This research introduces an Adaptive Chaotic Salp Swarm Algorithm (AC-SSA) for optimizing the placement and sizing of Distributed Generation (DG) units in radial distribution networks. The proposed AC-SSA incorporates advanced population initialization and a stagnation-driven chaotic neighborhood search to enhance the balance between exploration and exploitation, hence averting premature convergence. The optimization concurrently reduces real and reactive power losses, voltage variation, and total energy expenditures, including investment, operational, and maintenance expenditures. The algorithm's efficacy is confirmed using the IEEE 33-bus and IEEE 69-bus test systems, utilizing the backward-forward sweep (BFS) approach for load flow analysis. The findings indicate that the proposed AC-SSA significantly reduces total power losses and voltage variations in comparison to traditional SSA and other metaheuristic algorithms. This insertion reduces the active power loss to 15 kW and 30 kW for the IEEE 33-bus and IEEE 69-bus systems, respectively, corresponding to a power-loss reduction of 92.59 % and 86.63 % when compared to the baseline networks. Moreover, the AC-SSA demonstrates expedited convergence, enhanced stability, and reduced standard deviation values, hence affirming its resilience and efficacy in addressing intricate multi-objective optimization challenges associated with DG integration. The results indicate that the AC-SSA is a viable method for smart grid planning and the optimization of power distribution based on renewable energy.
{"title":"Multi-objective optimization of distributed generation in electrical grids using an adaptive chaotic salp swarm algorithm","authors":"Meriem M'dioud , Youssef Er-Rays , Abdelfettah Bannari , Rachid Bannari , Badre Bossoufi , Ismail El Kafazi","doi":"10.1016/j.uncres.2025.100299","DOIUrl":"10.1016/j.uncres.2025.100299","url":null,"abstract":"<div><div>This research introduces an Adaptive Chaotic Salp Swarm Algorithm (AC-SSA) for optimizing the placement and sizing of Distributed Generation (DG) units in radial distribution networks. The proposed AC-SSA incorporates advanced population initialization and a stagnation-driven chaotic neighborhood search to enhance the balance between exploration and exploitation, hence averting premature convergence. The optimization concurrently reduces real and reactive power losses, voltage variation, and total energy expenditures, including investment, operational, and maintenance expenditures. The algorithm's efficacy is confirmed using the IEEE 33-bus and IEEE 69-bus test systems, utilizing the backward-forward sweep (BFS) approach for load flow analysis. The findings indicate that the proposed AC-SSA significantly reduces total power losses and voltage variations in comparison to traditional SSA and other metaheuristic algorithms. This insertion reduces the active power loss to 15 kW and 30 kW for the IEEE 33-bus and IEEE 69-bus systems, respectively, corresponding to a power-loss reduction of 92.59 % and 86.63 % when compared to the baseline networks. Moreover, the AC-SSA demonstrates expedited convergence, enhanced stability, and reduced standard deviation values, hence affirming its resilience and efficacy in addressing intricate multi-objective optimization challenges associated with DG integration. The results indicate that the AC-SSA is a viable method for smart grid planning and the optimization of power distribution based on renewable energy.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100299"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839724","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}
Concurrent approaches for estimating storage coefficients (E) of Geological Carbon Sequestration (GCS) target reservoirs are critically reviewed, and a robust procedure for estimation of such coefficients, which are time-dependent, is proposed. Our method is based on close analogy of what historically is done in hydrocarbon production and reserves estimations using recovery factors (F). Typically, F is computed by first estimating the original hydrocarbons in place (OHIP), then the cumulative production to a certain date (of the economic limit) is computed using production forecasting methods. The production forecast provides an estimated ultimate resource (EUR), and then F follows from the ratio EUR/OHIP. We propose to similarly compute the estimated ultimate storage (EUS) or cumulative injection by forward modeling, using Gaussian-based solutions of the pressure diffusivity equation, and after estimating the total storage resource (TSR), the coefficient E follows from the ratio EUS/TSR. The new method is demonstrated in a case study using representative data from the Porthos GCS Project, which repurposes the depleted P18 gas field (offshore, Dutch shelf area) for geological CO2 sequestration (GCS). The storage coefficient for the P18-6 segment of the Porthos GCS field after 20 years of injection reaches 18 %. In addition to the deterministic storage coefficient estimation, probabilistic values after 20 years of injection for E were estimated: P90-16 %, P50-36 % and P10-59 %. Separately, it is shown how a GCS project in a depleted gas field offers significant operational advantages over storage in saline aquifers. The competitive edge of depleted gas fields over saline aquifers has not been articulated before. The new methods for computing TSR, EUS and E, can handle probabilistic storage resource classification in compliance with the SPE SRMS classification framework for storage resource estimation.
{"title":"Estimation of storage capacity coefficients: Porthos GCS project case study","authors":"Ruud Weijermars , Clement Afagwu , Yakai Tian , Ibere Alves","doi":"10.1016/j.uncres.2025.100268","DOIUrl":"10.1016/j.uncres.2025.100268","url":null,"abstract":"<div><div>Concurrent approaches for estimating storage coefficients (<em>E</em>) of Geological Carbon Sequestration (GCS) target reservoirs are critically reviewed, and a robust procedure for estimation of such coefficients, which are time-dependent, is proposed. Our method is based on close analogy of what historically is done in hydrocarbon production and reserves estimations using recovery factors (<em>F</em>). Typically, <em>F</em> is computed by first estimating the original hydrocarbons in place (OHIP), then the cumulative production to a certain date (of the economic limit) is computed using production forecasting methods. The production forecast provides an estimated ultimate resource (EUR), and then <em>F</em> follows from the ratio EUR/OHIP. We propose to similarly compute the estimated ultimate storage (EUS) or cumulative injection by forward modeling, using Gaussian-based solutions of the pressure diffusivity equation, and after estimating the total storage resource (TSR), the coefficient <em>E</em> follows from the ratio EUS/TSR. The new method is demonstrated in a case study using representative data from the Porthos GCS Project, which repurposes the depleted P18 gas field (offshore, Dutch shelf area) for geological CO<sub>2</sub> sequestration (GCS). The storage coefficient for the P18-6 segment of the Porthos GCS field after 20 years of injection reaches 18 %. In addition to the deterministic storage coefficient estimation, probabilistic values after 20 years of injection for <em>E</em> were estimated: P90-16 %, P50-36 % and P10-59 %. Separately, it is shown how a GCS project in a depleted gas field offers significant operational advantages over storage in saline aquifers. The competitive edge of depleted gas fields over saline aquifers has not been articulated before. The new methods for computing TSR, EUS and <em>E</em>, can handle probabilistic storage resource classification in compliance with the SPE SRMS classification framework for storage resource estimation.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100268"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467406","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 : 2026-01-01Epub Date: 2025-12-16DOI: 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.
{"title":"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","authors":"Chen Zhang , Ruyue Wang , Yahao Huang , Junyi Shi , Zhongrui Wu , Ze Tao , Zhigang Wen , Jizhen Zhang","doi":"10.1016/j.uncres.2025.100295","DOIUrl":"10.1016/j.uncres.2025.100295","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100295"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789574","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 : 2026-01-01Epub Date: 2025-12-11DOI: 10.1016/j.uncres.2025.100293
Verónica Y. Zapata , Neil Craigie , Guillermina Sagasti
Geochemical data provide comprehensive insights into reservoir chemistry, commonly obtained from well logs or through the laboratory analysis of core or cuttings samples. Data may be missing from certain intervals due to mechanical failures or geological complexity. This study evaluates the efficacy of machine learning methods in predicting missing element values within the Vaca Muerta Formation (Tithonian) located in Argentina, utilising X-ray fluorescence (XRF) results from 14 wells. Two approaches were evaluated: 1) Imputation methods to complete missing elements across specific depth intervals and testing four algorithms: K-nearest neighbour (KNN), multiple imputations by chained equations (MICE), miceforest, and Multiple Imputation with Denoising Autoencoders (MIDAS); 2) Prediction methods for estimating trace elements based on major elements concentrations, using selected “training” wells and comparing four ensemble tree-based algorithms: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and Histogram-based Gradient Boosting Regression Tree (HGBT). Model validation involved statistical metrics (NRMSE and R2) and qualitative assessments, including comparisons between observed and predicted elemental values and comparison of wireline gamma-ray logs versus synthetic gamma-ray (derived from K, U, and Th). Imputation metrics indicated that KNN and miceforest outperformed alternative algorithms (NRMSE <0.1), with miceforest performing best qualitatively. The prediction results indicated similar accuracy among all tested algorithms, with a mean R2 reaching 0.83 in one of the two tested wells. In contrast, greater variability was observed in the second well (RF: 0.20–0.97; XGBoost: 0.05–0.96; LightGBM: 0.2–0.92; HGBT: 0.09–0.92). RF and XGBoost provided superior qualitative results. Both imputation and prediction methods exhibited reduced accuracy for Fe, Mg, Mn, Na, and P, primarily due to variations in depositional environments and diagenetic processes among training wells. This study demonstrates that small datasets (132–526 samples) and geological heterogeneity significantly impact model accuracy, and highlights the importance of combining qualitative and statistical evaluations.
{"title":"Machine learning applied to geochemical missing data of the Vaca Muerta unconventional reservoir: dealing with small sample sizes and heterogeneity","authors":"Verónica Y. Zapata , Neil Craigie , Guillermina Sagasti","doi":"10.1016/j.uncres.2025.100293","DOIUrl":"10.1016/j.uncres.2025.100293","url":null,"abstract":"<div><div>Geochemical data provide comprehensive insights into reservoir chemistry, commonly obtained from well logs or through the laboratory analysis of core or cuttings samples. Data may be missing from certain intervals due to mechanical failures or geological complexity. This study evaluates the efficacy of machine learning methods in predicting missing element values within the Vaca Muerta Formation (Tithonian) located in Argentina, utilising X-ray fluorescence (XRF) results from 14 wells. Two approaches were evaluated: 1) Imputation methods to complete missing elements across specific depth intervals and testing four algorithms: K-nearest neighbour (KNN), multiple imputations by chained equations (MICE), miceforest, and Multiple Imputation with Denoising Autoencoders (MIDAS); 2) Prediction methods for estimating trace elements based on major elements concentrations, using selected “training” wells and comparing four ensemble tree-based algorithms: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and Histogram-based Gradient Boosting Regression Tree (HGBT). Model validation involved statistical metrics (NRMSE and R2) and qualitative assessments, including comparisons between observed and predicted elemental values and comparison of wireline gamma-ray logs versus synthetic gamma-ray (derived from K, U, and Th). Imputation metrics indicated that KNN and miceforest outperformed alternative algorithms (NRMSE <0.1), with miceforest performing best qualitatively. The prediction results indicated similar accuracy among all tested algorithms, with a mean R2 reaching 0.83 in one of the two tested wells. In contrast, greater variability was observed in the second well (RF: 0.20–0.97; XGBoost: 0.05–0.96; LightGBM: 0.2–0.92; HGBT: 0.09–0.92). RF and XGBoost provided superior qualitative results. Both imputation and prediction methods exhibited reduced accuracy for Fe, Mg, Mn, Na, and P, primarily due to variations in depositional environments and diagenetic processes among training wells. This study demonstrates that small datasets (132–526 samples) and geological heterogeneity significantly impact model accuracy, and highlights the importance of combining qualitative and statistical evaluations.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100293"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789576","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}
Renewable energy has emerged as one of the most reliable and widely accepted approaches to address the rising global energy requirements. Among these, solar power development requires focused attention on both maintenance strategies and application methods. To enhance solar energy utilization, Internet of Things (IoT)-enabled monitoring frameworks have been designed, allowing real-time collection and analysis of solar parameters for predicting efficiency and ensuring stable electricity generation. A major concern in demand-side energy regulation lies in utilizing renewable sources effectively while keeping costs manageable and minimizing unnecessary consumption. Hence, careful planning of renewable resource integration is essential. Advanced energy management platforms play a crucial role in supervising energy distribution, especially in scenarios where heavy dependence on the grid exists. The complexities and opportunities introduced by expanding grid networks can be efficiently managed through cloud-based technologies. This work emphasizes the practical application of energy management systems in both industrial operations and academic research, treating them as key stakeholders in the energy sector. The investigation offers a detailed evaluation of IoT applications in photovoltaic power systems and highlights the promising future avenues available to researchers. These include developing new benchmarks to measure IoT performance and refining existing systems through innovative approaches. Furthermore, there is an increasing demand for comprehensive studies on intelligent energy frameworks in smart infrastructures. Such research is critical to advancing IoT-driven applications and sustaining continuous progress in this domain.
{"title":"A review of IoT enabled intelligent smart energy management for photovoltaic power forecasting and generation","authors":"Challa Krishna Rao , Sarat Kumar Sahoo , Franco Fernando Yanine","doi":"10.1016/j.uncres.2025.100279","DOIUrl":"10.1016/j.uncres.2025.100279","url":null,"abstract":"<div><div>Renewable energy has emerged as one of the most reliable and widely accepted approaches to address the rising global energy requirements. Among these, solar power development requires focused attention on both maintenance strategies and application methods. To enhance solar energy utilization, Internet of Things (IoT)-enabled monitoring frameworks have been designed, allowing real-time collection and analysis of solar parameters for predicting efficiency and ensuring stable electricity generation. A major concern in demand-side energy regulation lies in utilizing renewable sources effectively while keeping costs manageable and minimizing unnecessary consumption. Hence, careful planning of renewable resource integration is essential. Advanced energy management platforms play a crucial role in supervising energy distribution, especially in scenarios where heavy dependence on the grid exists. The complexities and opportunities introduced by expanding grid networks can be efficiently managed through cloud-based technologies. This work emphasizes the practical application of energy management systems in both industrial operations and academic research, treating them as key stakeholders in the energy sector. The investigation offers a detailed evaluation of IoT applications in photovoltaic power systems and highlights the promising future avenues available to researchers. These include developing new benchmarks to measure IoT performance and refining existing systems through innovative approaches. Furthermore, there is an increasing demand for comprehensive studies on intelligent energy frameworks in smart infrastructures. Such research is critical to advancing IoT-driven applications and sustaining continuous progress in this domain.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100279"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789670","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: 2025-09-10DOI: 10.1016/j.uncres.2025.100239
Mohamed Osman Atallah , Abdallah M. Elsayed , Mohammed H. Alqahtani , Abdullah M. Shaheen
As the world accelerates its transition toward net-zero emissions, green hydrogen production via seawater electrolysis presents a promising and innovative pathway for clean energy generation and ensuring long-term energy sustainability. This study investigates the feasibility of green hydrogen production through seawater electrolysis powered by renewable energy sources in four Egyptian coastal cities: New Alamein, El Tor, New Port Said (Salam), and Suez. A hybrid system comprising photovoltaic panels, wind turbines, batteries, a proton exchange membrane electrolyzer, hydrogen storage tanks, and diesel generators is proposed and evaluated under four distinct scenarios. Both stand-alone and grid-connected configurations are evaluated using HOMER Pro software to optimize system design based on techno-economic and environmental criteria. Multiple scenarios, incorporating varying numbers of wind turbines (15, 20, and 50), are assessed for each location. The results are compared depending on energy generation, hydrogen production, storage capacity, excess electricity, and key financial indicators, including net present cost, levelized cost of energy, and levelized cost of hydrogen. The findings indicate that Scenario Four, which achieves the lowest hydrogen production cost of 0.177 $/kg, demonstrates the most cost-effective performance. This result remains consistent across all studied locations and operating conditions, thereby highlighting the robustness and adaptability of the proposed hybrid system. This research demonstrates the technical viability of integrating renewable energy with seawater electrolysis for sustainable hydrogen production, contributing to Egypt's transition toward a low-carbon energy system and supporting the objectives of its national hydrogen strategy.
{"title":"Hybrid renewable energy systems for seawater-based green hydrogen in Egyptian coastal zones: A case study","authors":"Mohamed Osman Atallah , Abdallah M. Elsayed , Mohammed H. Alqahtani , Abdullah M. Shaheen","doi":"10.1016/j.uncres.2025.100239","DOIUrl":"10.1016/j.uncres.2025.100239","url":null,"abstract":"<div><div>As the world accelerates its transition toward net-zero emissions, green hydrogen production via seawater electrolysis presents a promising and innovative pathway for clean energy generation and ensuring long-term energy sustainability. This study investigates the feasibility of green hydrogen production through seawater electrolysis powered by renewable energy sources in four Egyptian coastal cities: New Alamein, El Tor, New Port Said (Salam), and Suez. A hybrid system comprising photovoltaic panels, wind turbines, batteries, a proton exchange membrane electrolyzer, hydrogen storage tanks, and diesel generators is proposed and evaluated under four distinct scenarios. Both stand-alone and grid-connected configurations are evaluated using HOMER Pro software to optimize system design based on techno-economic and environmental criteria. Multiple scenarios, incorporating varying numbers of wind turbines (15, 20, and 50), are assessed for each location. The results are compared depending on energy generation, hydrogen production, storage capacity, excess electricity, and key financial indicators, including net present cost, levelized cost of energy, and levelized cost of hydrogen. The findings indicate that Scenario Four, which achieves the lowest hydrogen production cost of 0.177 $/kg, demonstrates the most cost-effective performance. This result remains consistent across all studied locations and operating conditions, thereby highlighting the robustness and adaptability of the proposed hybrid system. This research demonstrates the technical viability of integrating renewable energy with seawater electrolysis for sustainable hydrogen production, contributing to Egypt's transition toward a low-carbon energy system and supporting the objectives of its national hydrogen strategy.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100239"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099442","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: 2025-08-19DOI: 10.1016/j.uncres.2025.100233
Ahmad S. Al Humssi , Elena G. Popkova , Bruno S. Sergi , Larisa N. Sorokina , Liliya G. Akhmetshina
The main purpose of this research is to examine the relationship between gross domestic product (GDP) and carbon dioxide emissions and domestic general government health expenditure per capita (GGHE_D_PC) for 166 countries in the period 2000–2020. In this research we use the ordinary least squares (OLS) method, quantile regression analysis, log-log models, and the multivariate comparative analysis method, in addition to other techniques such as the augmented Dickey–Fuller test, Johansen cointegration, Autoregressive conditional heteroskedasticity test, etc. According to the OLS method the research results show a positive direct impact of Gross domestic product on domestic general government health expenditure per capita, with an increase of 1 % in GDP for sample countries leading to an increase in on domestic general government health expenditure per capita of 0.337 %. Additionally, the results show that carbon dioxide emissions have direct positively affected domestic general government health expenditure per capita growth, an increase of 1 % in carbon dioxide emissions for sample countries can cause an increase in domestic general government health expenditure per capita of 0.3495 %. To investigate the impact of GDP and carbon dioxide emissions on domestic general government health expenditure per capita outside the OLS mean, we used quantile regression analysis at 20 levels (percentiles). The results show that the increase in GDP and carbon emissions is accompanied by an increase in general government spending on health care per capita in the high-income and developed countries in absolute units (in $US for GDP and in kilotons of CO2 emissions) at a higher rate than in the least developed and developing countries, but in percentage terms the situation is the opposite, with the percentage growth rate is highest in developing and least developed countries. Despite this high rate in these countries, and the flow of international aid to the health sector in these countries, the issue of increasing spending on health care per capita remains a fundamental problem due to limited sources of income, the high prevalence of poverty, weak health immunity and low health awareness of the population, and the spread of epidemics in many developing and least developed countries.
{"title":"The impact of GDP and CO2 emissions on domestic general government health expenditure per capita. Evidence from 166 countries","authors":"Ahmad S. Al Humssi , Elena G. Popkova , Bruno S. Sergi , Larisa N. Sorokina , Liliya G. Akhmetshina","doi":"10.1016/j.uncres.2025.100233","DOIUrl":"10.1016/j.uncres.2025.100233","url":null,"abstract":"<div><div>The main purpose of this research is to examine the relationship between gross domestic product (GDP) and carbon dioxide emissions and domestic general government health expenditure per capita (GGHE_D_PC) for 166 countries in the period 2000–2020. In this research we use the ordinary least squares (OLS) method, quantile regression analysis, log-log models, and the multivariate comparative analysis method, in addition to other techniques such as the augmented Dickey–Fuller test, Johansen cointegration, Autoregressive conditional heteroskedasticity test, etc. According to the OLS method the research results show a positive direct impact of Gross domestic product on domestic general government health expenditure per capita, with an increase of 1 % in GDP for sample countries leading to an increase in on domestic general government health expenditure per capita of 0.337 %. Additionally, the results show that carbon dioxide emissions have direct positively affected domestic general government health expenditure per capita growth, an increase of 1 % in carbon dioxide emissions for sample countries can cause an increase in domestic general government health expenditure per capita of 0.3495 %. To investigate the impact of GDP and carbon dioxide emissions on domestic general government health expenditure per capita outside the OLS mean, we used quantile regression analysis at 20 levels (percentiles). The results show that the increase in GDP and carbon emissions is accompanied by an increase in general government spending on health care per capita in the high-income and developed countries in absolute units (in $US for GDP and in kilotons of CO<sub>2</sub> emissions) at a higher rate than in the least developed and developing countries, but in percentage terms the situation is the opposite, with the percentage growth rate is highest in developing and least developed countries. Despite this high rate in these countries, and the flow of international aid to the health sector in these countries, the issue of increasing spending on health care per capita remains a fundamental problem due to limited sources of income, the high prevalence of poverty, weak health immunity and low health awareness of the population, and the spread of epidemics in many developing and least developed countries.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100233"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886941","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: 2025-07-23DOI: 10.1016/j.uncres.2025.100225
Chen Guo , Xiankuo Yang , Yong Qin , Pengrui Lyu , Reza Taherdangko , Lingling Lu , Xi Cheng
The development of coalbed methane (CBM) resources is controlled by the characteristics of CBM systems. Three types of superposed CBM systems, including growing, decaying, and stable, were recognized in the Zhijin Block of western Guizhou, South China, each exhibiting differences in hydrodynamic conditions. Insufficient understanding of these differences currently hinders the efficient CBM development in the Zhijin Block. Hydrochemical signatures of the Zhucang and Agong synclines were investigated using produced water samples from CBM wells, which develop a decaying-type and a stable-type CBM system, respectively. The hydrochemical characteristics and formation processes of the produced water, along with their implications for the CBM development, were analyzed. Produced water chemistry exhibits marked contrasts between the Agong and Zhucang synclines, with the former exhibiting high SO42− concentration and the latter exhibiting high Cl−, HCO3−, Na+, and TDS concentrations. Based on the theoretical calculations, the groundwater flow velocity in the coal seams of the Agong syncline is 7.49 times that of the Zhucang syncline. As a result, the CBM enrichment and production potential in the Agong syncline is lower. Three genetic mechanisms of hydrochemical evolution affecting the water qualities were identified: the retention (salt accumulation) process, the desulfurization (reduction) process, and the recharge (oxidation) process. The coal measure water in the Agong and Zhucang synclines undergo oxidation and reduction process after receiving surface water replenishment, respectively. Key hydrochemical parameters, including γNa+/γCl−, γCl−/γHCO3-, γCl−, and γSr2+, exhibit a power function relationship with peak daily gas production. Critical values of the key hydrochemical parameters, which indicate gas production potential, can be extracted based on this relationship. For example, when the γNa+/γCl− is greater than 142, gas production rates are generally low, predominantly observed in CBM wells within the Agong syncline. These findings enable a better understanding of variations in CBM accumulation and production potential across the Zhijin Block, and provide a basis for optimizing CBM development plans and technologies under conditions of superposed CBM systems.
{"title":"Coalbed methane systems and hydrochemical signature of produced water in the Zhijin Block, Western Guizhou province, China: Implications for gas production","authors":"Chen Guo , Xiankuo Yang , Yong Qin , Pengrui Lyu , Reza Taherdangko , Lingling Lu , Xi Cheng","doi":"10.1016/j.uncres.2025.100225","DOIUrl":"10.1016/j.uncres.2025.100225","url":null,"abstract":"<div><div>The development of coalbed methane (CBM) resources is controlled by the characteristics of CBM systems. Three types of superposed CBM systems, including growing, decaying, and stable, were recognized in the Zhijin Block of western Guizhou, South China, each exhibiting differences in hydrodynamic conditions. Insufficient understanding of these differences currently hinders the efficient CBM development in the Zhijin Block. Hydrochemical signatures of the Zhucang and Agong synclines were investigated using produced water samples from CBM wells, which develop a decaying-type and a stable-type CBM system, respectively. The hydrochemical characteristics and formation processes of the produced water, along with their implications for the CBM development, were analyzed. Produced water chemistry exhibits marked contrasts between the Agong and Zhucang synclines, with the former exhibiting high SO<sub>4</sub><sup>2−</sup> concentration and the latter exhibiting high Cl<sup>−</sup>, HCO<sub>3</sub><sup>−</sup>, Na<sup>+</sup>, and TDS concentrations. Based on the theoretical calculations, the groundwater flow velocity in the coal seams of the Agong syncline is 7.49 times that of the Zhucang syncline. As a result, the CBM enrichment and production potential in the Agong syncline is lower. Three genetic mechanisms of hydrochemical evolution affecting the water qualities were identified: the retention (salt accumulation) process, the desulfurization (reduction) process, and the recharge (oxidation) process. The coal measure water in the Agong and Zhucang synclines undergo oxidation and reduction process after receiving surface water replenishment, respectively. Key hydrochemical parameters, including γNa<sup>+</sup>/γCl<sup>−</sup>, γCl<sup>−</sup>/γHCO<sub>3</sub><sup>-</sup>, γCl<sup>−</sup>, and γSr<sup>2+</sup>, exhibit a power function relationship with peak daily gas production. Critical values of the key hydrochemical parameters, which indicate gas production potential, can be extracted based on this relationship. For example, when the γNa<sup>+</sup>/γCl<sup>−</sup> is greater than 142, gas production rates are generally low, predominantly observed in CBM wells within the Agong syncline. These findings enable a better understanding of variations in CBM accumulation and production potential across the Zhijin Block, and provide a basis for optimizing CBM development plans and technologies under conditions of superposed CBM systems.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100225"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749829","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: 2025-08-15DOI: 10.1016/j.uncres.2025.100232
Zhengbin Wu , Morice Richard Mworia , Kun Shu , Yuhang Ren , Qiyang Gou , Shu Jiang , Zhangxin Chen
SAGD is one of the steam recovery methods of heavy oil, which is typically performed using one horizontal injection well and one horizontal production well. However, multiple injection and production wells might also be utilized. Recently, studies of this method involving steam injection rate and pressure parameterization were performed over limited ranges of these parameters, with limited knowledge of steam injection rate characterization based on different levels of average homogeneity and heterogeneity of permeability systems. Moreover, there are no simulation studies of vertical injectors and multiple horizontal injection wells, or steam injection rates characterization. This paper examines these limitations considering the reservoir and fluid properties of the Liaohe heavy oil field in China. It is revealed that elevating pressures for a given steam injection rate do not have a significant impact on heavy oil recovery performance factors. A recommendation of injection at slightly higher than the initial reservoir pressure (6.3 MPa), which is 6.5 MPa to compensate for steam compression costs. Increasing the steam injection rate within a limited range provides a larger volume for the steam chambers that raise the average reservoir temperature and result in higher oil recovery factors. The positive-rhythm reservoir (permeability rises from the top to the bottom of the reservoir) extracts more oil than that of the negative-rhythm one (permeability decreases from the top to the bottom of the reservoir). The vertical injector has better oil-sweep efficiency compared with the horizontal injector due to more expandable steam chambers with a very small steam chamber rising stage. Two horizontal injectors at the same total steam injection rate as conventional SAGD enhance the oil recovery factor (an increment of up to 15.24 %) and reduce cumulative steam-oil ratio by 3.64.
{"title":"Steam injection pressures and rates, variable permeabilities systems, and wells alignments parameterization in SAGD: A simulation study","authors":"Zhengbin Wu , Morice Richard Mworia , Kun Shu , Yuhang Ren , Qiyang Gou , Shu Jiang , Zhangxin Chen","doi":"10.1016/j.uncres.2025.100232","DOIUrl":"10.1016/j.uncres.2025.100232","url":null,"abstract":"<div><div>SAGD is one of the steam recovery methods of heavy oil, which is typically performed using one horizontal injection well and one horizontal production well. However, multiple injection and production wells might also be utilized. Recently, studies of this method involving steam injection rate and pressure parameterization were performed over limited ranges of these parameters, with limited knowledge of steam injection rate characterization based on different levels of average homogeneity and heterogeneity of permeability systems. Moreover, there are no simulation studies of vertical injectors and multiple horizontal injection wells, or steam injection rates characterization. This paper examines these limitations considering the reservoir and fluid properties of the Liaohe heavy oil field in China. It is revealed that elevating pressures for a given steam injection rate do not have a significant impact on heavy oil recovery performance factors. A recommendation of injection at slightly higher than the initial reservoir pressure (6.3 MPa), which is 6.5 MPa to compensate for steam compression costs. Increasing the steam injection rate within a limited range provides a larger volume for the steam chambers that raise the average reservoir temperature and result in higher oil recovery factors. The positive-rhythm reservoir (permeability rises from the top to the bottom of the reservoir) extracts more oil than that of the negative-rhythm one (permeability decreases from the top to the bottom of the reservoir). The vertical injector has better oil-sweep efficiency compared with the horizontal injector due to more expandable steam chambers with a very small steam chamber rising stage. Two horizontal injectors at the same total steam injection rate as conventional SAGD enhance the oil recovery factor (an increment of up to 15.24 %) and reduce cumulative steam-oil ratio by 3.64.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100232"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861160","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: 2025-07-24DOI: 10.1016/j.uncres.2025.100224
Mourad Yessef , Habib Benbouhenni , Ahmed Lagrioui , Youness El Mourabit , Nicu Bizon , Ilhami Colak , Badre Bossoufi , Ayman Alhejji
One of the most well-known nonlinear methods that is not based on the mathematical model of the wind conversion system is super-twisting control. This method is one of the best alternatives due to its excellent performance and robustness. However, this control technique has drawbacks, such as the presence of a significant gains number and the susceptibility to malfunctions in the complex wind energy system. Accordingly, a suitable solution for applying the super-twisting control strategy in the system control domain is proposed under the name "modified super-twisting control". This enhanced technique is characterized by its algorithmic simplicity, a reduced number of control gains, straightforward implementation on embedded platforms, and low computational and hardware cost, making it particularly suitable for real-time control applications. The proposed method strategy was applied to the direct power control method of a doubly fed induction generator, for which purpose a controller identifies and determines the reference voltage values for the machine's inverter. In addition to the use of the suggested control strategy, pulse width modulation was employed to control inverter operation. The proposed novel control strategy is characterized by its simplicity, minimal gain requirements, ease of implementation on embedded systems, and fast dynamic response. This proposed strategy was used in this research project to improve the quality of the supplied energy and reduce the value obtained for the various total harmonic distortion of the Fast Fourier Transform analysis of the supplied system currents and minimizing generated power overshoot. This proposed innovative strategy was, first, verified and implemented in a simulation environment. Then, Processor-in-the-Loop implementation was used to verify the behavior of this strategy in real-time embedded implementation, and compare the numerical results with conventional and typical control method strategies and some recent research works. Furthermore, the designed strategy reduced the ripples value, overshoot, and steady-state error of active power by estimated percentages of 78.84 %, 66.66 %, and 50 %, respectively, compared to the conventional direct power control strategy. Furthermore, the steady-state error, overshoot, and reactive power ripples were reduced by 60 %, 81.25 %, and 66.66 %, respectively, compared to the classical direct power control strategy.
{"title":"Optimizing wind energy conversion system efficiency using advanced modified super-twisting direct power control: Real-time implementation on dSPACE 1104 board","authors":"Mourad Yessef , Habib Benbouhenni , Ahmed Lagrioui , Youness El Mourabit , Nicu Bizon , Ilhami Colak , Badre Bossoufi , Ayman Alhejji","doi":"10.1016/j.uncres.2025.100224","DOIUrl":"10.1016/j.uncres.2025.100224","url":null,"abstract":"<div><div>One of the most well-known nonlinear methods that is not based on the mathematical model of the wind conversion system is super-twisting control. This method is one of the best alternatives due to its excellent performance and robustness. However, this control technique has drawbacks, such as the presence of a significant gains number and the susceptibility to malfunctions in the complex wind energy system. Accordingly, a suitable solution for applying the super-twisting control strategy in the system control domain is proposed under the name \"modified super-twisting control\". This enhanced technique is characterized by its algorithmic simplicity, a reduced number of control gains, straightforward implementation on embedded platforms, and low computational and hardware cost, making it particularly suitable for real-time control applications. The proposed method strategy was applied to the direct power control method of a doubly fed induction generator, for which purpose a controller identifies and determines the reference voltage values for the machine's inverter. In addition to the use of the suggested control strategy, pulse width modulation was employed to control inverter operation. The proposed novel control strategy is characterized by its simplicity, minimal gain requirements, ease of implementation on embedded systems, and fast dynamic response. This proposed strategy was used in this research project to improve the quality of the supplied energy and reduce the value obtained for the various total harmonic distortion of the Fast Fourier Transform analysis of the supplied system currents and minimizing generated power overshoot. This proposed innovative strategy was, first, verified and implemented in a simulation environment. Then, Processor-in-the-Loop implementation was used to verify the behavior of this strategy in real-time embedded implementation, and compare the numerical results with conventional and typical control method strategies and some recent research works. Furthermore, the designed strategy reduced the ripples value, overshoot, and steady-state error of active power by estimated percentages of 78.84 %, 66.66 %, and 50 %, respectively, compared to the conventional direct power control strategy. Furthermore, the steady-state error, overshoot, and reactive power ripples were reduced by 60 %, 81.25 %, and 66.66 %, respectively, compared to the classical direct power control strategy.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"8 ","pages":"Article 100224"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738448","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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