teaching materials, are insufficient to address the individualized and real-time learning needs of students. With the rapid development of artificial intelligence and conversational agents, there is an increasing demand for intelligent systems capable of providing dynamic guidance, supporting autonomous learning, and improving diagnostic efficiency when students encounter experimental difficulties. This study addresses this need by developing and evaluating an artificial intelligence(AI) Agent–based intelligent tutoring system specifically designed for experimental teaching scenarios. The system supports a closed-loop learning process integrating self-learning, hands-on operation, and fault diagnosis. The primary objective is to improve laboratory task completion efficiency, enhance the quality of students' self-directed learning paths, and increase the success rate of fault diagnosis while fostering greater engagement and satisfaction. [Methods] The system was designed using a modular and service-oriented architecture consisting of four main components: a front-end interaction layer, an AI Agent module, a knowledge base system, and a log analysis module. The front-end interaction layer provides students with an intuitive and responsive interface that integrates multimodal content delivery, semantic highlighting, and a conversational window for natural language interaction, ensuring accessibility across devices. The AI Agent module functions as the intelligent core and incorporates natural language understanding, intent recognition, context modeling, and response generation. By integrating a large language model with customized prompt strategies, the Agent delivers adaptive feedback and targeted recommendations. A hybrid knowledge base was constructed by combining rule-based structures for rapid keyword matching with vector-based semantic retrieval to address complex or ambiguous queries. The knowledge base organizes experimental procedures, common error cases, and semantic links between conceptual knowledge and operational steps, enabling fine-grained alignment between theory and practice. To support personalized recommendations and adaptive interventions, a log analysis module continuously records and analyzes student interactions, including behavioral trajectories, error frequencies, and system responses. Empirical validation was conducted in an experimental class of the Computer Organization course at a university. An experimental group used the AI-supported system, whereas a control group followed conventional instructional practices. Data collection included task completion time, error resolution rate, quality of recommended self-learning paths, and post-course satisfaction surveys. [Results] The experimental evaluation demonstrated that the system produced notable improvements across multiple dimensions. Compared with the control group, students in the experimental group completed laboratory tasks with approximately 20% greater efficiency, reflecting the benefits of streamlined guidance and real-time support. The quality and adaptability of self-directed learning paths improved markedly, as the AI Agent generated context-aware recommendations that reduced redundant exploration and directed students toward more effective solutions. Fault diagnosis performance also improved substantially, with the success rate of problem identification and resolution exceeding 85%, significantly higher than that of the control group. In addition, survey results indicated high levels of student satisfaction with the system. Students particularly valued its ability to provide timely assistance, explain complex concepts in accessible terms, and promote greater autonomy during laboratory work. Qualitative feedback further suggested that the system encouraged independent learning by reducing reliance on instructors for immediate troubleshooting and supporting active problem-solving. [Conclusions] The findings demonstrate that the AI Agent–based intelligent tutoring system effectively enhances laboratory teaching by addressing both cognitive and operational challenges encountered by students. By integrating semantic modeling of experimental tasks, multi-source fault knowledge bases, and dialog-driven intent recognition, the system provides a comprehensive solution supporting the full cycle of self-learning, practical experimentation, and diagnostic reasoning. Its modular architecture enables adaptation to different subject domains, while the hybrid knowledge base and real-time log analysis provide a foundation for continuous improvement and scalability. The observed improvements in task efficiency, learning path optimization, fault resolution, and student satisfaction highlight the system's potential to transform experimental pedagogy in higher education. Beyond its immediate educational benefits, this study proposes a replicable framework for applying AI Agents in educational environments, offering guidance for future research and practice in human–AI collaborative learning. Overall, the results underscore the transformative potential of artificial intelligence in promoting autonomous learning, reducing instructional bottlenecks, and advancing the modernization of laboratory teaching.
[Objective] Current experimental teaching in the computer vision field faces challenges such as outdated content and limited integration of cutting-edge research into talent cultivation. These limitations are particularly problematic in courses designed for senior students who seek practical research experience. To address this issue, this paper designs a teaching experiment on face prototype reconstruction based on adversarial attack defense. The proposed experiment has significant pedagogical value because it addresses the real-world problem in which face recognition fails for images affected by variations such as pose, expression, and illumination, while also incorporating the critical issue of AI security. The experiment aims to guide students systematically through the entire process of model design, training, and testing to reconstruct standardized, identity-preserved face prototypes from contaminated images, while simultaneously learning the design and implementation of adversarial defense strategies. [Methods] The method used in the proposed experiment consists of two core components: a prototype reconstruction generative adversarial network and an adversarial defense network. Specifically,(1) the prototype reconstruction generative adversarial network comprises an encoder–decoder structured generator and a multi-task discriminator. It is designed to reconstruct variation-free face prototypes by jointly learning prototypes and representations within a unified framework. The network is optimized through an adversarial game between the generator and the discriminator. As the discriminator improves its ability to perform identity prediction, facial variation detection, and real–fake prototype classification, the generator simultaneously learns to produce realistic prototype images that preserve identity information while removing facial variations, thereby deceiving the discriminator.(2) The adversarial defense network aims to mitigate the adverse effects of adversarial perturbations by preprocessing the input images of the prototype reconstruction generative adversarial network through a combination of image denoising and matrix estimation. The network includes four key modules: a feature enhancement module for extracting latent features, an attention module that refines noise-related features using an attention mechanism, a residual learning module that subtracts the predicted noise from the original input, and a matrix estimation module employing Universal Singular Value Thresholding(USVT) to restore the global image structure. For the experimental procedure, students are guided through three stages. First, data preparation is conducted by dividing facial images under visible light in the LAMP-HQ dataset into training and testing sets. Second, model training is performed for both the prototype reconstruction generative adversarial network and the adversarial defense network, during which students are encouraged to adjust hyperparameters to better understand the training process. Third, model testing is conducted by generating adversarial samples to evaluate the effectiveness and robustness of prototype reconstruction under adversarial attacks. [Results] Through both qualitative and quantitative evaluation, students can verify the effectiveness of the proposed method based on the experimental results. In the qualitative evaluation, visualization of reconstruction results demonstrates the effectiveness of the prototype reconstruction generative adversarial network in generating identity-preserved prototypes from contaminated inputs, as well as the effectiveness of the adversarial defense network in restoring prototype image quality that may otherwise be significantly degraded by adversarial attacks. In the quantitative evaluation, objective metrics including the Rank-10 recognition rate and image sharpness measured by the Laplacian variance method are used to further assess performance. The results confirm the negative impact of adversarial attacks on prototype reconstruction, with the Rank-10 recognition rate decreasing by 22.51%, 31.86%, and 38.25% at perturbation magnitudes of 0.15, 0.20, and 0.25, respectively, while image sharpness exhibits a similar downward trend. After integrating the adversarial defense network, the Rank-10 recognition rate recovers by 7.61%, 14.64%, and 15.86% at the corresponding perturbation magnitudes, and image sharpness demonstrates a similar upward trend. [Conclusions] This paper develops a comprehensive teaching experiment that translates advanced research findings in computer vision and AI security into a feasible pedagogical case. The experiment bridges the gap between theory and complex engineering challenges by guiding students through model design, implementation, and rigorous experimental analysis for face prototype reconstruction based on adversarial attack defense. Teaching practice demonstrates that the experiment helps students transition from passive verifiers to active problem-solvers by improving their engineering skills, empirical research abilities, and innovative thinking, while simultaneously fostering awareness of AI security. The proposed experiment therefore provides a valuable and replicable paradigm for reforming computer vision curricula.
[Objective] Conventional methods for assessing gel breaker efficiency in viscoelastic fracturing fluids mainly rely on monitoring a single parameter, the viscosity of the broken fluid supernatant. This approach, though simple, has significant limitations and fails to capture the full physical state of broken gel, especially when residual undissolved fragments(“fish-eyes”) remain, impairing fracture conductivity and causing formation damage. This incomplete assessment may lead to suboptimal selection and dosage of the breaker, ultimately impacting well productivity. Therefore, there is a pressing need for more holistic and reliable methods for a comprehensive evaluation of the gel-breaking process. This study aimed to develop and validate a new multi-index, comprehensive evaluation system to provide an improved scientific tool for optimizing fracturing fluid formulations and breaker strategies, particularly in complex reservoirs. [Methods] A dual approach was used, consisting of method development and systematic experimental validation. Two representative gelled fracturing fluids were formulated: a widely used borate-crosslinked hydroxypropyl guar(HPG) gel and a zirconium-crosslinked synthetic polymer gel. Fourteen breakers from different mechanistic categories were selected, including oxidative agents(Ammonium Persulfate, Potassium Persulfate), acidic agents(oxalic acid, citric acid, ammonium hydrogen sulfate), and various chelating agents(e.g., EDTA-2 Na, sodium citrate, sodium tartrate). A key feature of this study is the development of a new multi-parameter evaluation framework. This framework moves beyond single-point viscosity measurement by introducing a comprehensive “integrity index”(ψ), which is derived from simultaneous measurements of(1) the dynamic viscosity of the clear broken gel filtrate and(2) the mass of remaining solid-like gel fragments after a standardized breaking process. A specially designed calculation model, involving segmented functions, converts these two physical measurements into a single, continuous ψ value ranging from 1.0(intact gel) to near 0.0(complete breakdown), providing a nuanced and quantitative scale for assessing breaking extent. The accuracy and reliability of this new ψ-based system were thoroughly verified by comparing it with established analytical techniques. Macroscopic validation involved detailed rheological analysis of the evolution of viscoelastic moduli(G', G") and steady-shear viscosity throughout the breaking process. Microscopic validation was achieved by directly analyzing changes in the molecular weight distribution and polydispersity index of a polymer via gel permeation chromatography(GPC), confirming chemical degradation of the polymer chains. [Results] This new multi-index approach produced clear and distinct results. The integrity index ψ effectively served as a sensitive metric for continuously ranking the performance of all tested breakers across both gel types. Oxidative breakers like APS and KPS, along with the acidic breaker ammonium hydrogen sulfate, showed exceptional efficiency in breaking the borate-crosslinked HPG gel. At a moderate concentration of 0.25%, these breakers reduced ψ below 0.1, indicating near-complete gel disintegration. In contrast, most chelating agents had minimal effect on this gel system, with high ψ values remaining. Similar trends were observed for the zirconium-crosslinked polymer gel, confirming the robustness of the method across different chemistries. Oxidative and acidic breakers again proved most effective. Among chelators, EDTA-2 Na showed the highest activity in this metal-crosslinked system, although overall performance based on ψ remained inferior to the top oxidative/acidic breakers. Validation data strongly supported the ψ index results. The viscoelastic network of samples with low ψ values collapsed entirely in rheological tests, transitioning from a solid-like gel to a Newtonian fluid. Conversely, samples with higher ψ values retained measurable elasticity. Crucially, GPC analysis provided molecular-level evidence: effective breakers with low ψ values caused a significant reduction in the weight-average molecular weight and increased molecular weight distribution broadening, confirming extensive polymer chain scission. This close correlation between the macroscopic ψ index, rheology, and microscopic polymer analysis conclusively validated the proposed comprehensive evaluation method. [Conclusions] A novel multi-index method for assessing the extent of gel-breaking in fracturing fluids was successfully developed and validated. The introduced integrity index ψ, which combines information from fluid viscosity and residual gel mass, offers a major improvement over traditional single-parameter methods by delivering a more complete, quantitative, and reliable characterization. The method effectively differentiates breaker performance and correlates well with independent rheological and polymer degradation analyses. This robust framework can be a valuable practical tool for optimizing breaker selection and dosing, helping to minimize fracture-conductivity damage and enhance well productivity. As a technical innovation with direct field application, it has great potential to improve stimulation treatment efficiency, especially in challenging reservoir environments, and adds valuable insights to the broader field of production chemistry.
[Objective] Underground gas and energy storage are emerging interdisciplinary fields that utilize subsurface geological space to store gaseous substances and, in some cases, surplus energy that is converted into gaseous carriers. The large-scale deployment of carbon, seasonal natural gas, geological hydrogen, and compressed air energy storage depends critically on a thorough understanding of multiphase flow mechanisms in porous media. However, accurately quantifying trace amounts of fluids under high-temperature and high-pressure conditions remains a major bottleneck in core-scale gas–liquid two-phase flow experiments. Conventional experimental approaches are largely limited to bulk statistical observations and fail to monitor the temporal and spatial evolution of fluid migration and distribution in real time. Furthermore, numerical simulation methods for accurate modeling through scale-up remain insufficiently developed. To address these challenges, this study conducts integrated experimental and numerical simulation investigations focused on underground gas and energy storage. [Methods] A comprehensive experimental platform was designed and constructed, consisting of three tightly coupled subsystems for displacement, nuclear magnetic resonance(NMR) measurement, and metering. Gas injection experiments into brine-saturated cores were conducted following standardized core-flooding procedures, employing stepwise injection and intermittent NMR scans. NMR measurements were performed using three complementary modalities: T2 relaxation spectra to quantify pore-scale fluid occupancy and movable pore space; SE-SPI maps to capture one-dimensional spatial distributions along the core axis via virtual slicing; and NMR imaging to obtain a two-dimensional visualization of dynamic displacement patterns and gravity segregation. To translate the laboratory findings to field scales, a hierarchical numerical simulation workflow was established. A core-scale model was constructed, enabling mechanistic interpretation of observed NMR patterns. Further, a two-dimensional plane radial flow model was developed to examine cyclic injection–production behavior and gas–water transition-zone evolution. Finally, a three-dimensional field-scale model was constructed to replicate the complex structures and heterogeneity of real formations and provide fully visualized multiphase–multicomponent dynamics for production forecasting and optimization. [Results] The NMR-based experimental system enabled multidimensional, real-time quantification of gas–water processes, which are difficult to resolve using conventional methods. T2 spectra enabled accurate quantification of hydrogen-bearing fluids in porous media and revealed that evaporation is an additional transport pathway that can reduce residual water saturation, indicating a coupled “displacement–evaporation” mechanism. The SE-SPI maps revealed pronounced spatial non-uniformity in fluid distributions, indicating that injected gas preferentially concentrated in the upper part of the formation. NMR imaging indicated that the evaporation of residual water into the gas phase could mobilize water mass transport without requiring bulk liquid flow. Numerical simulations provided consistent mechanistic explanations and enabled reliable scale-up insights, and core-scale simulations successfully replicated key features observed in the experiments, including upward gas accumulation driven by gravity. The two-dimensional single-well model demonstrated a clear zonal storage pattern, comprising gas, gas–water transition, and outer water zones, and clarified the aquifer's role in stabilizing reservoir pressure while driving water invasion during production. The three-dimensional field-scale model enabled visualization of convective behavior and heterogeneity-induced fingering and identified leakage-prone areas that require operational constraints and continuous monitoring. [Conclusions] This study proposes and validates an integrated experimental–numerical framework for underground gas and energy storage research. It combines an NMR-based laboratory system with multiscale, multiphase, multicomponent simulations spanning from core to field scale. This platform addresses longstanding limitations in trace fluid measurement and spatiotemporal visualization, enabling a systematic interpretation of storage mechanisms, migration behaviors, and distribution patterns under realistic formation conditions. By bridging laboratory measurements with well-and field-scale modeling, the framework establishes a practical basis for evaluating storage sites, designing injection and production processes, and optimizing underground gas storage in a risk-informed manner. Leveraging the China–Saudi “Belt and Road” joint laboratory, this methodology provides a scalable pathway for international collaboration and technology transfer in underground gas and energy storage, particularly in Middle Eastern regions with a number of depleted reservoirs and in China, which possesses extensive aquifer resources. This approach thus supports the development of cleaner and more diversified energy systems.
[Objective] This study aims to develop a comprehensive experimental framework for an intelligent control technology course by introducing a battery testing platform. The experiment integrates battery modeling, intelligent algorithm design and simulation, and experimental validation within an engineering-oriented battery testing scenario. This approach addresses the limitations of traditional experiments that rely on open-source datasets and lack practical system-level case studies. This approach systematically improves the modeling, algorithm application, and engineering problem-solving abilities of students. It also enhances the hands-on skills of students, broadens their analytical thinking, and lays a solid foundation for them to become qualified engineering and technical professionals in the future. [Methods] This experimental design implements a closed-loop experimental process of “data acquisition–theoretical modeling–algorithm design–result optimization.” Using the NEWARE BTS-4008 battery testing platform, real-time voltage and current data of Panasonic 18 650 batteries are collected under constant-temperature conditions. Based on these data, students establish a second-order RC equivalent circuit model and use the particle swarm optimization(PSO) algorithm for parameter identification. Two different methods are used to estimate the state of charge(SOC): one is the model-based extended Kalman filter(EKF) estimation method, and the other is the data-driven random forest(RF) algorithm. A weighted fusion strategy is used to process the estimates from the two methods, improving the accuracy and robustness of the overall estimation scheme. [Results] The parameters identified using the PSO algorithm can effectively reproduce the terminal voltage curve, proving the accuracy and reliability of the parameters and providing a reliable foundation for subsequent SOC estimation. Under dynamic stress test conditions, the performance of fusion and individual methods was evaluated and compared. Compared with the single method, the mean absolute error of the EKF and RF–weighted fusion methods decreased by 16% and 47%, respectively, the root mean square error decreased by 7% and 48%, respectively, and the coefficient of determination(R2) was closer to 1, proving that the fusion method can overcome the limitations of individual methods and significantly improve estimation performance. [Conclusions] Students collected the necessary data through the experimental platform and constructed a battery model based on course knowledge. They then improved the accuracy of the model by adjusting algorithm parameters. This process enhanced their engineering application and modeling abilities while addressing the gap between theory and practice in traditional teaching. This experimental design overcomes the limitations of single algorithm application, achieving battery SOC estimation through the fusion of the EKF algorithm and data-driven RF methods and further guiding students to perform weighted fusion of the estimation results from the two algorithms. This design helps students understand the applicable scenarios, advantages, and limitations of different intelligent algorithms and breaks the rigid thinking of traditional single-solution approaches by integrating real engineering objects and multiple technical paths. It cultivates innovative thinking by encouraging students to analyze problems from multiple dimensions and integrate technical solutions, effectively meeting the training goals of engineering and technical talent development. In the future, the experimental scenarios can be further expanded to continuously improve the depth and breadth of course teaching and provide practical teaching support for talent cultivation in the field of intelligent control.