Unconventional Resources最新文献

Real time implementation for robust adaptive sliding mode predictive control of PMSM 永磁同步电机鲁棒自适应滑模预测控制的实时实现

IF 4.6

Unconventional Resources

Pub Date : 2026-01-02 DOI: 10.1016/j.uncres.2026.100304

Rachid Ibrahimi , Mohamed said Adouairi , Abdennabi Morchid , Badre Bossoufi , Paweł Skruch , Saleh Mobayen

Model Predictive Control is recognized as a promising approach for electric drives, with particular interest in the development of robust and high-performance predictive models. In this work, we propose an Adaptive Integral Sliding Mode Predictive Control for Permanent Magnet Synchronous Motors, combining a predictive model with an adaptive law based on the integral sliding mode. This method automatically adjusts the sliding function limit, reducing the chattering phenomenon while enhancing the system dynamics.

To ensure compatibility with industrial electronic boards, the Adaptive Integral Sliding Mode Predictive Control was implemented following the V-cycle development process, including Model-in-the-Loop, Software-in-the-Loop, and Processor-in-the-Loop validation. This framework facilitates the deployment of embedded control software in the automotive sector and provides a cost-effective evaluation of the hardware implementation.

Furthermore, real-time simulations of control, Invariant Sliding Mode Predictive Control, and Sliding Mode Control configurations were carried out on the dSPACE DS1104 platform, showing excellent correlation with MATLAB/Simulink results. Experimental validation on the STM32F4 board confirms that the proposed approach offers faster response to load torque disturbances and better performance over a wide speed range, demonstrating its reliability, robustness, and effectiveness.

模型预测控制被认为是一种很有前途的电力驱动方法，特别是对鲁棒和高性能预测模型的开发。在这项工作中，我们提出了一种永磁同步电机的自适应积分滑模预测控制，将预测模型与基于积分滑模的自适应律相结合。该方法自动调节滑动函数极限，在增强系统动力学的同时减少了抖振现象。为了确保与工业电子电路板的兼容性，自适应积分滑模预测控制遵循v周期开发过程，包括模型在环、软件在环和处理器在环验证。该框架促进了嵌入式控制软件在汽车领域的部署，并提供了对硬件实现的成本效益评估。此外，在dSPACE DS1104平台上对控制、不变滑模预测控制和滑模控制配置进行了实时仿真，结果与MATLAB/Simulink的结果具有良好的相关性。在STM32F4板上的实验验证证实了所提出的方法对负载扭矩干扰的响应速度更快，并且在较宽的速度范围内具有更好的性能，证明了其可靠性，鲁棒性和有效性。

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引用次数: 0

Modeling and simulation of hybrid fuzzy-PID and model predictive control for enhanced dual-axis photovoltaic tracking precision 提高双轴光伏跟踪精度的模糊pid与模型预测混合控制建模与仿真

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2025.100303

Rezi Delfianti , Mohammed Mareai , Federico Minelli , Catur Harsito , Fauzan Nusyura

This paper presents a comprehensive evaluation of several control strategies for dual-axis solar tracking systems, including proportional–integral–derivative, fuzzy logic, fuzzy–PID, and fuzzy–PID enhanced with model predictive control. Each controller was implemented in MATLAB/Simulink to analyse its dynamic and steady-state behaviour under identical conditions. The findings reveal that hybrid and MPC-based controllers achieve superior tracking precision and response smoothness compared to single-loop designs. Specifically, the fuzzy-tuned PID exhibits the fastest rise time of 12.53

m s

but with a higher overshoot of 18.45 %. In contrast, the standalone fuzzy controller offers superior stability with a minimal overshoot of 0.50 %, though at the expense of slower dynamics with a rise time of 91.81

m s

. The proposed MPC–Fuzzy–PID Series hybrid achieves a rapid rise time of 16.02

m s

and a settling time of 0.2

s,

providing a balanced trade-off between speed, stability, and computational efficiency, making it suitable for real-time solar tracking applications. Overall, the study demonstrates that controller performance depends on the specific operational goals whether prioritizing rapid response, precision, or robustness highlighting the importance of adaptive hybrid control design in sustainable energy systems.

本文综合评价了双轴太阳跟踪系统的几种控制策略，包括比例-积分-导数、模糊逻辑、模糊pid和模型预测控制增强的模糊pid。每个控制器在MATLAB/Simulink中实现，分析其在相同条件下的动态和稳态行为。研究结果表明，与单回路设计相比，混合和基于mpc的控制器具有更高的跟踪精度和响应平稳性。具体而言，模糊调谐PID的上升时间最快，为12.53 ms，但超调量较高，为18.45%。相比之下，独立模糊控制器提供了卓越的稳定性，最小超调为0.50%，但代价是较慢的动态，上升时间为91.81 ms。所提出的MPC-Fuzzy-PID系列混合实现了16.02 ms的快速上升时间和0.2 s的沉降时间，在速度，稳定性和计算效率之间提供了平衡的权衡，使其适合实时太阳跟踪应用。总体而言，该研究表明，控制器的性能取决于具体的操作目标，是否优先考虑快速响应、精度或鲁棒性，这突出了自适应混合控制设计在可持续能源系统中的重要性。

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引用次数: 0

Modeling geothermal reservoirs permeability based upon NMR laboratory data 基于核磁共振实验室数据的地热储层渗透率建模

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2026.100307

Kusum Yadav , Lulwah M. Alkwai , Shahad Almansour , Debashis K. Dutta , Ahmad Adel Abu-Shareha , Mehrdad Mottaghi

Accurate permeability characterization is crucial for the efficient and sustainable development of geothermal resources. However, conventional methods like well testing and core analysis are often expensive and fail to capture the complex, heterogeneous nature of geothermal reservoirs. While Nuclear Magnetic Resonance (NMR) logging provides valuable insights into pore structure, its traditional permeability models are often unreliable in high-temperature, high-salinity geothermal environments. A novel data-driven methodology is introduced for modeling permeability in geothermal reservoirs by integrating Nuclear Magnetic Resonance (NMR) laboratory measurements with advanced machine learning algorithms. The approach employs a curated dataset of geothermal core samples, utilizing porosity, logarithmic mean transverse relaxation time (T2lm), and mode transverse relaxation time (T2mode) as predictive features across multiple learning models. Outlier detection was conducted using the Leverage technique, while model reliability was validated through K-fold cross-validation. Among the tested algorithms, the Decision Tree model demonstrated superior performance, yielding the highest coefficient of determination (R²) and the lowest error metrics. Sensitivity analysis further revealed porosity as the most dominant factor influencing geothermal permeability. The findings validate the utility of using ensemble soft computing to boost the accuracy of permeability prediction, presenting a valuable and affordable alternative to traditional techniques. Our findings bridge the gap between core analysis and computational modeling, paving the way for more accurate geothermal reservoir characterization and optimization.

准确的渗透率表征对地热资源的高效可持续开发至关重要。然而，常规的方法，如试井和岩心分析，往往是昂贵的，并且无法捕捉到地热储层的复杂性和非均质性。虽然核磁共振（NMR）测井提供了宝贵的孔隙结构信息，但其传统渗透率模型在高温、高盐度地热环境中往往不可靠。引入了一种新的数据驱动方法，通过将核磁共振（NMR）实验室测量与先进的机器学习算法相结合，来模拟地热储层的渗透率。该方法采用地热岩心样本的精心整理的数据集，利用孔隙度、对数平均横向松弛时间（T2lm）和模式横向松弛时间（T2mode）作为多个学习模型的预测特征。采用杠杆技术进行离群值检测，通过K-fold交叉验证验证模型的可靠性。在测试的算法中，决策树模型表现出优异的性能，产生最高的决定系数（R2）和最低的误差指标。敏感性分析进一步揭示孔隙度是影响渗透率的最主要因素。研究结果验证了使用集成软计算提高渗透率预测准确性的实用性，为传统技术提供了一种有价值且经济实惠的替代方案。我们的发现弥补了岩心分析和计算建模之间的差距，为更准确地表征和优化地热储层铺平了道路。

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引用次数: 0

Comparative impact of cold water and thermochemical cooling methods on breakdown pressure for improved stimulation in unconventional formations 冷水和热化学冷却方法对非常规地层破裂压力的影响比较，以提高增产效果

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2026.100309

Fahad Khan , Arshad Raza , Mohamed Mahmoud , Murtadha J. AlTammar , Talal Al Shafloot

High breakdown pressure poses a significant challenges in terms of pumping pressure and associated costs during stimulation operations in unconventional reservoirs. These reservoirs are characterized with low porosity, low permeability, and high in-situ temperature due to greater depths. To address this challenge, the current study investigates the effectiveness of various cooling strategies and their comparative impact on the breakdown pressure and strength of unconventional rocks (Kentucky sandstone and Eagleford shale). These rocks were heated up to 150 °C and cooled down by following three different strategies: ⅰ) spontaneous cooling i.e. without any external aid ⅱ) cooling with cold water and ⅲ) cooling with endothermic chemicals involving NH₄Cl and NaOH. With endothermic cooling, the temperature of Kentucky sandstone decreases from 150 °C to 56.7 °C in 14 s, while Eagleford shale cools from 150 °C to 43.8 °C in 10 s. The endothermic cooling was followed by the Cold water and spontaneous cooling which showed slower and less pronounced temperature drops in both rocks as compared to the endothermic cooling. The endothermic cooling also leads to highest reduction in rock strength and breakdown pressure. The strength shows a reduction of 21.9 % in Kentucky sandstone and 25.4 % in Eagleford shale while the breakdown pressure reduces by 38.6 % and 37.3 % for the Kentucky sandstone and Eagleford shale respectively. The study also shows the structural changes in the rocks, particularly rock morphology and pore volume. FIB-SEM analysis shows the development of multiple micro-cracks in the rocks which plays an important role in reducing the breakdown pressure. The outcomes of this study indicate that pre-fracturing cooling treatment using endothermic fluids can enhance the effectiveness of hydraulic fracturing operations by reducing the formation breakdown pressure.

在非常规油藏增产作业中，高破裂压力给泵送压力和相关成本带来了重大挑战。这些储层具有低孔隙度、低渗透率、深层温度高的特点。为了应对这一挑战，目前的研究调查了各种冷却策略的有效性，以及它们对非常规岩石（肯塔基砂岩和Eagleford页岩）破裂压力和强度的比较影响。将这些岩石加热至150℃后，采用三种不同的冷却策略进行冷却：ⅰ)自然冷却，即没有任何外部帮助；ⅱ)冷水冷却；ⅲ)含NH4Cl和NaOH的吸热化学物质冷却。通过吸热冷却，Kentucky砂岩的温度在14 s内从150°C降至56.7°C， Eagleford页岩在10 s内从150°C降至43.8°C。吸热冷却之后是冷水和自发冷却，与吸热冷却相比，这两种岩石的温度下降速度更慢，也更不明显。吸热冷却还能最大程度地降低岩石强度和破裂压力。肯塔基砂岩强度降低21.9%，Eagleford页岩强度降低25.4%，破裂压力降低38.6%，Eagleford页岩强度降低37.3%。研究还揭示了岩石的结构变化，特别是岩石形态和孔隙体积的变化。FIB-SEM分析表明，岩石中存在多个微裂纹，这对降低破裂压力起着重要作用。研究结果表明，采用吸热流体进行压裂前冷却处理可以通过降低地层破裂压力来提高水力压裂作业的有效性。

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引用次数: 0

Robust multi-time-scale scheduling of microgrids with renewable energy interpretation and bidirectionally controlled electric vehicles using adaptive Harris hawks optimization 基于自适应哈里斯鹰优化的可再生能源解释和双向控制电动汽车微电网鲁棒多时间尺度调度

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2026.100305

Zhuoting Cheng , Rendong Ji , Hai Tao , Ahmed N. Abdalla , Xiu Tang , Shifeng Li

The increasing penetration of renewable energy sources and bidirectionally controlled electric vehicles into microgrids introduces significant uncertainty in generation output, electricity market prices, and user behavior. These uncertainties pose substantial challenges to achieving reliable, economical, and flexible microgrid scheduling. This paper proposes a robust multi-time-scale scheduling framework that integrates renewable energy interpretation with advanced optimization techniques. A distributionally robust optimization model is developed for day-ahead planning, capturing renewable energy variability and electric vehicle dynamics through an ambiguity set constructed from historical data. The non-convex scheduling problem is solved using an adaptive Harris Hawks Optimization algorithm, which enhances convergence stability and search diversity. During real-time operation, a model predictive control strategy refines dispatch decisions every 15 min based on updated forecasts, ensuring responsiveness to operational fluctuations. The microgrid system under study includes wind turbines, photovoltaic units, microturbines, battery energy storage, and a fleet of bidirectionally controlled electric vehicles. Simulation results demonstrate that the proposed framework significantly reduces operating costs, mitigates power fluctuations, and enhances renewable energy utilization compared to conventional deterministic methods. These findings validate the effectiveness of the proposed strategy in delivering resilient, cost-effective, and renewable-integrated microgrid scheduling under uncertainty.

可再生能源和双向控制的电动汽车越来越多地渗透到微电网中，在发电量、电力市场价格和用户行为方面带来了重大的不确定性。这些不确定性对实现可靠、经济、灵活的微电网调度提出了重大挑战。本文提出了一种鲁棒的多时间尺度调度框架，该框架将可再生能源解释与先进的优化技术相结合。建立了一种分布式鲁棒优化模型，通过历史数据构建的模糊集捕获可再生能源的可变性和电动汽车的动态。采用自适应Harris Hawks优化算法求解非凸调度问题，提高了算法的收敛稳定性和搜索多样性。在实时运行中，模型预测控制策略每15分钟根据更新的预测优化调度决策，确保对运行波动的响应。正在研究的微电网系统包括风力涡轮机、光伏发电装置、微涡轮机、电池储能和一批双向控制的电动汽车。仿真结果表明，与传统的确定性方法相比，该框架显著降低了运行成本，缓解了电力波动，提高了可再生能源的利用率。这些发现验证了所提出的策略在不确定性下提供弹性、成本效益和可再生集成微电网调度的有效性。

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引用次数: 0

A review on microgrid control: Conventional, advanced and intelligent control approaches 微电网控制综述：传统、先进和智能控制方法

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2025.100297

Kalpana Bijayeeni Samal, Mitali Mahapatra, Swagat Pati, Manoj Kumar Debnath

A microgrid is an advanced, small-scale power grid comprising conventional and non-conventional generation units, loads, and controllers. One of the reasons behind the growing interest and research in developing the microgrid concept is the seamless integration of renewable energy into the power grid. This concept also helps to ensure a reliable and economical local power supply to remote areas. Furthermore, renewable energy systems supply the network with the necessary power, which may be utilized directly or saved using energy storage devices. This approach to power management requires an effective control system to improve the microgrid's overall performance. Selection and design of an efficient controller for microgrids is a challenging task. There are several challenges to design a stable and effective control structure for a microgrid. This review article provides the details based on 194 published research articles in between 2010 and 2025, covering microgrid architectures, working principles, their harmfulness and control strategies. This review articulates an innovative classification of microgrid control methods, categorizing them as conventional, advanced, and intelligent techniques, along with a comparative evaluation framework based on performance metrics such as robustness, settling time, computational load, and communication requirements. It also identifies existing research gaps as well as their benefits and limitations and suggests a path forward for controller development, aiming to achieve robust and autonomous microgrids. The major findings indicate that hybrid intelligent control systems, which incorporate adaptive, predictive, and AI-based methodologies, excel in complex and dynamic microgrid contexts.

微电网是一种先进的小型电网，由传统和非常规发电机组、负载和控制器组成。开发微电网概念的兴趣和研究日益增长的原因之一是可再生能源与电网的无缝集成。这一概念也有助于确保偏远地区的可靠和经济的本地电力供应。此外，可再生能源系统向网络提供必要的电力，该电力可以直接利用或使用储能设备节省。这种电源管理方法需要一个有效的控制系统来提高微电网的整体性能。选择和设计一种高效的微电网控制器是一项具有挑战性的任务。为微电网设计稳定有效的控制结构面临着诸多挑战。本文基于2010年至2025年间发表的194篇研究论文，详细介绍了微电网的结构、工作原理、危害和控制策略。这篇综述阐述了微电网控制方法的创新分类，将它们分为传统、先进和智能技术，以及基于鲁棒性、稳定时间、计算负荷和通信要求等性能指标的比较评估框架。它还指出了现有的研究差距以及它们的优点和局限性，并为控制器的发展提出了一条前进的道路，旨在实现健壮和自主的微电网。主要研究结果表明，混合智能控制系统结合了自适应、预测和基于人工智能的方法，在复杂和动态的微电网环境中表现出色。

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引用次数: 0

Modeling capillary bound water in limestone reservoirs 石灰岩储层毛细管束缚水模拟

IF 4.6

Unconventional Resources

Pub Date : 2026-01-01 DOI: 10.1016/j.uncres.2026.100308

Lulwah M. Alkwai , Kusum Yadav , Shahad Almansour , Debashis Dutta , Hojjat Abbasi

Precise evaluation of capillary bound water is critical for accurate hydrocarbon reserve estimation and reliable forecasting of reservoir behavior. Traditional laboratory techniques and empirical models often struggle with limitations in both accuracy and efficiency. This study introduces a data-driven approach to precisely assess capillary bound water saturation (CABW) in limestone reservoirs, a critical yet challenging aspect of hydrocarbon reserve estimation. This research leverages comprehensive core-analysis data, such as He-porosity, Gas permeability, and Nuclear Magnetic Resonance (NMR) T2lm data from diverse limestone core samples to train and validate various machine learning models. We evaluated the performance of several machine learning models. Rigorous data integrity checks were performed using the Leverage technique for outlier detection, while sensitivity analysis quantified the impact of features on CABW. Model robustness was confirmed through K-fold cross-validation. The CNN model demonstrated superior performance and gas permeability as the strongest interpreter of CABW, a finding that underscores the physical validity of our model at the core scale. This study highlights the strong capability of soft computing approaches to enhance petrophysical modeling in complex reservoirs, providing a scalable and economical alternative to traditional techniques. By integrating core analysis with computational modeling, the methodology advances reservoir characterization and reserve estimation with greater accuracy and reliability.

准确评价毛细束缚水是准确估计油气储量和可靠预测储层动态的关键。传统的实验室技术和经验模型经常在准确性和效率上受到限制。该研究引入了一种数据驱动的方法来精确评估石灰岩储层的毛细管束缚水饱和度（CABW），这是油气储量估算的一个关键但具有挑战性的方面。本研究利用综合岩心分析数据，如不同石灰岩岩心样品的he孔隙度、气体渗透率和核磁共振（NMR） T2lm数据，来训练和验证各种机器学习模型。我们评估了几个机器学习模型的性能。使用杠杆技术进行了严格的数据完整性检查，用于异常值检测，而敏感性分析量化了特征对CABW的影响。通过K-fold交叉验证证实了模型的稳健性。CNN模型表现出卓越的性能和渗透率，是最强的CABW解释器，这一发现强调了我们的模型在核心尺度上的物理有效性。这项研究强调了软计算方法在复杂储层中增强岩石物理建模的强大能力，为传统技术提供了一种可扩展且经济的替代方案。通过将岩心分析与计算建模相结合，该方法提高了储层表征和储量估计的准确性和可靠性。

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引用次数: 0

Prediction of pressure build-up distribution and geomechanical analysis in a CO2 sequestration reservoir using optimized artificial intelligence models 基于优化人工智能模型的CO2封存油藏压力累积分布预测与地质力学分析

IF 4.6

Unconventional Resources

Pub Date : 2025-12-17 DOI: 10.1016/j.uncres.2025.100290

Emmanuel Karikari Duodu , Eric Thompson Brantson , Binshan Ju , Richard Fiifi Annan , Eugene Jerry Adjei

Injecting carbon dioxide into geological formations is a common method, but it carries significant geomechanical risks due to pore pressure buildup. This pressure increase can cause caprock failure, fault reactivation, poroelastic responses, and compromise well integrity. In this study, we develop a predictive model for effective mean stress that directly links reservoir pressure buildup to geomechanical deformation. We introduce a hybrid Artificial Intelligence (AI) workflow that forecasts pressure buildup and its effects on effective stresses, eliminating the need for extensive compositional simulations. Additionally, we compare hybrid algorithms with the traditional ADAM optimizer and Physics-Informed Neural Networks (PINN). Our investigation further examines how pressure-induced effective stresses influence CO₂ injection in reservoirs. To improve predictive accuracy and computational efficiency, we utilize innovative hybrid models that combine Artificial Neural Networks (ANNs) with advanced optimization algorithms, including the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Gorilla Troops Optimization (GTO). Among these, the ANN-GTO model exhibits the highest predictive accuracy in our tests, with a correlation coefficient of 0.99970 and an R² of 0.99941. Microseismic analysis reveals temporal and spatial clustering of induced events, strongly linked to pressure-related stress changes. Event densities increase notably over the simulation period, from 0.1 to 1 event to 30 to 220 events, providing valuable indicators for leakage risk and enabling proactive mitigation. Our results confirm that hybrid ANN models effectively predict leakage risks caused by pressure buildup and microseismic activity, thereby enhancing the safety and efficiency of CO₂ sequestration.

向地层中注入二氧化碳是一种常用的方法，但由于孔隙压力的增加，这种方法存在很大的地质力学风险。这种压力的增加可能导致盖层破裂、断层重新激活、孔隙弹性响应，并损害井的完整性。在这项研究中，我们开发了一个有效平均应力的预测模型，该模型直接将储层压力累积与地质力学变形联系起来。我们引入了一种混合人工智能（AI）工作流程，可以预测压力累积及其对有效应力的影响，从而消除了大量成分模拟的需要。此外，我们将混合算法与传统的ADAM优化器和物理信息神经网络（PINN）进行了比较。我们的研究进一步研究了压力诱导的有效应力如何影响储层中的二氧化碳注入。为了提高预测精度和计算效率，我们利用创新的混合模型，将人工神经网络（ann）与先进的优化算法相结合，包括鲸鱼优化算法（WOA）、粒子群优化算法（PSO）、灰狼优化器（GWO）和大猩猩部队优化算法（GTO）。其中，ANN-GTO模型在我们的检验中表现出最高的预测准确率，相关系数为0.99970，R2为0.99941。微震分析揭示了诱发事件的时空聚类，与压力相关的应力变化密切相关。在模拟期间，事件密度显著增加，从0.1到1个事件增加到30到220个事件，为泄漏风险提供了有价值的指标，并实现了主动缓解。研究结果表明，混合人工神经网络模型能够有效预测压力累积和微地震活动引起的泄漏风险，从而提高CO2封存的安全性和效率。

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引用次数: 0

Forecasting wind speed over multiple horizons: Superiority of deep learning and decomposition-driven hybrid models 多视界风速预测：深度学习和分解驱动混合模型的优越性

IF 4.6

Unconventional Resources

Pub Date : 2025-12-16 DOI: 10.1016/j.uncres.2025.100296

Ajay Kumar , A.J. Singh , Sanjay Kumar

Accurate wind speed forecasting impacts grid stability, energy pricing, and ability to replace fossil fuels with clean power, however, stochastic nature and nonlinearity of wind make prediction a challenge. To address these challenges, this work explores a range of forecasting strategies like statistical, machine learning (ML), and deep learning (DL) methods, for short-term (ST) and mid-term (MT) horizons. The experimental results across forecast horizons of 3, 6, 12, and 24 steps demonstrate that statistical models perform well at very short horizons (RMSE ≈ 0.55–1.14, R² up to 0.40) but degrade rapidly as the forecast length increases, with RMSE above 1.30 and R² near 0.00 at a forecasting horizon of 24. ML model XGBoost offers high accuracy and stability in comparison to RF, having an RMSE value of 0.43 and R² value 0.78 at step-3, remaining outstanding even compared at step-24, which offered RMSE = 1.00 to 1.18 and R² = 0.25 to 0.40, respectively. Transformer consistently performs better in having RMSE = 0.55–0.70 and R² up to 0.57 at step-3, and maintains better accuracy for longer horizons with RMSE = 0.95 and R² up to 0.50 at step-24. To improve the wind speed forecasting (WSF), Variational Mode Decomposition (VMD) with LSTM has been used by decomposing wind speed signals (WSS) into intrinsic mode functions (IMF). The proposed decomposition-driven hybrid model and advanced deep learning techniques help to achieve accurate and better forecasting and can be implemented in various applications of forecasting.

准确的风速预测影响电网稳定性、能源定价以及用清洁能源替代化石燃料的能力，然而，风的随机性和非线性使预测成为一项挑战。为了应对这些挑战，这项工作探索了一系列预测策略，如统计、机器学习（ML）和深度学习（DL）方法，用于短期（ST）和中期（MT）的视野。3、6、12和24步预测水平的实验结果表明，统计模型在极短水平（RMSE≈0.55-1.14，R2可达0.40）表现良好，但随着预测长度的增加而迅速退化，在24步预测水平上RMSE大于1.30，R2接近0.00。与RF相比，ML模型XGBoost具有较高的准确性和稳定性，在步骤3时RMSE值为0.43，R2值为0.78，即使与步骤24相比也保持出色，RMSE分别为1.00至1.18,R2 = 0.25至0.40。Transformer在步骤3的RMSE = 0.55-0.70和R2高达0.57时始终表现更好，并且在步骤24的RMSE = 0.95和R2高达0.50时保持更长的视野更好的精度。为了提高风速预报的准确性，将变分模态分解（VMD）与LSTM相结合，将风速信号分解为内禀模态函数（IMF）。提出的分解驱动混合模型和先进的深度学习技术有助于实现准确和更好的预测，并可在各种预测应用中实现。

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引用次数: 0

Unraveling tight gas migration and accumulation mechanisms via Raman-based single-inclusion carbon isotopes and paleo-pressure reconstruction: A case study of the Jurassic Shaximiao formation, Sichuan basin 基于拉曼单包裹体碳同位素和古压力重建揭示致密气运聚机制——以四川盆地侏罗系沙溪庙组为例

IF 4.6

Unconventional Resources

Pub Date : 2025-12-16 DOI: 10.1016/j.uncres.2025.100295

Chen Zhang , Ruyue Wang , Yahao Huang , Junyi Shi , Zhongrui Wu , Ze Tao , Zhigang Wen , Jizhen Zhang

The innovative application of Raman spectroscopy for single-inclusion carbon isotope analysis represents a key advancement in understanding the migration and enrichment mechanisms of tight gas. In this study, Jurassic Shaximiao Formation reservoirs in the central Sichuan Basin were selected as the research focus. By combining single-inclusion carbon isotope analysis with paleo-pressure reconstruction of fluid inclusions, this work systematically investigates the migration pathways, enrichment patterns, and controlling factors of tight gas accumulation. The results reveal that variations in carbon isotope gradients indicate radial outward diffusion of natural gas from faults. The western Sichuan-Zhongjiang Fault and Bajiaochang Fault serve as the primary vertical migration conduits for natural gas, driven by ancient pressure differentials. Reverse faults formed during the Yanshan period are identified as critical pathways for hydrocarbon accumulation, while normal faults formed during the Himalayan period contribute to secondary migration and redistribution among sandbodies. This study offers a novel and effective approach to reconstructing the accumulation processes and enrichment mechanisms of tight gas reservoirs by integrating Raman-based single-inclusion carbon isotope analysis with paleo-pressure recovery techniques. These findings provide valuable insights into the mechanisms of tight gas enrichment and offer practical guidance for enhancing exploration and development of unconventional gas resources.

拉曼光谱在单包裹体碳同位素分析中的创新应用，是理解致密气运移富集机制的重要进展。本研究以川中侏罗系沙溪庙组储层为研究重点。通过单包裹体碳同位素分析与流体包裹体古压力重建相结合，系统探讨了致密气的运移路径、富集模式及控制因素。结果表明，碳同位素梯度的变化表明天然气从断裂向外径向扩散。川西—中江断裂和八角场断裂受古压差驱动，是天然气的主要垂向运移通道。燕山期形成的逆断裂是油气成藏的关键通道，喜马拉雅期形成的正断裂则是油气在砂体间的二次运移和再分配。将拉曼单包裹体碳同位素分析与古压力恢复技术相结合，为重建致密气藏成藏过程和富集机制提供了一种新颖有效的方法。这些发现为进一步认识致密气富集机理提供了有价值的思路，为加强非常规天然气资源勘探开发提供了实践指导。

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引用次数: 0