Pub Date : 2026-03-01Epub Date: 2026-01-16DOI: 10.1016/j.uncres.2026.100310
Mohammad Chegeni, Mohammad Tolou Askari, Meysam Amirahmadi, Vahid Ghods
Grid-connected photovoltaic systems used in zero-energy building applications are reliable and practically deployable only when maximum power extraction, direct-current link stability, and grid power-injection control are designed and evaluated in a coordinated, cascaded manner. This study presents an integrated three-stage framework. In the first stage, maximum power point tracking is performed using a data-driven support vector regression approach, with automatic hyperparameter tuning via a tree-structured Parzen estimator. The second stage addresses power smoothing and direct-current link stabilization through a hybrid energy storage system composed of a battery and a supercapacitor, together with a two-layer control strategy for intelligent current sharing. At this stage, the impact of tracker model selection on direct-current link voltage stability is analyzed directly, demonstrating that evaluating maximum power point tracking without considering its implications for the direct-current link and the storage subsystem can lead to a misleading assessment of true system performance. In the third stage, power conversion and bidirectional exchange with the grid are ensured by a single-phase inverter equipped with a third-order filter and a modified synchronous reference frame transformation-based control scheme. Simulation results indicate that co-design of the three stages simultaneously improves renewable energy harvesting, reduces direct-current link oscillations and battery transient stresses, and enables grid-compliant power injection and stable power exchange under zero-energy building operating scenarios.
{"title":"Enhanced maximum power point tracking performance for PV systems in zero-energy buildings: An optimized SVR–TPE approach with hybrid energy storage and real-time capability","authors":"Mohammad Chegeni, Mohammad Tolou Askari, Meysam Amirahmadi, Vahid Ghods","doi":"10.1016/j.uncres.2026.100310","DOIUrl":"10.1016/j.uncres.2026.100310","url":null,"abstract":"<div><div>Grid-connected photovoltaic systems used in zero-energy building applications are reliable and practically deployable only when maximum power extraction, direct-current link stability, and grid power-injection control are designed and evaluated in a coordinated, cascaded manner. This study presents an integrated three-stage framework. In the first stage, maximum power point tracking is performed using a data-driven support vector regression approach, with automatic hyperparameter tuning via a tree-structured Parzen estimator. The second stage addresses power smoothing and direct-current link stabilization through a hybrid energy storage system composed of a battery and a supercapacitor, together with a two-layer control strategy for intelligent current sharing. At this stage, the impact of tracker model selection on direct-current link voltage stability is analyzed directly, demonstrating that evaluating maximum power point tracking without considering its implications for the direct-current link and the storage subsystem can lead to a misleading assessment of true system performance. In the third stage, power conversion and bidirectional exchange with the grid are ensured by a single-phase inverter equipped with a third-order filter and a modified synchronous reference frame transformation-based control scheme. Simulation results indicate that co-design of the three stages simultaneously improves renewable energy harvesting, reduces direct-current link oscillations and battery transient stresses, and enables grid-compliant power injection and stable power exchange under zero-energy building operating scenarios.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100310"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-30DOI: 10.1016/j.uncres.2026.100324
Kalpana Bijayeeni Samal , Mitali Mahapatra , Swagat Pati , Manoj Kumar Debnath
{"title":"Corrigendum to “A review on microgrid control: Conventional, advanced and intelligent control approaches” [Volume 9, January 2026, 100297]","authors":"Kalpana Bijayeeni Samal , Mitali Mahapatra , Swagat Pati , Manoj Kumar Debnath","doi":"10.1016/j.uncres.2026.100324","DOIUrl":"10.1016/j.uncres.2026.100324","url":null,"abstract":"","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100324"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.uncres.2025.100301
Zhicheng Zhou, Haoyu Mao, Boxun Yang, Shaoxuan Sun
With the growing strategic importance of shale gas in China's energy portfolio, effective risk assessment during the production phase has become crucial. Conventional risk analysis approaches often struggle to capture the in herent complexity and diversity of shale gas well production systems. To address this limitation, this study proposes a hybrid framework combining integrates the System-Theoretic Process Analysis (STPA), Fault Tree Analysis (FTA), and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to contruct a comprehensive risk assessment framework for shale gas well production. Through STPA and FTA, the study investigates four dimensions—human factors, equipment, materials, and environment—to accurately identify potential risks such as frontline operator errors, equipment failures, material supply and quality issues, and complex geological and climatic conditions. DEMATEL is subsequently employed to quantify the weights of risk factors, highlighting high-weight risks such as gas production equipment failures, gathering and transportation pipeline system failures, geological risks increasing extraction difficulty, and climatic and environmental risks that complicate extraction processes. These risks are interdependent and manifest across multiple production stages, significantly impacting the safety, stability, and efficiency of shale gas production. This research provides a more precise and comprehensive basis for shale gas production risk assessment contributing to the safe and efficient production of shale gas.
{"title":"A novel method for risk identification and quantitative assessment in shale gas development phase based on STPA-FTA-DEMATEL","authors":"Zhicheng Zhou, Haoyu Mao, Boxun Yang, Shaoxuan Sun","doi":"10.1016/j.uncres.2025.100301","DOIUrl":"10.1016/j.uncres.2025.100301","url":null,"abstract":"<div><div>With the growing strategic importance of shale gas in China's energy portfolio, effective risk assessment during the production phase has become crucial. Conventional risk analysis approaches often struggle to capture the in herent complexity and diversity of shale gas well production systems. To address this limitation, this study proposes a hybrid framework combining integrates the System-Theoretic Process Analysis (STPA), Fault Tree Analysis (FTA), and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to contruct a comprehensive risk assessment framework for shale gas well production. Through STPA and FTA, the study investigates four dimensions—human factors, equipment, materials, and environment—to accurately identify potential risks such as frontline operator errors, equipment failures, material supply and quality issues, and complex geological and climatic conditions. DEMATEL is subsequently employed to quantify the weights of risk factors, highlighting high-weight risks such as gas production equipment failures, gathering and transportation pipeline system failures, geological risks increasing extraction difficulty, and climatic and environmental risks that complicate extraction processes. These risks are interdependent and manifest across multiple production stages, significantly impacting the safety, stability, and efficiency of shale gas production. This research provides a more precise and comprehensive basis for shale gas production risk assessment contributing to the safe and efficient production of shale gas.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100301"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.uncres.2025.100300
Xiaoxuan Zhu , Hong Ji , Yuxin Huang , Yingjie Han
As a crucial unconventional energy resource, shale gas has attracted significant attention. The successful application of artificial intelligence (AI) technology plays a vital role in advancing shale gas exploration and development, driving technological progress, enhancing economic efficiency, and promoting environmental sustainability. Using the Web of Science Core Collection, this study systematically analyzed relevant literature published between 2011 and 2024 through CiteSpace-based bibliometric analysis a knowledge graph of AI applications in shale gas play was conducted, covering annual publication trends, the distribution of high-output countries, keyword co-occurrence networks, and citation frequency statistics of highly cited papers. The results indicate that AI has been widely applied across key stages of shale gas exploration and production, including reservoir evaluation, development optimization and production forecasting, with primy focus on total organic carbon (TOC) prediction, estimated ultimate recovery (EUR) evaluation, and production performance modeling. Despite significant progress in AI application for shale gas, challenges persist, such as inconsistent geological data quality, limited model generalization, and high computational demands. Future research should prioritize optimizing data pre-processing methods, developing cross-regional knowledge transfer frameworks, and enhancing algorithmic efficiency and interpretability to further improve shale gas exploration and production strategies.
页岩气作为一种重要的非常规能源，受到了广泛关注。人工智能（AI）技术的成功应用，对于推进页岩气勘探开发、推动技术进步、提高经济效益、促进环境可持续性发挥着至关重要的作用。本研究利用Web of Science核心文集，通过基于citespace的文献计量分析，系统分析了2011 - 2024年间发表的相关文献，绘制了人工智能在页岩气领域应用的知识图谱，包括年度发表趋势、高产国分布、关键词共现网络、高被引论文被引频次统计等。结果表明，人工智能已广泛应用于页岩气勘探和生产的关键阶段，包括储层评价、开发优化和生产预测，主要集中在总有机碳（TOC）预测、估计最终采收率（EUR）评估和生产动态建模上。尽管人工智能在页岩气领域的应用取得了重大进展，但仍然存在一些挑战，例如地质数据质量不一致、模型泛化有限以及计算需求高。未来的研究应优先优化数据预处理方法，开发跨区域知识转移框架，提高算法效率和可解释性，以进一步改善页岩气勘探和生产策略。
{"title":"Application and research progress of artificial intelligence in shale gas exploration and development","authors":"Xiaoxuan Zhu , Hong Ji , Yuxin Huang , Yingjie Han","doi":"10.1016/j.uncres.2025.100300","DOIUrl":"10.1016/j.uncres.2025.100300","url":null,"abstract":"<div><div>As a crucial unconventional energy resource, shale gas has attracted significant attention. The successful application of artificial intelligence (AI) technology plays a vital role in advancing shale gas exploration and development, driving technological progress, enhancing economic efficiency, and promoting environmental sustainability. Using the Web of Science Core Collection, this study systematically analyzed relevant literature published between 2011 and 2024 through CiteSpace-based bibliometric analysis a knowledge graph of AI applications in shale gas play was conducted, covering annual publication trends, the distribution of high-output countries, keyword co-occurrence networks, and citation frequency statistics of highly cited papers. The results indicate that AI has been widely applied across key stages of shale gas exploration and production, including reservoir evaluation, development optimization and production forecasting, with primy focus on total organic carbon (TOC) prediction, estimated ultimate recovery (EUR) evaluation, and production performance modeling. Despite significant progress in AI application for shale gas, challenges persist, such as inconsistent geological data quality, limited model generalization, and high computational demands. Future research should prioritize optimizing data pre-processing methods, developing cross-regional knowledge transfer frameworks, and enhancing algorithmic efficiency and interpretability to further improve shale gas exploration and production strategies.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100300"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.uncres.2026.100321
Mohamed Said Adouairi , Saad Motahhir , Badre Bossoufi
This paper presents a novel and resilient control strategy for isolated AC microgrids based on a Multiple Second-Order Generalized Integrator with Frequency-Locked Loop architecture and a complex virtual impedance design. The proposed method addresses key challenges associated with power sharing accuracy, harmonic distortion, and system robustness in the presence of nonlinear loads, frequency variation, and complex inverter-to-load impedance characteristics. The main innovation lies in the integration of multi-harmonic Multiple Second-Order Generalized Integrator with Frequency-Locked Loop signal decomposition with adaptive complex virtual impedance and coordinated droop-based control, providing improved harmonic suppression, precise power sharing, and enhanced transient stability compared to existing approaches. A novel signal conditioning scheme based on Multiple Second-Order Generalized Integrator with Frequency-Locked Loop is employed to extract fundamental and selected harmonic components of inverter currents while rejecting DC offsets. The extracted signals are used to synthesize an adaptive virtual complex impedance that enhances droop-based power sharing under coupled resistive-inductive line conditions. To accurately assess system dynamics, a linear time-periodic model is developed for the Multiple Second-Order Generalized Integrator with Frequency-Locked Loop, enabling the derivation of harmonic transfer functions and stability margins. The control strategy is further augmented by a coordinated Battery Management System, ensuring energy balance and flexibility in transient scenarios. Simulation results involving three parallel single-phase inverters confirm the proposed method's ability to achieve accurate active and reactive power sharing, minimize circulating currents, and maintain robust performance under distorted and unbalanced operating conditions. The effectiveness of the proposed control is validated through detailed comparisons with conventional droop methods.
{"title":"Resilient control of AC microgrids via MSOGI-FLL and virtual complex impedance","authors":"Mohamed Said Adouairi , Saad Motahhir , Badre Bossoufi","doi":"10.1016/j.uncres.2026.100321","DOIUrl":"10.1016/j.uncres.2026.100321","url":null,"abstract":"<div><div>This paper presents a novel and resilient control strategy for isolated AC microgrids based on a Multiple Second-Order Generalized Integrator with Frequency-Locked Loop architecture and a complex virtual impedance design. The proposed method addresses key challenges associated with power sharing accuracy, harmonic distortion, and system robustness in the presence of nonlinear loads, frequency variation, and complex inverter-to-load impedance characteristics. The main innovation lies in the integration of multi-harmonic Multiple Second-Order Generalized Integrator with Frequency-Locked Loop signal decomposition with adaptive complex virtual impedance and coordinated droop-based control, providing improved harmonic suppression, precise power sharing, and enhanced transient stability compared to existing approaches. A novel signal conditioning scheme based on Multiple Second-Order Generalized Integrator with Frequency-Locked Loop is employed to extract fundamental and selected harmonic components of inverter currents while rejecting DC offsets. The extracted signals are used to synthesize an adaptive virtual complex impedance that enhances droop-based power sharing under coupled resistive-inductive line conditions. To accurately assess system dynamics, a linear time-periodic model is developed for the Multiple Second-Order Generalized Integrator with Frequency-Locked Loop, enabling the derivation of harmonic transfer functions and stability margins. The control strategy is further augmented by a coordinated Battery Management System, ensuring energy balance and flexibility in transient scenarios. Simulation results involving three parallel single-phase inverters confirm the proposed method's ability to achieve accurate active and reactive power sharing, minimize circulating currents, and maintain robust performance under distorted and unbalanced operating conditions. The effectiveness of the proposed control is validated through detailed comparisons with conventional droop methods.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100321"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-16DOI: 10.1016/j.uncres.2026.100323
Peilin Zhang , Kouqi Liu , Zhenlin Wang , Kai Feng , Zhichao Wang , Yamin Wang , Mehdi Ostadhassan
Clustering analysis of nanoindentation data is generally done to distinguish the mechanical phases and quantitatively obtain their related mechanical properties and fractions in the entire sample. Clustering analysis is carried out based on the mechanical parameters (Young's modulus and hardness) that are obtained from the load–displacement curves of each indent. However, the accuracy of this method and its suitability for the analysis of grid nanoindentation data on a heterogeneous composite material like shale is still unknown. Therefore, this study applied clustering analysis directly on the load–displacement curves in a shale sample for the first time and compared the results with the widely used mechanical parameter-based clustering analysis method. The results showed that the mechanical parameter-based clustering distinguished four mechanical phases with 8.62, 16.07, 24.31, and 37.16 GPa as the average Young's moduli, and hardness values of 0.15, 0.35, 0.80, and 2.56 GPa, respectively. The fractions of these phases are 34.22 %, 27.78 %, 20.89 %, and 7.11 %, respectively. Likewise, the curve clustering method also distinguished four mechanical phases with Young's moduli of 4.75, 7.91, 14.10, and 22.49 GPa, and hardness values of 0.03, 0.08, 0.23, and 0.97 GPa, with fractions of 2.67 %, 12.89 %, 40.44 %, and 44.00 %, respectively. Comparing the results with the sample's mineralogy suggests that parameter-based clustering provides a better distinction between mechanical phases, closely aligning with the mineral fractions obtained from X-ray diffraction (XRD) analysis. Therefore, this approach is recommended for analyzing grid nanoindentation data in composite materials.
{"title":"Clustering analysis of nanoindentation data for shale: Curve-based versus mechanical parameter-based approaches","authors":"Peilin Zhang , Kouqi Liu , Zhenlin Wang , Kai Feng , Zhichao Wang , Yamin Wang , Mehdi Ostadhassan","doi":"10.1016/j.uncres.2026.100323","DOIUrl":"10.1016/j.uncres.2026.100323","url":null,"abstract":"<div><div>Clustering analysis of nanoindentation data is generally done to distinguish the mechanical phases and quantitatively obtain their related mechanical properties and fractions in the entire sample. Clustering analysis is carried out based on the mechanical parameters (Young's modulus and hardness) that are obtained from the load–displacement curves of each indent. However, the accuracy of this method and its suitability for the analysis of grid nanoindentation data on a heterogeneous composite material like shale is still unknown. Therefore, this study applied clustering analysis directly on the load–displacement curves in a shale sample for the first time and compared the results with the widely used mechanical parameter-based clustering analysis method. The results showed that the mechanical parameter-based clustering distinguished four mechanical phases with 8.62, 16.07, 24.31, and 37.16 GPa as the average Young's moduli, and hardness values of 0.15, 0.35, 0.80, and 2.56 GPa, respectively. The fractions of these phases are 34.22 %, 27.78 %, 20.89 %, and 7.11 %, respectively. Likewise, the curve clustering method also distinguished four mechanical phases with Young's moduli of 4.75, 7.91, 14.10, and 22.49 GPa, and hardness values of 0.03, 0.08, 0.23, and 0.97 GPa, with fractions of 2.67 %, 12.89 %, 40.44 %, and 44.00 %, respectively. Comparing the results with the sample's mineralogy suggests that parameter-based clustering provides a better distinction between mechanical phases, closely aligning with the mineral fractions obtained from X-ray diffraction (XRD) analysis. Therefore, this approach is recommended for analyzing grid nanoindentation data in composite materials.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100323"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-05DOI: 10.1016/j.uncres.2026.100331
Deivid Campos , Bruno da Silva Macêdo , Oscar Ikechukwu Ogali , Matteo Bodini , Dmitriy A. Martyushev , Farouk Abduh Kamil Al-Fahaidy , Camila Martins Saporetti , Leonardo Goliatt
Accurately predicting Flowing Bottom-Hole Pressure (FBHP) is critical for optimizing oil and gas production. Existing predictive methods often rely on oversimplified or complex, yet computationally expensive, models that fail to capture the intrinsic nonlinearities of well dynamics, leading to inaccurate predictions and potential economic losses. This paper introduces a three-layer heterogeneous stacking ensemble model to address the latter challenge. In particular, the key novelty of the developed work is a hierarchical architecture that integrates five distinct Machine Learning (ML) base learners, two meta-learners, and a final super-learner, i.e., an additional meta-model that combines the outputs of the meta-learners to capture complex, non-linear relationships in the data. When evaluated on a field dataset (total dataset samples ; test set samples ), the proposed Super Learner Stacking model (ST-S) demonstrated superior predictive performance on the independent test set, achieving R-squared () = and Root Mean Squared Error (RMSE) = . In addition, the ST-S model outperformed all individual models and simpler stacking ensembles reported in the article. As a result, the developed ST-S model provides a robust, data-driven tool for FBHP prediction, achieving high predictive accuracy without resorting to computationally expensive methods, thereby supporting improved well management and production optimization.
{"title":"Heterogeneous stacking strategy for modeling flowing bottom-hole pressure of oil wells","authors":"Deivid Campos , Bruno da Silva Macêdo , Oscar Ikechukwu Ogali , Matteo Bodini , Dmitriy A. Martyushev , Farouk Abduh Kamil Al-Fahaidy , Camila Martins Saporetti , Leonardo Goliatt","doi":"10.1016/j.uncres.2026.100331","DOIUrl":"10.1016/j.uncres.2026.100331","url":null,"abstract":"<div><div>Accurately predicting Flowing Bottom-Hole Pressure (FBHP) is critical for optimizing oil and gas production. Existing predictive methods often rely on oversimplified or complex, yet computationally expensive, models that fail to capture the intrinsic nonlinearities of well dynamics, leading to inaccurate predictions and potential economic losses. This paper introduces a three-layer heterogeneous stacking ensemble model to address the latter challenge. In particular, the key novelty of the developed work is a hierarchical architecture that integrates five distinct Machine Learning (ML) base learners, two meta-learners, and a final super-learner, <em>i.e.</em>, an additional meta-model that combines the outputs of the meta-learners to capture complex, non-linear relationships in the data. When evaluated on a field dataset (total dataset samples <span><math><mrow><mi>N</mi><mo>=</mo><mn>795</mn></mrow></math></span>; test set samples <span><math><mrow><mi>N</mi><mo>=</mo><mn>199</mn></mrow></math></span>), the proposed Super Learner Stacking model (ST-S) demonstrated superior predictive performance on the independent test set, achieving R-squared (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) = <span><math><mrow><mn>0</mn><mo>.</mo><mn>857</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>006</mn></mrow></math></span> and Root Mean Squared Error (RMSE) = <span><math><mrow><mn>146</mn><mo>.</mo><mn>382</mn><mo>±</mo><mn>2</mn><mo>.</mo><mn>806</mn></mrow></math></span>. In addition, the ST-S model outperformed all individual models and simpler stacking ensembles reported in the article. As a result, the developed ST-S model provides a robust, data-driven tool for FBHP prediction, achieving high predictive accuracy without resorting to computationally expensive methods, thereby supporting improved well management and production optimization.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100331"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-06DOI: 10.1016/j.uncres.2026.100333
Ali Mahmoud, Rahul Gajbhiye
Lost circulation is one of the most persistent and costly challenges in drilling operations, particularly under high-pressure and high-temperature conditions and in fractured carbonate reservoirs. Despite decades of research, no universal solution exists, and severe fluid losses continue to jeopardize well construction, increase non-productive time, and compromise safety. This review delivers a comprehensive synthesis of mechanisms, materials, experimental evaluations, and field practices, spanning petroleum, geothermal, and emerging energy-transition wells. Mechanistic pathways of loss initiation are critically examined across porous, fractured, and cavernous formations, as well as severe lost circulation scenarios, highlighting the limitations of existing predictive models. Lost circulation materials, ranging from conventional particulates and fibers to advanced nano-enabled and biodegradable systems, are assessed in terms of bridging efficiency, survivability under high-pressure and high-temperature conditions, and sustainability. Experimental and modeling approaches, including fracture-slot tests, dynamic high-pressure and high-temperature flow loops, and computational tools such as computational fluid dynamics, discrete element modeling, and artificial intelligence and machine learning, are evaluated to expose the gap between laboratory results and field reliability. Field strategies, including wellbore strengthening, cement squeezes, and managed pressure drilling, are reviewed to underline their largely reactive nature. Finally, a forward-looking roadmap is presented, identifying research needs such as standardized high-pressure and high-temperature validation protocols, chemically compatible and durable materials for carbon dioxide and hydrogen wells, and the integration of digital twins with artificial intelligence-driven predictive diagnostics.
{"title":"Lost circulation in drilling: Mechanisms, materials, and future directions for HPHT and energy-transition wells","authors":"Ali Mahmoud, Rahul Gajbhiye","doi":"10.1016/j.uncres.2026.100333","DOIUrl":"10.1016/j.uncres.2026.100333","url":null,"abstract":"<div><div>Lost circulation is one of the most persistent and costly challenges in drilling operations, particularly under high-pressure and high-temperature conditions and in fractured carbonate reservoirs. Despite decades of research, no universal solution exists, and severe fluid losses continue to jeopardize well construction, increase non-productive time, and compromise safety. This review delivers a comprehensive synthesis of mechanisms, materials, experimental evaluations, and field practices, spanning petroleum, geothermal, and emerging energy-transition wells. Mechanistic pathways of loss initiation are critically examined across porous, fractured, and cavernous formations, as well as severe lost circulation scenarios, highlighting the limitations of existing predictive models. Lost circulation materials, ranging from conventional particulates and fibers to advanced nano-enabled and biodegradable systems, are assessed in terms of bridging efficiency, survivability under high-pressure and high-temperature conditions, and sustainability. Experimental and modeling approaches, including fracture-slot tests, dynamic high-pressure and high-temperature flow loops, and computational tools such as computational fluid dynamics, discrete element modeling, and artificial intelligence and machine learning, are evaluated to expose the gap between laboratory results and field reliability. Field strategies, including wellbore strengthening, cement squeezes, and managed pressure drilling, are reviewed to underline their largely reactive nature. Finally, a forward-looking roadmap is presented, identifying research needs such as standardized high-pressure and high-temperature validation protocols, chemically compatible and durable materials for carbon dioxide and hydrogen wells, and the integration of digital twins with artificial intelligence-driven predictive diagnostics.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100333"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Interfacial tension (IFT) between displacing fluids and reservoir hydrocarbons is vital in enhanced oil recovery (EOR) as it affects fluid displacement efficiency and the mobilization of trapped oil. Lower IFT increases the capillary number and enhances fluid mobility, improving oil displacement in porous media. In this study, advanced machine learning (ML) techniques, including adaptive boosting decision tree (AdaBoost-DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and random forest (RF) were utilized to model the IFT of n-alkanes and aqueous systems containing surfactants and nanoparticles (NPs), using a collection of 708 experimental data points. The results demonstrated that the LightGBM model outperformed the others, achieving average absolute relative errors (AARE) of 2.02%, 3.27%, and 2.27% for the training, testing, and total datasets, respectively, along with the highest overall determination coefficient (R2) value of 0.9967. Moreover, sensitivity and trend analyses highlighted that the phase inversion temperature (PIT) of surfactants and the NPs concentration significantly affect IFT, showing the strongest negative effects. The input variables were ranked by impact, with PIT, NPs concentration, surfactant concentration, hydrophilic-lipophilic balance (HLB), molecular weight (Mw) of n-alkanes, average NPs diameter, and temperature. The Mw of n-alkanes and the average NPs diameter positively influenced IFT, while the other factors negatively affected it. Finally, the leverage technique applied to the LightGBM model indicated that over 95% of the data fell within the acceptable validation zone, verifying the model's statistical robustness and the reliability of the experimental data collected. The models developed in this study are data-driven and demonstrate reliable performance within the reported data ranges. To ensure their broader applicability, these models should be validated using entirely unseen datasets. Future research efforts could focus on expanding the dataset, exploring alternative input variables, and examining the effects of various surfactants and NPs on the IFT behavior of hydrocarbons and aqueous mixtures.
{"title":"Modeling interfacial tension between n-alkanes and aqueous systems containing surfactants and nanoparticles","authors":"Behnam Amiri-Ramsheh , Seyyed-Mohammad-Mehdi Hosseini , Amir-Ehsan Avazzadeh , Mohammad-Reza Mohammadi , Saeid Atashrouz , Dragutin Nedeljkovic , Mehdi Ostadhassan , Abdolhossein Hemmati-Sarapardeh , Ahmad Mohaddespour","doi":"10.1016/j.uncres.2026.100332","DOIUrl":"10.1016/j.uncres.2026.100332","url":null,"abstract":"<div><div>Interfacial tension (IFT) between displacing fluids and reservoir hydrocarbons is vital in enhanced oil recovery (EOR) as it affects fluid displacement efficiency and the mobilization of trapped oil. Lower IFT increases the capillary number and enhances fluid mobility, improving oil displacement in porous media. In this study, advanced machine learning (ML) techniques, including adaptive boosting decision tree (AdaBoost-DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and random forest (RF) were utilized to model the IFT of n-alkanes and aqueous systems containing surfactants and nanoparticles (NPs), using a collection of 708 experimental data points. The results demonstrated that the LightGBM model outperformed the others, achieving average absolute relative errors (AARE) of 2.02%, 3.27%, and 2.27% for the training, testing, and total datasets, respectively, along with the highest overall determination coefficient (R<sup>2</sup>) value of 0.9967. Moreover, sensitivity and trend analyses highlighted that the phase inversion temperature (PIT) of surfactants and the NPs concentration significantly affect IFT, showing the strongest negative effects. The input variables were ranked by impact, with PIT, NPs concentration, surfactant concentration, hydrophilic-lipophilic balance (HLB), molecular weight (Mw) of n-alkanes, average NPs diameter, and temperature. The Mw of n-alkanes and the average NPs diameter positively influenced IFT, while the other factors negatively affected it. Finally, the leverage technique applied to the LightGBM model indicated that over 95% of the data fell within the acceptable validation zone, verifying the model's statistical robustness and the reliability of the experimental data collected. The models developed in this study are data-driven and demonstrate reliable performance within the reported data ranges. To ensure their broader applicability, these models should be validated using entirely unseen datasets. Future research efforts could focus on expanding the dataset, exploring alternative input variables, and examining the effects of various surfactants and NPs on the IFT behavior of hydrocarbons and aqueous mixtures.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100332"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-30DOI: 10.1016/j.uncres.2026.100328
Yingyan Li , Chenlin Hu , Jie Zeng , Wenfeng Wang , Shiqian Xu , Yingfang Zhou , Fanhua Zeng , Jingpeng Wang
Graded proppant injection into complex fractures is frequently used to prop connected secondary fractures in tight unconventional reservoirs. A comprehensive conductivity model incorporating creep, decreasing proppant size distribution, proppant embedment and deformation, and unpropped fracture surface deformation is established to ascertain partially propped fracture network conductivity. The propped fracture width variation is described by creep deformation, proppant embedment, and proppant particle deformation. The corresponding fracture permeability is depicted by the Carman-Kozeny equation where the dynamic proppant pack porosity is calculated via proppant size and the number of proppant layers. For unpropped areas, the width is controlled by effective stress, and the permeability is a function of fracture aperture. The hydraulic–electric analogies concept is use to integrate the local conductivity of different areas and characterize the overall fracture network conductivity. The model is verified against long-term conductivity measurement data. Results show that the fracture width variation is mainly caused by rock creep and proppant embedment. Larger Kelvin shear modulus and Maxwell viscosity slow down the conductivity decline rate. The conductivity becomes stable after 4 days when the Kelvin shear modulus is increased to 5.4 × 108 Pa. The Maxwell shear modulus has the slightest influence on conductivity. Larger-size proppants offer higher overall conductivity and better maintain the conductivity. The fracture network conductivity is significantly larger than the conductivity of the main fracture fully supported by the graded proppants and that of the fracture branches. The three-dimensional (3D) conductivity diagram and two-dimensional (2D) conductivity maps are generated to better demonstrate time-dependent conductivity evolution.
{"title":"A comprehensive fracture network conductivity model for tight unconventional reservoirs considering various proppant size, creep deformation, and proppant compaction and embedment","authors":"Yingyan Li , Chenlin Hu , Jie Zeng , Wenfeng Wang , Shiqian Xu , Yingfang Zhou , Fanhua Zeng , Jingpeng Wang","doi":"10.1016/j.uncres.2026.100328","DOIUrl":"10.1016/j.uncres.2026.100328","url":null,"abstract":"<div><div>Graded proppant injection into complex fractures is frequently used to prop connected secondary fractures in tight unconventional reservoirs. A comprehensive conductivity model incorporating creep, decreasing proppant size distribution, proppant embedment and deformation, and unpropped fracture surface deformation is established to ascertain partially propped fracture network conductivity. The propped fracture width variation is described by creep deformation, proppant embedment, and proppant particle deformation. The corresponding fracture permeability is depicted by the Carman-Kozeny equation where the dynamic proppant pack porosity is calculated via proppant size and the number of proppant layers. For unpropped areas, the width is controlled by effective stress, and the permeability is a function of fracture aperture. The hydraulic–electric analogies concept is use to integrate the local conductivity of different areas and characterize the overall fracture network conductivity. The model is verified against long-term conductivity measurement data. Results show that the fracture width variation is mainly caused by rock creep and proppant embedment. Larger Kelvin shear modulus and Maxwell viscosity slow down the conductivity decline rate. The conductivity becomes stable after 4 days when the Kelvin shear modulus is increased to 5.4 × 10<sup>8</sup> Pa. The Maxwell shear modulus has the slightest influence on conductivity. Larger-size proppants offer higher overall conductivity and better maintain the conductivity. The fracture network conductivity is significantly larger than the conductivity of the main fracture fully supported by the graded proppants and that of the fracture branches. The three-dimensional (3D) conductivity diagram and two-dimensional (2D) conductivity maps are generated to better demonstrate time-dependent conductivity evolution.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"10 ","pages":"Article 100328"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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