Digitalization is one of the key drivers of the global economy. This is especially true for the GCC (Gulf Cooperation Council) countries, which include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. These countries are seeking to diversify their economies and reduce their dependence on hydrocarbon exports. The introduction of digital technologies is seen as a way to diversify their economies and to adapt to the global sustainable development agenda. This study aims to assess the role of digital transformation in ensuring sustainable development of the GCC countries. Econometric modeling is used for the analysis. The main data of the model include digitalization indicators, economic, social and environmental parameters. The regression model was tested on an example from each country in the region. The results show that digitalization has a significant impact on the sustainable development of GCC countries. The article also identifies the positive and negative aspects of digitalization.
{"title":"Digitalization as a driver for sustainable development in the GCC economies","authors":"Ravil Ramilevich Asmyatullin, Sofya Grigoryevna Glavina","doi":"10.1016/j.uncres.2025.100231","DOIUrl":"10.1016/j.uncres.2025.100231","url":null,"abstract":"<div><div>Digitalization is one of the key drivers of the global economy. This is especially true for the GCC (Gulf Cooperation Council) countries, which include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. These countries are seeking to diversify their economies and reduce their dependence on hydrocarbon exports. The introduction of digital technologies is seen as a way to diversify their economies and to adapt to the global sustainable development agenda. This study aims to assess the role of digital transformation in ensuring sustainable development of the GCC countries. Econometric modeling is used for the analysis. The main data of the model include digitalization indicators, economic, social and environmental parameters. The regression model was tested on an example from each country in the region. The results show that digitalization has a significant impact on the sustainable development of GCC countries. The article also identifies the positive and negative aspects of digitalization.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100231"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863604","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 : 2025-10-01Epub Date: 2025-09-18DOI: 10.1016/j.uncres.2025.100246
Hongjian Chen , Feifei Fang , Pujun Long , Putian Yang , Wei Guo
As conventional hydrocarbon resources continue to deplete, shale gas has become a key driver of the global energy transition due to its production potential and economic viability. However, significant reservoir heterogeneity and the complex evolution of fracture networks introduce uncertainties in single-well production forecasts, making hydraulic fracturing designs heavily dependent on empirical judgment. To address these challenges, this study proposes a data-driven, multi-objective integrated evaluation framework that links feature selection, EUR prediction, and optimal fracturing scheme generation. The framework employs a binary-encoded genetic algorithm (GA) for feature selection, balancing linear and nonlinear dependencies among geological and engineering variables. A heterogeneous ensemble of models, including CatBoost, NGBoost, and TabPFN, is fused using a two-level stacking strategy, significantly improving EUR prediction accuracy. The framework optimizes decision variables, such as fluid and proppant volumes, and uses NSGA-II to solve the bi-objective problem of maximizing EUR while minimizing fluid-proppant consumption, yielding Pareto-optimal designs. Validation on 231 shale gas wells in the Sichuan Basin demonstrates a 10–30 % improvement in , a reduction in , a 40–270 % increase in EUR, and a 10–20 % reduction in fracturing costs for medium-to-low-yield wells. SHAP analysis identifies key factors such as FSL, TIRLD, and HRT as strongly, nonlinearly, and positively correlated with EUR, offering valuable insights for precise production enhancement. The framework shows robustness and transferability, providing essential decision support for shale gas development across diverse geological settings.
{"title":"Coupling EUR prediction with fracturing optimization: An integrated machine learning framework for shale gas development","authors":"Hongjian Chen , Feifei Fang , Pujun Long , Putian Yang , Wei Guo","doi":"10.1016/j.uncres.2025.100246","DOIUrl":"10.1016/j.uncres.2025.100246","url":null,"abstract":"<div><div>As conventional hydrocarbon resources continue to deplete, shale gas has become a key driver of the global energy transition due to its production potential and economic viability. However, significant reservoir heterogeneity and the complex evolution of fracture networks introduce uncertainties in single-well production forecasts, making hydraulic fracturing designs heavily dependent on empirical judgment. To address these challenges, this study proposes a data-driven, multi-objective integrated evaluation framework that links feature selection, EUR prediction, and optimal fracturing scheme generation. The framework employs a binary-encoded genetic algorithm (GA) for feature selection, balancing linear and nonlinear dependencies among geological and engineering variables. A heterogeneous ensemble of models, including CatBoost, NGBoost, and TabPFN, is fused using a two-level stacking strategy, significantly improving EUR prediction accuracy. The framework optimizes decision variables, such as fluid and proppant volumes, and uses NSGA-II to solve the bi-objective problem of maximizing EUR while minimizing fluid-proppant consumption, yielding Pareto-optimal designs. Validation on 231 shale gas wells in the Sichuan Basin demonstrates a 10–30 % improvement in <span><math><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></math></span>, a reduction in <span><math><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi><mo>/</mo><mi>M</mi><mi>S</mi><mi>E</mi></mrow></math></span>, a 40–270 % increase in EUR, and a 10–20 % reduction in fracturing costs for medium-to-low-yield wells. SHAP analysis identifies key factors such as FSL, TIRLD, and HRT as strongly, nonlinearly, and positively correlated with EUR, offering valuable insights for precise production enhancement. The framework shows robustness and transferability, providing essential decision support for shale gas development across diverse geological settings.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100246"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219888","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 : 2025-10-01Epub Date: 2025-09-08DOI: 10.1016/j.uncres.2025.100238
Huijun Wang , Zhiguo Shu , Taohua He , Jiyong Liu , Juan Teng , Gaofeng Zou , Liu He , Shuangfang Lu , Jiayi He , Yuanzhen Zhou , Yuchen Yao
<div><div>Shale gas development optimization faces significant challenges due to computational constraints when handling complex parameter interactions across different scales. Conventional optimization methods are limited by their inability to efficiently process high-dimensional parameter spaces, excessive computational demands that prevent field-scale application, and failure to simultaneously consider both technical performance and economic outcomes. The fundamental objective of this research is to maximize field-scale Net Present Value (NPV) through systematic optimization of engineering parameters under given geological constraints, transforming the complex field development decision-making process into a quantitative mathematical problem of maximizing the NPV objective function while determining the optimal corresponding engineering parameters. This paper introduces a novel proxy-assisted multi-layer cooperative optimization (PAMLCO) framework that systematically addresses these limitations through hierarchical problem decomposition and multi-scale parameter integration. The PAMLCO framework transforms the complex field optimization problem into three hierarchically connected subproblems: (1) an outer layer focuses on field-scale optimization, determining global parameters including fracture half-length (FHL), fracture conductivity (FC), cluster spacing (CS) and target A coordinate; (2) a middle layer optimizes well column parameters such as horizontal section length (HSL), well number and target B coordinate; and (3) an inner layer optimizes single well parameters such as the length from wellhead to target A and the drilling platforms connected to each well. Unlike conventional divide-and-conquer methods that often lead to locally optimal solutions, PAMLCO implements a bidirectional information exchange mechanism between adjacent optimization layers—higher-level optimization results provide constraint boundaries for lower-level optimization, while lower-level optimal solutions guide the evolution direction of higher-level parameters. The key innovation of the PAMLCO framework lies in its ability to efficiently handle the coupling effects between microscopic fracture parameters and macroscopic field development strategies while considering reservoir heterogeneity and surface constraints. At its core, a high-precision Gaussian Process Regression (GPR) proxy model (R<sup>2</sup> = 0.9999, RMSE = 0.0132) coupled with a genetic algorithm (GA) accelerates the optimization process over 2400 times compared to traditional numerical simulation methods while maintaining solution accuracy within 2 % of exhaustive approaches. This computational efficiency breakthrough makes comprehensive field-scale optimization practically feasible, enabling the integration of complex technical and economic factors in real-world decision-making processes. Applied to the Sichuan Basin, the PAMLCO framework achieved accumulated gas production of 68.58 × 10<sup>8</sup>
{"title":"A proxy-assisted multi-layer cooperative optimization framework for economic shale gas field development","authors":"Huijun Wang , Zhiguo Shu , Taohua He , Jiyong Liu , Juan Teng , Gaofeng Zou , Liu He , Shuangfang Lu , Jiayi He , Yuanzhen Zhou , Yuchen Yao","doi":"10.1016/j.uncres.2025.100238","DOIUrl":"10.1016/j.uncres.2025.100238","url":null,"abstract":"<div><div>Shale gas development optimization faces significant challenges due to computational constraints when handling complex parameter interactions across different scales. Conventional optimization methods are limited by their inability to efficiently process high-dimensional parameter spaces, excessive computational demands that prevent field-scale application, and failure to simultaneously consider both technical performance and economic outcomes. The fundamental objective of this research is to maximize field-scale Net Present Value (NPV) through systematic optimization of engineering parameters under given geological constraints, transforming the complex field development decision-making process into a quantitative mathematical problem of maximizing the NPV objective function while determining the optimal corresponding engineering parameters. This paper introduces a novel proxy-assisted multi-layer cooperative optimization (PAMLCO) framework that systematically addresses these limitations through hierarchical problem decomposition and multi-scale parameter integration. The PAMLCO framework transforms the complex field optimization problem into three hierarchically connected subproblems: (1) an outer layer focuses on field-scale optimization, determining global parameters including fracture half-length (FHL), fracture conductivity (FC), cluster spacing (CS) and target A coordinate; (2) a middle layer optimizes well column parameters such as horizontal section length (HSL), well number and target B coordinate; and (3) an inner layer optimizes single well parameters such as the length from wellhead to target A and the drilling platforms connected to each well. Unlike conventional divide-and-conquer methods that often lead to locally optimal solutions, PAMLCO implements a bidirectional information exchange mechanism between adjacent optimization layers—higher-level optimization results provide constraint boundaries for lower-level optimization, while lower-level optimal solutions guide the evolution direction of higher-level parameters. The key innovation of the PAMLCO framework lies in its ability to efficiently handle the coupling effects between microscopic fracture parameters and macroscopic field development strategies while considering reservoir heterogeneity and surface constraints. At its core, a high-precision Gaussian Process Regression (GPR) proxy model (R<sup>2</sup> = 0.9999, RMSE = 0.0132) coupled with a genetic algorithm (GA) accelerates the optimization process over 2400 times compared to traditional numerical simulation methods while maintaining solution accuracy within 2 % of exhaustive approaches. This computational efficiency breakthrough makes comprehensive field-scale optimization practically feasible, enabling the integration of complex technical and economic factors in real-world decision-making processes. Applied to the Sichuan Basin, the PAMLCO framework achieved accumulated gas production of 68.58 × 10<sup>8</sup> ","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100238"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099445","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 : 2025-10-01Epub Date: 2025-09-03DOI: 10.1016/j.uncres.2025.100236
Mohamed A. Atiea , Ali M. El-Rifaie , Ghareeb Moustafa , Abdullah M. Shaheen
Forecasting photovoltaic (PV) power output is essential for reliable grid integration, operational planning, and supporting the global transition toward renewable energy. This paper proposes an integrated machine learning framework that improves prediction accuracy through systematically designed preprocessing, model selection, and advanced hyperparameter optimization. Using a high-resolution dataset from the Sharda University PV system, 13 regression models, including ensemble methods and neural networks, are tested and compared with the aim of maximizing generalizability and predictive performance. Performance gains are achieved through structured hyperparameter optimization using Randomized Search Cross-Validation (RSCV) and Grid Search Cross-Validation (GSCV), where the Random Forest Regressor achieved an R2 of 0.9561 before tuning and 0.9893 after tuning, representing the highest improvement. Gradient Boosting Regressor and K-Nearest Neighbors also benefited from hyperparameter optimization. A comparative study with benchmark approaches shows that the optimized models in this work are superior in both predictive accuracy and computational efficiency. The proposed framework is scalable, as it can be adapted to different PV datasets while requiring fewer computational resources than deep learning methods, thereby bridging the gap between traditional machine learning approaches and practical energy management systems.
{"title":"A scalable forecasting framework for PV systems using hyper-tuned regressors and environmental data","authors":"Mohamed A. Atiea , Ali M. El-Rifaie , Ghareeb Moustafa , Abdullah M. Shaheen","doi":"10.1016/j.uncres.2025.100236","DOIUrl":"10.1016/j.uncres.2025.100236","url":null,"abstract":"<div><div>Forecasting photovoltaic (PV) power output is essential for reliable grid integration, operational planning, and supporting the global transition toward renewable energy. This paper proposes an integrated machine learning framework that improves prediction accuracy through systematically designed preprocessing, model selection, and advanced hyperparameter optimization. Using a high-resolution dataset from the Sharda University PV system, 13 regression models, including ensemble methods and neural networks, are tested and compared with the aim of maximizing generalizability and predictive performance. Performance gains are achieved through structured hyperparameter optimization using Randomized Search Cross-Validation (RSCV) and Grid Search Cross-Validation (GSCV), where the Random Forest Regressor achieved an R<sup>2</sup> of 0.9561 before tuning and 0.9893 after tuning, representing the highest improvement. Gradient Boosting Regressor and K-Nearest Neighbors also benefited from hyperparameter optimization. A comparative study with benchmark approaches shows that the optimized models in this work are superior in both predictive accuracy and computational efficiency. The proposed framework is scalable, as it can be adapted to different PV datasets while requiring fewer computational resources than deep learning methods, thereby bridging the gap between traditional machine learning approaches and practical energy management systems.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100236"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004308","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}
Unutilized thermal energy is a prevalent issue, particularly in cooking-related activities within kitchens. Thermoelectric technology presents a viable solution by converting this wasted heat into alternative electrical energy, a prospect that has garnered significant research interest in applying of thermoelectric generators. In this study, a numerical model is developed to evaluate the combined effect of stage configuration and positive leg material on the performance of a kitchen hood–based thermoelectric generator system. The design employs a cross-flow arrangement, in which hot air from the hood provides the heat source and outside air serves as the cooling medium. The investigation is conducted using ANSYS simulation software. The results show that the two-stage design with BiTe as the positive leg provides the best performance, producing 10.74 W from a 40 × 40 mm module. When scaled to a full kitchen hood containing 600 modules, the output reaches 6.104 kW. This work highlights a pathway to transform wasted kitchen heat into a meaningful power source. It demonstrates that carefully selecting the stage number and material configuration can substantially improve system efficiency.
{"title":"Thermoelectric system optimization for waste heat energy recovery in building kitchen hoods","authors":"Catur Harsito , Rezi Delfianti , Federico Minelli , Rafiel Carino Syahroni , Fauzan Nusyura","doi":"10.1016/j.uncres.2025.100251","DOIUrl":"10.1016/j.uncres.2025.100251","url":null,"abstract":"<div><div>Unutilized thermal energy is a prevalent issue, particularly in cooking-related activities within kitchens. Thermoelectric technology presents a viable solution by converting this wasted heat into alternative electrical energy, a prospect that has garnered significant research interest in applying of thermoelectric generators. In this study, a numerical model is developed to evaluate the combined effect of stage configuration and positive leg material on the performance of a kitchen hood–based thermoelectric generator system. The design employs a cross-flow arrangement, in which hot air from the hood provides the heat source and outside air serves as the cooling medium. The investigation is conducted using ANSYS simulation software. The results show that the two-stage design with BiTe as the positive leg provides the best performance, producing 10.74 W from a 40 × 40 mm module. When scaled to a full kitchen hood containing 600 modules, the output reaches 6.104 kW. This work highlights a pathway to transform wasted kitchen heat into a meaningful power source. It demonstrates that carefully selecting the stage number and material configuration can substantially improve system efficiency.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100251"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219890","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}
Transitioning from traditional power systems to advanced smart grid and microgrid systems is crucial for meeting increasing energy demands and ensuring a reliable and secure power supply. Integrating renewable energy sources, electric vehicle charging, power electronics, and nonlinear loads complicates the system dynamics and introduces operational uncertainties. Furthermore, this poses challenges to system protection, fault detection and location, control, and power quality. Factors such as dynamic system behaviour, reduced inertia, bidirectional power flow, and a low short-circuit ratio exacerbate these challenges. This paper reviews advanced protection schemes and fault detection, estimation, and location techniques, with a focus on traveling wave (TW) technology. It provides a comprehensive overview of TW-based methods in distribution network protection, highlighting significant progress in fault detection, assessment, and location. In addition, it identifies existing research gaps and future development directions, including operational challenges that arise during and after implementation. The insights from this review are invaluable for researchers working to enhance power system protection. It aims to facilitate the development of innovative protection schemes to address the evolving challenges of the power grid. This work is instrumental in advancing state-of-the-art power system protection and is pivotal for the grid's future stability and efficiency.
{"title":"A critical review on traveling wave-based fault assessment and enhanced protection of distribution networks in smart grid scenario","authors":"Chinmayee Biswal , Binod Kumar Sahu , Pravat Kumar Rout , Manohar Mishra","doi":"10.1016/j.uncres.2025.100242","DOIUrl":"10.1016/j.uncres.2025.100242","url":null,"abstract":"<div><div>Transitioning from traditional power systems to advanced smart grid and microgrid systems is crucial for meeting increasing energy demands and ensuring a reliable and secure power supply. Integrating renewable energy sources, electric vehicle charging, power electronics, and nonlinear loads complicates the system dynamics and introduces operational uncertainties. Furthermore, this poses challenges to system protection, fault detection and location, control, and power quality. Factors such as dynamic system behaviour, reduced inertia, bidirectional power flow, and a low short-circuit ratio exacerbate these challenges. This paper reviews advanced protection schemes and fault detection, estimation, and location techniques, with a focus on traveling wave (TW) technology. It provides a comprehensive overview of TW-based methods in distribution network protection, highlighting significant progress in fault detection, assessment, and location. In addition, it identifies existing research gaps and future development directions, including operational challenges that arise during and after implementation. The insights from this review are invaluable for researchers working to enhance power system protection. It aims to facilitate the development of innovative protection schemes to address the evolving challenges of the power grid. This work is instrumental in advancing state-of-the-art power system protection and is pivotal for the grid's future stability and efficiency.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100242"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060105","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 : 2025-10-01Epub Date: 2025-09-12DOI: 10.1016/j.uncres.2025.100243
Wei Xia , Xiaowei Zhao , Qiu Li , Pan Wang , Rui Xu , Jiangtao Wu
Addressing the poor waterflooding performance (characterized by high injection pressure and high water cut) in the high-viscosity oil reservoir of Block A, Eastern China, this study systematically investigated the impact mechanisms of CO2 flooding injection-production parameters via PVT experiments, numerical simulations, and multi-factor optimization. Results demonstrate that CO2 exerts significant viscosity-reducing and swelling effects on high-viscosity crude oil, with its oil recovery efficiency being substantially higher than that of waterflooding. Ranking of influencing factors using the Spearman correlation coefficient reveals that CH4 can notably reduce the gas-oil ratio during CO2 flooding; moreover, a well pattern with gas injection in the upper section and oil production in the lower section enhances the recovery rate to 23.28 %. Additionally, a recovery rate prediction model with a fitting degree of 97.6 % was established. This research provides a scientific basis for optimizing CO2 flooding injection-production parameters in high pour-point oil reservoirs and offers valuable guidance for the development of analogous reservoirs.
{"title":"Model and application of CO2-EOR injection and production parameters for high pour-point oil reservoirs: A case study of SUBEI A reservoir","authors":"Wei Xia , Xiaowei Zhao , Qiu Li , Pan Wang , Rui Xu , Jiangtao Wu","doi":"10.1016/j.uncres.2025.100243","DOIUrl":"10.1016/j.uncres.2025.100243","url":null,"abstract":"<div><div>Addressing the poor waterflooding performance (characterized by high injection pressure and high water cut) in the high-viscosity oil reservoir of Block A, Eastern China, this study systematically investigated the impact mechanisms of CO<sub>2</sub> flooding injection-production parameters via PVT experiments, numerical simulations, and multi-factor optimization. Results demonstrate that CO<sub>2</sub> exerts significant viscosity-reducing and swelling effects on high-viscosity crude oil, with its oil recovery efficiency being substantially higher than that of waterflooding. Ranking of influencing factors using the Spearman correlation coefficient reveals that CH<sub>4</sub> can notably reduce the gas-oil ratio during CO<sub>2</sub> flooding; moreover, a well pattern with gas injection in the upper section and oil production in the lower section enhances the recovery rate to 23.28 %. Additionally, a recovery rate prediction model <span><math><mrow><msub><mi>R</mi><mi>f</mi></msub><mo>=</mo><mn>18.24</mn><mo>+</mo><mn>0.092</mn><mi>R</mi><mo>+</mo><mn>0.068</mn><mi>S</mi><mo>+</mo><mn>0.004</mn><msup><mi>P</mi><mn>2</mn></msup><mo>−</mo><mn>0.185</mn><mi>P</mi><mo>−</mo><mn>0.063</mn><mi>V</mi></mrow></math></span> with a fitting degree of 97.6 % was established. This research provides a scientific basis for optimizing CO<sub>2</sub> flooding injection-production parameters in high pour-point oil reservoirs and offers valuable guidance for the development of analogous reservoirs.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100243"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219361","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 : 2025-10-01Epub Date: 2025-08-25DOI: 10.1016/j.uncres.2025.100235
Salah Sabeeh Abed Al Kareem , Qusay Hassan , Hassan Falah Fakhruldeen , Talib Munshid Hanoon , Feryal Ibrahim Jabbar , Sameer Algburi , Doaa H. Khalaf
A comprehensive review of physical, chemical, and geological hydrogen storage and delivery methods to support sustainable energy systems is presented a survey of compressed gas, liquid hydrogen, adsorption on porous carbon and metal organic frameworks, metal and complex hydrides, liquid organic hydrogen carriers, and subsurface options such as salt caverns and depleted reservoirs is provided. Pathways are compared using energy density, reversibility, efficiency, safety, scalability, and cost, and synthesize design trade-offs across mobile and stationary applications. Compressed gas demonstrates technological maturity yet faces compression energy penalties and lower volumetric density. Liquid hydrogen offers compact storage and long-distance transport but contends with liquefaction energy demand and boil-off losses. Metal and complex hydrides enable dense, inherently contained storage, with challenges in heat management and reaction kinetics. Adsorption materials show promise yet often require low temperature for high uptake. Liquid organic hydrogen carriers leverage familiar logistics at the expense of catalytic dehydrogenation steps and efficiency. Geological storage provides seasonal and strategic capacity, with salt caverns emerging as strong candidates while contamination and integrity risks require monitoring and robust standards. Highlight hybrid architectures that pair high-pressure tanks with hydride beds and advanced cryo-compressed approaches that increase practical capacity for mobility. Priorities include faster kinetics at moderate temperature, durable sorbents and hydrides, loss mitigation, standardized safety protocols, techno-economic benchmarks, and integration with renewable grids and transport.
{"title":"A review on physical and chemical hydrogen storage methods for sustainable energy applications","authors":"Salah Sabeeh Abed Al Kareem , Qusay Hassan , Hassan Falah Fakhruldeen , Talib Munshid Hanoon , Feryal Ibrahim Jabbar , Sameer Algburi , Doaa H. Khalaf","doi":"10.1016/j.uncres.2025.100235","DOIUrl":"10.1016/j.uncres.2025.100235","url":null,"abstract":"<div><div>A comprehensive review of physical, chemical, and geological hydrogen storage and delivery methods to support sustainable energy systems is presented a survey of compressed gas, liquid hydrogen, adsorption on porous carbon and metal organic frameworks, metal and complex hydrides, liquid organic hydrogen carriers, and subsurface options such as salt caverns and depleted reservoirs is provided. Pathways are compared using energy density, reversibility, efficiency, safety, scalability, and cost, and synthesize design trade-offs across mobile and stationary applications. Compressed gas demonstrates technological maturity yet faces compression energy penalties and lower volumetric density. Liquid hydrogen offers compact storage and long-distance transport but contends with liquefaction energy demand and boil-off losses. Metal and complex hydrides enable dense, inherently contained storage, with challenges in heat management and reaction kinetics. Adsorption materials show promise yet often require low temperature for high uptake. Liquid organic hydrogen carriers leverage familiar logistics at the expense of catalytic dehydrogenation steps and efficiency. Geological storage provides seasonal and strategic capacity, with salt caverns emerging as strong candidates while contamination and integrity risks require monitoring and robust standards. Highlight hybrid architectures that pair high-pressure tanks with hydride beds and advanced cryo-compressed approaches that increase practical capacity for mobility. Priorities include faster kinetics at moderate temperature, durable sorbents and hydrides, loss mitigation, standardized safety protocols, techno-economic benchmarks, and integration with renewable grids and transport.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100235"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917480","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 : 2025-10-01Epub Date: 2025-08-08DOI: 10.1016/j.uncres.2025.100230
Saaransh Choudhary , Shiv Lal , Sumit Verma
Solar radiance is used as a fuel in a solar photovoltaic plant to generate electricity. The maximum solar radiance mainly depends on latitudes and optimal tilt angle. This paper aims to determine the optimal tilt angle for various locations in Rajasthan, India. For this purpose, a general algorithm for the optimization of the solar tilt angle is investigated based on MATLAB software for four different locations in Rajasthan, India. The four different locations are Kota, Barmer, Jodhpur, and Jaisalmer, whose average optimal tilt angles and latitudes are 27.88° (25.21°N), 27.15°(25.75°N), 27.54° (26.23°N), and 28.38° (26.91°N), respectively. This study investigates the seasonal optimization of solar photovoltaic module tilt angles to optimize the energy efficiency of solar photovoltaic plants in Rajasthan. The results reveal that dynamic tilt adjustments can boost annual solar yield by 8–9 % across diverse climatic zones.
From the analysis, Jodhpur has shown the highest average solar radiance during the summer season (301.5 W/m2), while Kota city experienced the lowest values during the monsoon season (229.4 W/m2). Post-monsoon data indicate a recovery in radiance across all locations, with Jaisalmer achieving the highest average solar radiance value (289.2 W/m2) annually. The results reveal a clear seasonal trend, where optimum tilt angles are higher in the winter season (58°–60°) and gradually decrease to 0° during the summer season (May to July), reflecting the changing solar altitude. By using optimal tilt angles, the values of solar radiance can be improved by approximately 8 %–9 % across all locations of Rajasthan from tracking solar systems to achieve the maximum solar output power. The regression analysis indicates that fixed tilt systems become less optimal as latitude increases, suggesting greater potential benefits from tracking systems in northern locations. The slope (b) decreases from south to north (0.8392–0.5986), whereas the intercept (a) increases from south to north (61.0957–128.7876). R2 values decrease northward (0.8273–0.6082).
{"title":"Seasonal optimization of solar PV tilt angles for enhanced energy efficiency in Rajasthan, India","authors":"Saaransh Choudhary , Shiv Lal , Sumit Verma","doi":"10.1016/j.uncres.2025.100230","DOIUrl":"10.1016/j.uncres.2025.100230","url":null,"abstract":"<div><div>Solar radiance is used as a fuel in a solar photovoltaic plant to generate electricity. The maximum solar radiance mainly depends on latitudes and optimal tilt angle. This paper aims to determine the optimal tilt angle for various locations in Rajasthan, India. For this purpose, a general algorithm for the optimization of the solar tilt angle is investigated based on MATLAB software for four different locations in Rajasthan, India. The four different locations are Kota, Barmer, Jodhpur, and Jaisalmer, whose average optimal tilt angles and latitudes are 27.88° (25.21°N), 27.15°(25.75°N), 27.54° (26.23°N), and 28.38° (26.91°N), respectively. This study investigates the seasonal optimization of solar photovoltaic module tilt angles to optimize the energy efficiency of solar photovoltaic plants in Rajasthan. The results reveal that dynamic tilt adjustments can boost annual solar yield by 8–9 % across diverse climatic zones.</div><div>From the analysis, Jodhpur has shown the highest average solar radiance during the summer season (301.5 W/m<sup>2</sup>), while Kota city experienced the lowest values during the monsoon season (229.4 W/m<sup>2</sup>). Post-monsoon data indicate a recovery in radiance across all locations, with Jaisalmer achieving the highest average solar radiance value (289.2 W/m<sup>2</sup>) annually. The results reveal a clear seasonal trend, where optimum tilt angles are higher in the winter season (58°–60°) and gradually decrease to 0° during the summer season (May to July), reflecting the changing solar altitude. By using optimal tilt angles, the values of solar radiance can be improved by approximately 8 %–9 % across all locations of Rajasthan from tracking solar systems to achieve the maximum solar output power. The regression analysis indicates that fixed tilt systems become less optimal as latitude increases, suggesting greater potential benefits from tracking systems in northern locations. The slope (b) decreases from south to north (0.8392–0.5986), whereas the intercept (a) increases from south to north (61.0957–128.7876). R<sup>2</sup> values decrease northward (0.8273–0.6082).</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100230"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830770","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 : 2025-10-01Epub Date: 2025-07-17DOI: 10.1016/j.uncres.2025.100210
Yanan Miao , Xin Li , Xiaofei Fu , Shu Jiang , Pengfei Wang , Xuejia Du , Xiaoxiao Leng , Wenjie Liu , Haoran Wang
<div><div>Substantial hydrocarbons in deep-buried reservoirs are challenged by diagenetically induced heterogeneity, hindering the identification of sweet-spot prospects. Despite being a common diagenetic mineral, genesis of kaolinite is rarely explored from a geochemical perspective, and much less is known about the effects of differential genetic kaolinite on reservoirs. In this paper, the distributary channel of Pinghu Formation in Xihu Sag was selected as a focused object. Petrological and geochemical analyses were conducted, including porosity/permeability test, light/electron microscope observation, electron probe test, and fluids inclusion measurement. In particular, hydrogen/oxygen (H/O) isotopes were applied to determine the genetic mechanisms of kaolinite. The results show that lithology types of distributary channel are mainly lithic arkose and feldspathic litharenite, with quartz comprising 65 %, feldspar sharing 16 %, and fragments sharing 19 % of the total sediments. Despite the uniformity of its detrital components, physical characteristics of the distributary channel exhibit significant variation. Porosity ranges from 3.3 % to 21.4 % (averaging 13.8 %), and permeability ranges from 0.02mD to 614.4mD (averaging 52.1mD). Furthermore, within individual channels, porosity/permeability values are high in the upper sections but fall in the lower. Kaolinite cementation can be observed in both the upper and lower channels, but exhibiting distinctive petrological and geochemical features. In the upper channels, kaolinite is characterized by an embedded-crystal form and low Mg/Ca/Fe content. Based on its high H/O isotopes (averaging −87.9 ‰ δD<sub>-SMOW</sub> and 12.3 ‰ δ<sup>18</sup>O<sub>-SMOW</sub>), the temperature of kaolinite cementation is estimated in the range of 90 °C–110 °C and the calculated δD<sub>water-SMOW</sub>/δ<sup>18</sup>O<sub>water-SMOW</sub> (averaging −90.7 ‰/-11.1 ‰) approached to the organic water region. These features suggest that kaolinite in the upper channels is the by-product of feldspar dissolution by organic acids. High kaolinite content indicates significant feldspar dissolution and extensive secondary dissolved pore space, which is a positive indicator of secondary pore development. In the lower channels, kaolinite is characterized by a sheet-crystal form and high Mg/Ca/Fe content. Based on its low H/O isotopes (averaging −103.8 ‰ δD<sub>-SMOW</sub> and 2.0 ‰ δ<sup>18</sup>O<sub>-SMOW</sub>), the temperature of kaolinite cementation is estimated in the range of 25 °C–50 °C, and the calculated δD<sub>water-SMOW</sub>/δ<sup>18</sup>O<sub>water-SMOW</sub> (averaging −60.5 ‰/-9.4 ‰) indicates a subsurface paleo-fluid environment. These features imply that kaolinite in the lower channels may derive from the recrystallization of muddy fragments. High kaolinite content indicates poor sorting, weak compaction resistance, and low dissolution extent, which negatively impacts both primary pore preservation and second
{"title":"Kaolinite origins and distinctive influences on deep-buried reservoir: A case study of Pinghu Formation in Xihu Depression, offshore China","authors":"Yanan Miao , Xin Li , Xiaofei Fu , Shu Jiang , Pengfei Wang , Xuejia Du , Xiaoxiao Leng , Wenjie Liu , Haoran Wang","doi":"10.1016/j.uncres.2025.100210","DOIUrl":"10.1016/j.uncres.2025.100210","url":null,"abstract":"<div><div>Substantial hydrocarbons in deep-buried reservoirs are challenged by diagenetically induced heterogeneity, hindering the identification of sweet-spot prospects. Despite being a common diagenetic mineral, genesis of kaolinite is rarely explored from a geochemical perspective, and much less is known about the effects of differential genetic kaolinite on reservoirs. In this paper, the distributary channel of Pinghu Formation in Xihu Sag was selected as a focused object. Petrological and geochemical analyses were conducted, including porosity/permeability test, light/electron microscope observation, electron probe test, and fluids inclusion measurement. In particular, hydrogen/oxygen (H/O) isotopes were applied to determine the genetic mechanisms of kaolinite. The results show that lithology types of distributary channel are mainly lithic arkose and feldspathic litharenite, with quartz comprising 65 %, feldspar sharing 16 %, and fragments sharing 19 % of the total sediments. Despite the uniformity of its detrital components, physical characteristics of the distributary channel exhibit significant variation. Porosity ranges from 3.3 % to 21.4 % (averaging 13.8 %), and permeability ranges from 0.02mD to 614.4mD (averaging 52.1mD). Furthermore, within individual channels, porosity/permeability values are high in the upper sections but fall in the lower. Kaolinite cementation can be observed in both the upper and lower channels, but exhibiting distinctive petrological and geochemical features. In the upper channels, kaolinite is characterized by an embedded-crystal form and low Mg/Ca/Fe content. Based on its high H/O isotopes (averaging −87.9 ‰ δD<sub>-SMOW</sub> and 12.3 ‰ δ<sup>18</sup>O<sub>-SMOW</sub>), the temperature of kaolinite cementation is estimated in the range of 90 °C–110 °C and the calculated δD<sub>water-SMOW</sub>/δ<sup>18</sup>O<sub>water-SMOW</sub> (averaging −90.7 ‰/-11.1 ‰) approached to the organic water region. These features suggest that kaolinite in the upper channels is the by-product of feldspar dissolution by organic acids. High kaolinite content indicates significant feldspar dissolution and extensive secondary dissolved pore space, which is a positive indicator of secondary pore development. In the lower channels, kaolinite is characterized by a sheet-crystal form and high Mg/Ca/Fe content. Based on its low H/O isotopes (averaging −103.8 ‰ δD<sub>-SMOW</sub> and 2.0 ‰ δ<sup>18</sup>O<sub>-SMOW</sub>), the temperature of kaolinite cementation is estimated in the range of 25 °C–50 °C, and the calculated δD<sub>water-SMOW</sub>/δ<sup>18</sup>O<sub>water-SMOW</sub> (averaging −60.5 ‰/-9.4 ‰) indicates a subsurface paleo-fluid environment. These features imply that kaolinite in the lower channels may derive from the recrystallization of muddy fragments. High kaolinite content indicates poor sorting, weak compaction resistance, and low dissolution extent, which negatively impacts both primary pore preservation and second","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694314","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号