Pub Date : 2026-01-01Epub Date: 2025-10-22DOI: 10.1016/j.uncres.2025.100267
Joy Nneamaka Obi , Emmanuel Ojo , Chika Oliver Ujah
This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability.
{"title":"Decentralised renewable energy in sub-Saharan Africa: A critical review of pathways to equitable and sustainable energy transitions","authors":"Joy Nneamaka Obi , Emmanuel Ojo , Chika Oliver Ujah","doi":"10.1016/j.uncres.2025.100267","DOIUrl":"10.1016/j.uncres.2025.100267","url":null,"abstract":"<div><div>This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100267"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365532","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}
This paper presents a comprehensive evaluation of several control strategies for dual-axis solar tracking systems, including proportional–integral–derivative, fuzzy logic, fuzzy–PID, and fuzzy–PID enhanced with model predictive control. Each controller was implemented in MATLAB/Simulink to analyse its dynamic and steady-state behaviour under identical conditions. The findings reveal that hybrid and MPC-based controllers achieve superior tracking precision and response smoothness compared to single-loop designs. Specifically, the fuzzy-tuned PID exhibits the fastest rise time of 12.53 but with a higher overshoot of 18.45 %. In contrast, the standalone fuzzy controller offers superior stability with a minimal overshoot of 0.50 %, though at the expense of slower dynamics with a rise time of 91.81 . The proposed MPC–Fuzzy–PID Series hybrid achieves a rapid rise time of 16.02 and a settling time of 0.2 providing a balanced trade-off between speed, stability, and computational efficiency, making it suitable for real-time solar tracking applications. Overall, the study demonstrates that controller performance depends on the specific operational goals whether prioritizing rapid response, precision, or robustness highlighting the importance of adaptive hybrid control design in sustainable energy systems.
{"title":"Modeling and simulation of hybrid fuzzy-PID and model predictive control for enhanced dual-axis photovoltaic tracking precision","authors":"Rezi Delfianti , Mohammed Mareai , Federico Minelli , Catur Harsito , Fauzan Nusyura","doi":"10.1016/j.uncres.2025.100303","DOIUrl":"10.1016/j.uncres.2025.100303","url":null,"abstract":"<div><div>This paper presents a comprehensive evaluation of several control strategies for dual-axis solar tracking systems, including proportional–integral–derivative, fuzzy logic, fuzzy–PID, and fuzzy–PID enhanced with model predictive control. Each controller was implemented in MATLAB/Simulink to analyse its dynamic and steady-state behaviour under identical conditions. The findings reveal that hybrid and MPC-based controllers achieve superior tracking precision and response smoothness compared to single-loop designs. Specifically, the fuzzy-tuned PID exhibits the fastest rise time of 12.53 <span><math><mrow><mi>m</mi><mi>s</mi></mrow></math></span> but with a higher overshoot of 18.45 %. In contrast, the standalone fuzzy controller offers superior stability with a minimal overshoot of 0.50 %, though at the expense of slower dynamics with a rise time of 91.81 <span><math><mrow><mi>m</mi><mi>s</mi></mrow></math></span>. The proposed MPC–Fuzzy–PID Series hybrid achieves a rapid rise time of 16.02 <span><math><mrow><mi>m</mi><mi>s</mi></mrow></math></span> and a settling time of 0.2 <span><math><mrow><mi>s</mi><mtext>,</mtext></mrow></math></span> providing a balanced trade-off between speed, stability, and computational efficiency, making it suitable for real-time solar tracking applications. Overall, the study demonstrates that controller performance depends on the specific operational goals whether prioritizing rapid response, precision, or robustness highlighting the importance of adaptive hybrid control design in sustainable energy systems.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100303"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924563","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: 2025-11-13DOI: 10.1016/j.uncres.2025.100275
H. Sharon , Ankit Kumar Jangir , Hitesh Kumawat , Aryan Singh , Marta Vivar , Seepana Bala Prasad
Solar photovoltaic modules play a vital role in the global clean energy transition. However, their efficient performance is hindered by rising operating temperature especially under harsh environments. Conversion efficiency of modules drops by at least 0.4–0.5 % for each 1.0 °C increment in its operating temperature from the standard testing condition of 25.0 °C. Hence, thermal management is essential for a module's sustained efficient performance. Evaporative cooling with water is more effective than any other passive module thermal management technique. In spite of seawater's abundance and inexpensiveness, it has not yet been utilized for module evaporative cooling in any of the available literatures. Hence, in this work, a novel passive evaporative cooling system utilizing a still seawater layer over a horizontally oriented module is proposed and tested under the climatic conditions of Visakhapatnam, Andhra Pradesh, India. This approach reduced module temperature on an average by around 8.8 °C. A 5.0 mm thick seawater layer improved the module's instantaneous power output by 0.14–31.0 %. Despite providing a tremendous cooling effect, seawater layer thickness of 30.0 and 4.0-mm had negative impact on a module's daily energy output due to increased light attenuation at high thickness and salt deposition caused by fast evaporation under low thickness, respectively. Low relative humidity and high wind speed facilitated rapid seawater evaporation, resulting in salt buildup over the module, emphasizing the importance of constant makeup water supply while operating at low water thickness (less than 5.0 mm) to avoid dry out. The overall heat transfer co-efficient of evaporatively cooled module was about 69.38–92.89 W/m2K, which was at least twice the value observed with the reference module. The observed results justifies the proposed thermal management technique because it is efficient and competitive with fin and phase change material-based module thermal management strategies. This highlights the necessity for further research and development towards improvement of this proposed technique for large scale applications.
{"title":"Photovoltaic module cooling with still seawater layer – Experimental study","authors":"H. Sharon , Ankit Kumar Jangir , Hitesh Kumawat , Aryan Singh , Marta Vivar , Seepana Bala Prasad","doi":"10.1016/j.uncres.2025.100275","DOIUrl":"10.1016/j.uncres.2025.100275","url":null,"abstract":"<div><div>Solar photovoltaic modules play a vital role in the global clean energy transition. However, their efficient performance is hindered by rising operating temperature especially under harsh environments. Conversion efficiency of modules drops by at least 0.4–0.5 % for each 1.0 °C increment in its operating temperature from the standard testing condition of 25.0 °C. Hence, thermal management is essential for a module's sustained efficient performance. Evaporative cooling with water is more effective than any other passive module thermal management technique. In spite of seawater's abundance and inexpensiveness, it has not yet been utilized for module evaporative cooling in any of the available literatures. Hence, in this work, a novel passive evaporative cooling system utilizing a still seawater layer over a horizontally oriented module is proposed and tested under the climatic conditions of Visakhapatnam, Andhra Pradesh, India. This approach reduced module temperature on an average by around 8.8 °C. A 5.0 mm thick seawater layer improved the module's instantaneous power output by 0.14–31.0 %. Despite providing a tremendous cooling effect, seawater layer thickness of 30.0 and 4.0-mm had negative impact on a module's daily energy output due to increased light attenuation at high thickness and salt deposition caused by fast evaporation under low thickness, respectively. Low relative humidity and high wind speed facilitated rapid seawater evaporation, resulting in salt buildup over the module, emphasizing the importance of constant makeup water supply while operating at low water thickness (less than 5.0 mm) to avoid dry out. The overall heat transfer co-efficient of evaporatively cooled module was about 69.38–92.89 W/m<sup>2</sup>K, which was at least twice the value observed with the reference module. The observed results justifies the proposed thermal management technique because it is efficient and competitive with fin and phase change material-based module thermal management strategies. This highlights the necessity for further research and development towards improvement of this proposed technique for large scale applications.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100275"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520598","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}
This study investigates the feasibility of cold in-situ bitumen recovery using a CO2 huff-n-puff process, focusing on foam swelling and expansion under reservoir conditions. Experiments simulate CO2 injection into Canadian bitumen at pressures of 2–4 MPa and temperatures of 5–55 °C to obtain swelling factors in the huff stage and expansion factors in the puff stage. Additional tests using a 1:1 M CO2-N2 mixture, pure N2, and pure O2 at 4 MPa and 25 °C provide comparative insights. Gas solubility in bitumen is assessed using PROPATH, the Peng–Robinson equation of state (EOS), and the Soave–Redlich–Kwong EOS. Results indicate that foam expansion is the dominant recovery mechanism, particularly at 4 MPa at 25 °C and 3–4 MPa at 5–15 °C, where expansion factors range from 9.7 to 10.7, independent of CO2 solubility anomalies. CO2 huff-n-puff also outperforms other gases, with expansion increasing proportionally to CO2 concentration. With bitumen expansion exceeding 10 times its initial volume, an estimated 66.7–88.9 % of the bitumen in place is expelled from the reservoir's pore space, enhancing recovery efficiency. The findings validate the feasibility of cold production techniques, demonstrating that optimal temperature and pressure conditions can be naturally achieved in Canada. This makes CO2 huff-n-puff a practical and efficient method for improving bitumen extraction, offering a promising alternative or complement to conventional thermal recovery processes.
{"title":"Experimental study on the expansion of foamy bitumen for CO2 huff-n-puff process","authors":"Sovanborey Meakh , Yuichi Sugai , Takehiro Esaki , Theodora Noely Tambaria","doi":"10.1016/j.uncres.2025.100265","DOIUrl":"10.1016/j.uncres.2025.100265","url":null,"abstract":"<div><div>This study investigates the feasibility of cold in-situ bitumen recovery using a CO<sub>2</sub> huff-n-puff process, focusing on foam swelling and expansion under reservoir conditions. Experiments simulate CO<sub>2</sub> injection into Canadian bitumen at pressures of 2–4 MPa and temperatures of 5–55 °C to obtain swelling factors in the huff stage and expansion factors in the puff stage. Additional tests using a 1:1 M CO<sub>2</sub>-N<sub>2</sub> mixture, pure N<sub>2</sub>, and pure O<sub>2</sub> at 4 MPa and 25 °C provide comparative insights. Gas solubility in bitumen is assessed using PROPATH, the Peng–Robinson equation of state (EOS), and the Soave–Redlich–Kwong EOS. Results indicate that foam expansion is the dominant recovery mechanism, particularly at 4 MPa at 25 °C and 3–4 MPa at 5–15 °C, where expansion factors range from 9.7 to 10.7, independent of CO<sub>2</sub> solubility anomalies. CO<sub>2</sub> huff-n-puff also outperforms other gases, with expansion increasing proportionally to CO<sub>2</sub> concentration. With bitumen expansion exceeding 10 times its initial volume, an estimated 66.7–88.9 % of the bitumen in place is expelled from the reservoir's pore space, enhancing recovery efficiency. The findings validate the feasibility of cold production techniques, demonstrating that optimal temperature and pressure conditions can be naturally achieved in Canada. This makes CO<sub>2</sub> huff-n-puff a practical and efficient method for improving bitumen extraction, offering a promising alternative or complement to conventional thermal recovery processes.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100265"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467403","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: 2025-11-13DOI: 10.1016/j.uncres.2025.100280
Lingxi Li , Yunlong Xu , Xiaoshui Mu , Zonyuan Yang , Debo Wang , Bo Yuan , Zhicheng Hu , Tianwu Xu , Xiang Cheng , Honglei Li , Dongdong Yang , Yaming Wang , Tao Hu
Shale oil and gas represent a research focus in petroleum exploration and development. Shale reservoirs exhibit strong heterogeneity, and their microscopic pore structure serves as an important basis for evaluating hydrocarbon storage performance. Taking the shale of the Shahejie Formation in the Dongpu Depression as the primary research object, this study employed various analytical techniques, including whole-rock X-ray diffraction, total organic carbon measurement, argon ion polishing, field emission scanning electron microscopy, low-temperature nitrogen adsorption, and high-pressure mercury intrusion, to analyze the heterogeneity of mixed shale reservoirs and identify the controlling factors. The research results indicate: The shale in the Shahejie Formation of the study area primarily consists of carbonate and clay minerals, with three lithofacies types: calcareous shale, argillaceous shale, and mixed shale. The mixed shale mainly develops intercrystalline pores in clay minerals, bedding fractures in clay minerals, intercrystalline pores in pyrite, intercrystalline pores in calcite, intragranular dissolution pores, organic matter shrinkage fractures, and round or elliptical organic matter pores. The mixed shale in the study area primarily develops slit-shaped micropores and small pores, which exhibit good connectivity. The average fractal dimension of macropores is 2.9924, and that of mesopores is 2.9630, both higher than those of micropores and small pores (2.4290 and 2.6361, respectively). Larger pores exhibit more complex internal structures and stronger heterogeneity. The distribution of micropores and small pores is relatively concentrated, while mesopores and macropores are more widely distributed, enhancing the heterogeneity of the reservoir structure. In the study area, carbonate and clay minerals are the main factors influencing reservoir heterogeneity. The dehydration of clay minerals forms bedding fractures, while carbonate minerals are prone to dissolution, forming intragranular dissolution pores. Due to the abundance of carbonate minerals in the study area, dissolution pores serve as connections between various types of pores, thereby reducing the heterogeneity of the mixed shale to some extent. A comparative analysis of the pore structure heterogeneity between mixed shale and calcareous/argillaceous shales revealed that argillaceous shale exhibits the strongest heterogeneity, mixed shale ranks second, and calcareous shale shows the weakest heterogeneity.
{"title":"Multi-scale pore structure heterogeneity characteristics and its controlling factors in hybrid shale reservoirs of the Shahejie Formation, Dongpu Depression","authors":"Lingxi Li , Yunlong Xu , Xiaoshui Mu , Zonyuan Yang , Debo Wang , Bo Yuan , Zhicheng Hu , Tianwu Xu , Xiang Cheng , Honglei Li , Dongdong Yang , Yaming Wang , Tao Hu","doi":"10.1016/j.uncres.2025.100280","DOIUrl":"10.1016/j.uncres.2025.100280","url":null,"abstract":"<div><div>Shale oil and gas represent a research focus in petroleum exploration and development. Shale reservoirs exhibit strong heterogeneity, and their microscopic pore structure serves as an important basis for evaluating hydrocarbon storage performance. Taking the shale of the Shahejie Formation in the Dongpu Depression as the primary research object, this study employed various analytical techniques, including whole-rock X-ray diffraction, total organic carbon measurement, argon ion polishing, field emission scanning electron microscopy, low-temperature nitrogen adsorption, and high-pressure mercury intrusion, to analyze the heterogeneity of mixed shale reservoirs and identify the controlling factors. The research results indicate: The shale in the Shahejie Formation of the study area primarily consists of carbonate and clay minerals, with three lithofacies types: calcareous shale, argillaceous shale, and mixed shale. The mixed shale mainly develops intercrystalline pores in clay minerals, bedding fractures in clay minerals, intercrystalline pores in pyrite, intercrystalline pores in calcite, intragranular dissolution pores, organic matter shrinkage fractures, and round or elliptical organic matter pores. The mixed shale in the study area primarily develops slit-shaped micropores and small pores, which exhibit good connectivity. The average fractal dimension of macropores is 2.9924, and that of mesopores is 2.9630, both higher than those of micropores and small pores (2.4290 and 2.6361, respectively). Larger pores exhibit more complex internal structures and stronger heterogeneity. The distribution of micropores and small pores is relatively concentrated, while mesopores and macropores are more widely distributed, enhancing the heterogeneity of the reservoir structure. In the study area, carbonate and clay minerals are the main factors influencing reservoir heterogeneity. The dehydration of clay minerals forms bedding fractures, while carbonate minerals are prone to dissolution, forming intragranular dissolution pores. Due to the abundance of carbonate minerals in the study area, dissolution pores serve as connections between various types of pores, thereby reducing the heterogeneity of the mixed shale to some extent. A comparative analysis of the pore structure heterogeneity between mixed shale and calcareous/argillaceous shales revealed that argillaceous shale exhibits the strongest heterogeneity, mixed shale ranks second, and calcareous shale shows the weakest heterogeneity.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100280"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571487","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: 2026-01-07DOI: 10.1016/j.uncres.2026.100307
Kusum Yadav , Lulwah M. Alkwai , Shahad Almansour , Debashis K. Dutta , Ahmad Adel Abu-Shareha , Mehrdad Mottaghi
Accurate permeability characterization is crucial for the efficient and sustainable development of geothermal resources. However, conventional methods like well testing and core analysis are often expensive and fail to capture the complex, heterogeneous nature of geothermal reservoirs. While Nuclear Magnetic Resonance (NMR) logging provides valuable insights into pore structure, its traditional permeability models are often unreliable in high-temperature, high-salinity geothermal environments. A novel data-driven methodology is introduced for modeling permeability in geothermal reservoirs by integrating Nuclear Magnetic Resonance (NMR) laboratory measurements with advanced machine learning algorithms. The approach employs a curated dataset of geothermal core samples, utilizing porosity, logarithmic mean transverse relaxation time (T2lm), and mode transverse relaxation time (T2mode) as predictive features across multiple learning models. Outlier detection was conducted using the Leverage technique, while model reliability was validated through K-fold cross-validation. Among the tested algorithms, the Decision Tree model demonstrated superior performance, yielding the highest coefficient of determination (R2) and the lowest error metrics. Sensitivity analysis further revealed porosity as the most dominant factor influencing geothermal permeability. The findings validate the utility of using ensemble soft computing to boost the accuracy of permeability prediction, presenting a valuable and affordable alternative to traditional techniques. Our findings bridge the gap between core analysis and computational modeling, paving the way for more accurate geothermal reservoir characterization and optimization.
{"title":"Modeling geothermal reservoirs permeability based upon NMR laboratory data","authors":"Kusum Yadav , Lulwah M. Alkwai , Shahad Almansour , Debashis K. Dutta , Ahmad Adel Abu-Shareha , Mehrdad Mottaghi","doi":"10.1016/j.uncres.2026.100307","DOIUrl":"10.1016/j.uncres.2026.100307","url":null,"abstract":"<div><div>Accurate permeability characterization is crucial for the efficient and sustainable development of geothermal resources. However, conventional methods like well testing and core analysis are often expensive and fail to capture the complex, heterogeneous nature of geothermal reservoirs. While Nuclear Magnetic Resonance (NMR) logging provides valuable insights into pore structure, its traditional permeability models are often unreliable in high-temperature, high-salinity geothermal environments. A novel data-driven methodology is introduced for modeling permeability in geothermal reservoirs by integrating Nuclear Magnetic Resonance (NMR) laboratory measurements with advanced machine learning algorithms. The approach employs a curated dataset of geothermal core samples, utilizing porosity, logarithmic mean transverse relaxation time (T2lm), and mode transverse relaxation time (T2mode) as predictive features across multiple learning models. Outlier detection was conducted using the Leverage technique, while model reliability was validated through K-fold cross-validation. Among the tested algorithms, the Decision Tree model demonstrated superior performance, yielding the highest coefficient of determination (R<sup>2</sup>) and the lowest error metrics. Sensitivity analysis further revealed porosity as the most dominant factor influencing geothermal permeability. The findings validate the utility of using ensemble soft computing to boost the accuracy of permeability prediction, presenting a valuable and affordable alternative to traditional techniques. Our findings bridge the gap between core analysis and computational modeling, paving the way for more accurate geothermal reservoir characterization and optimization.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100307"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924565","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: 2025-11-14DOI: 10.1016/j.uncres.2025.100281
Mina S. Khalaf
Geothermal energy plays a critical role in the transition to low-carbon energy systems, offering a stable and renewable source with minimal environmental impact. This study develops a comprehensive thermo-hydro-mechanical framework to investigate applications in geothermal systems. The applications include thermally induced fractures, geothermal energy extraction, and natural fracture activation under different reservoir conditions, to understand reservoir behavior and guide stimulation planning. The thermo-poroelastic theory is integrated to simulate coupled mechanical, hydraulic, and thermal processes in fractured geothermal reservoirs. The governing equations are implemented using the displacement discontinuity method, where fracture boundaries are discretized to evaluate stress, pressure, and temperature fields induced by displacement, fluid, and thermal sources. In addition, natural fracture deformation is modeled using the nonlinear Barton-Bandis relationship to account for stress-dependent closure and shear. Moreover, fracture propagation is governed by mixed-mode stress intensity criteria. Fluid flow within fractures follows Poiseuille's law, while thermal transport within fractures accounts for conduction, convection, and formation heat exchange. The coupled solution advances in time by iteratively solving for displacement, pressure, and temperature, ensuring full coupling across mechanical, hydraulic, and thermal domains. This framework integrates multiple modeling capacities that have previously been treated separately, enabling a comprehensive simulation of geothermal stimulation. The model was validated by comparing its numerical predictions with analytical solutions for thermo-poroelastic fracture responses under various loading conditions.
The results show that injecting fluid 200 °C colder increased the maximum fracture width from approximately 0.7 mm to approximately 0.9 mm and reduced the time needed to reach a 32 m fracture length by about 1.75 × . Over five years, a single 200 m fracture sustained 3.9 × 104 J/s and yielded 6.2 × 1012 J, whereas a 50 m fracture produced 1.6 × 104 J/s and 3.1 × 1012 J. Increasing rock thermal conductivity from 2 to 15 W/m·°C raised cumulative recovery from 2.2 × 1012 J to 5.3 × 1012 J and maintained production temperature near 125 °C after five years. In natural-fracture activation tests, final widths reached 2.25 mm in tight formations, while high-permeability cases showed minimal change (0.1 mm). This study helps operators design more efficient and cost-effective EGS projects by optimizing injection strategies, fracture geometry, and site selection. In addition, it offers actionable guidance to improve heat recovery, reduce stimulation volumes, and manage risks like fluid loss and induced seismicity.
{"title":"A comprehensive thermo-hydro-mechanical framework for enhanced geothermal systems: thermal stimulation, energy recovery, and natural fracture activation","authors":"Mina S. Khalaf","doi":"10.1016/j.uncres.2025.100281","DOIUrl":"10.1016/j.uncres.2025.100281","url":null,"abstract":"<div><div>Geothermal energy plays a critical role in the transition to low-carbon energy systems, offering a stable and renewable source with minimal environmental impact. This study develops a comprehensive thermo-hydro-mechanical framework to investigate applications in geothermal systems. The applications include thermally induced fractures, geothermal energy extraction, and natural fracture activation under different reservoir conditions, to understand reservoir behavior and guide stimulation planning. The thermo-poroelastic theory is integrated to simulate coupled mechanical, hydraulic, and thermal processes in fractured geothermal reservoirs. The governing equations are implemented using the displacement discontinuity method, where fracture boundaries are discretized to evaluate stress, pressure, and temperature fields induced by displacement, fluid, and thermal sources. In addition, natural fracture deformation is modeled using the nonlinear Barton-Bandis relationship to account for stress-dependent closure and shear. Moreover, fracture propagation is governed by mixed-mode stress intensity criteria. Fluid flow within fractures follows Poiseuille's law, while thermal transport within fractures accounts for conduction, convection, and formation heat exchange. The coupled solution advances in time by iteratively solving for displacement, pressure, and temperature, ensuring full coupling across mechanical, hydraulic, and thermal domains. This framework integrates multiple modeling capacities that have previously been treated separately, enabling a comprehensive simulation of geothermal stimulation. The model was validated by comparing its numerical predictions with analytical solutions for thermo-poroelastic fracture responses under various loading conditions.</div><div>The results show that injecting fluid 200 °C colder increased the maximum fracture width from approximately 0.7 mm to approximately 0.9 mm and reduced the time needed to reach a 32 m fracture length by about 1.75 × . Over five years, a single 200 m fracture sustained 3.9 × 10<sup>4</sup> J/s and yielded 6.2 × 10<sup>12</sup> J, whereas a 50 m fracture produced 1.6 × 10<sup>4</sup> J/s and 3.1 × 10<sup>12</sup> J. Increasing rock thermal conductivity from 2 to 15 W/m·°C raised cumulative recovery from 2.2 × 10<sup>12</sup> J to 5.3 × 10<sup>12</sup> J and maintained production temperature near 125 °C after five years. In natural-fracture activation tests, final widths reached 2.25 mm in tight formations, while high-permeability cases showed minimal change (0.1 mm). This study helps operators design more efficient and cost-effective EGS projects by optimizing injection strategies, fracture geometry, and site selection. In addition, it offers actionable guidance to improve heat recovery, reduce stimulation volumes, and manage risks like fluid loss and induced seismicity.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100281"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571415","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: 2025-11-12DOI: 10.1016/j.uncres.2025.100277
Lulwah M. Alkwai , Shahad Almansour , Kusum Yadav , Debashis Dutta , Samim Sherzod
This study introduces a data-driven framework for predicting irreducible water saturation (Swi) in reservoir rocks, addressing the limitations of traditional empirical models and capturing the complexity of heterogeneous formations. Using a comprehensive experimental dataset including porosity, grain density, permeability, and T2lm, eight machine learning algorithms were trained and evaluated. Among them, the Convolutional Neural Network (CNN) demonstrated the highest predictive accuracy, achieving superior R2 and error metrics. SHAP-based sensitivity analysis identified permeability as the dominant feature, reinforcing the model's physical relevance. By integrating core-scale measurements with advanced computational techniques, the proposed methodology offers a scalable and validated solution for Swi estimation, supporting more accurate reservoir characterization and informed hydrocarbon reserve management.
{"title":"Refining irreducible water saturation predictions in reservoir rocks using machine learning models","authors":"Lulwah M. Alkwai , Shahad Almansour , Kusum Yadav , Debashis Dutta , Samim Sherzod","doi":"10.1016/j.uncres.2025.100277","DOIUrl":"10.1016/j.uncres.2025.100277","url":null,"abstract":"<div><div>This study introduces a data-driven framework for predicting irreducible water saturation (Swi) in reservoir rocks, addressing the limitations of traditional empirical models and capturing the complexity of heterogeneous formations. Using a comprehensive experimental dataset including porosity, grain density, permeability, and T2lm, eight machine learning algorithms were trained and evaluated. Among them, the Convolutional Neural Network (CNN) demonstrated the highest predictive accuracy, achieving superior R<sup>2</sup> and error metrics. SHAP-based sensitivity analysis identified permeability as the dominant feature, reinforcing the model's physical relevance. By integrating core-scale measurements with advanced computational techniques, the proposed methodology offers a scalable and validated solution for Swi estimation, supporting more accurate reservoir characterization and informed hydrocarbon reserve management.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100277"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520599","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: 2025-11-04DOI: 10.1016/j.uncres.2025.100274
Mohamed Khaleel , Ziyodulla Yusupov
Achieving the 1.5 °C global temperature target and reaching net-zero emissions by 2050 require a fundamental transformation of energy systems, driven by the rapid deployment of renewable energy technologies and underpinned by systemic policy, financial, and infrastructural reform. The manuscript adopts a literature-driven approach, synthesizing findings from existing scholarly sources that shape the transition to sustainable energy systems. It begins by outlining global progress toward climate targets, emphasizing the critical role of renewable energy in decarbonizing electricity, industry, and transport sectors. The manuscript explores recent technological advancements and trends in solar, wind, hydrogen, and emerging clean technologies, highlighting their impact on global energy supply chains and production models. Particular attention is given to the complexities of integrating renewable energy into existing infrastructure, including grid modernization, digitaliation, and storage technologies. On the demand side, the article examines changing consumption patterns, electrification, and the role of distributed generation in shaping future energy landscapes. Investment and finance emerge as central challenges, with the paper analyzing the disparities in capital costs between developed and developing economies, and the need for innovative green finance instruments to de-risk investment. The manuscript further identifies structural barriers, including policy uncertainty, supply chain constraints, and permitting delays, as key impediments to progress. Nonetheless, the article outlines significant opportunities for scaling up renewable deployment through international cooperation, targeted subsidies, and public-private partnerships. The manuscript concludes by emphasizing the necessity of coherent and enforceable policy frameworks to align national commitments with global climate goals. It calls for an integrated, multi-stakeholder approach to ensure that finance, infrastructure, demand, and governance evolve in tandem, thereby enabling a just, inclusive, and resilient global energy transition.
{"title":"Advancing sustainable energy transitions: Insights on finance, policy, infrastructure, and demand-side integration","authors":"Mohamed Khaleel , Ziyodulla Yusupov","doi":"10.1016/j.uncres.2025.100274","DOIUrl":"10.1016/j.uncres.2025.100274","url":null,"abstract":"<div><div>Achieving the 1.5 °C global temperature target and reaching net-zero emissions by 2050 require a fundamental transformation of energy systems, driven by the rapid deployment of renewable energy technologies and underpinned by systemic policy, financial, and infrastructural reform. The manuscript adopts a literature-driven approach, synthesizing findings from existing scholarly sources that shape the transition to sustainable energy systems. It begins by outlining global progress toward climate targets, emphasizing the critical role of renewable energy in decarbonizing electricity, industry, and transport sectors. The manuscript explores recent technological advancements and trends in solar, wind, hydrogen, and emerging clean technologies, highlighting their impact on global energy supply chains and production models. Particular attention is given to the complexities of integrating renewable energy into existing infrastructure, including grid modernization, digitaliation, and storage technologies. On the demand side, the article examines changing consumption patterns, electrification, and the role of distributed generation in shaping future energy landscapes. Investment and finance emerge as central challenges, with the paper analyzing the disparities in capital costs between developed and developing economies, and the need for innovative green finance instruments to de-risk investment. The manuscript further identifies structural barriers, including policy uncertainty, supply chain constraints, and permitting delays, as key impediments to progress. Nonetheless, the article outlines significant opportunities for scaling up renewable deployment through international cooperation, targeted subsidies, and public-private partnerships. The manuscript concludes by emphasizing the necessity of coherent and enforceable policy frameworks to align national commitments with global climate goals. It calls for an integrated, multi-stakeholder approach to ensure that finance, infrastructure, demand, and governance evolve in tandem, thereby enabling a just, inclusive, and resilient global energy transition.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100274"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467402","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}
Precise estimation of CO2 diffusivity in hydrocarbon mixtures is fundamental for designing effective injection schemes, controlling displacement efficiency, and optimizing the overall performance of CO2-enhanced oil recovery processes. This study introduces a novel approach, integrating genetic programming with explainable AI, to accurately predict CO2 diffusivity in viscous hydrocarbon systems. Utilizing an experimental dataset of 260 samples from the literature, explicit data-driven predictive correlations were developed using the well-established genetic programming (GP) technique. The results confirmed the strong performance of the developed GP-based frameworks, with a coefficient of determination of 0.995 and a root mean square error of 0.17 × 10-10 m/s2. Compared to some existing correlations, this approach offers improved accuracy and reduced computational demands. Data quality was confirmed via leverage analysis, and model reliability was established. Shapley plot-based sensitivity analysis revealed pressure as the primary influence on carbon dioxide diffusivity, followed by temperature and carbon dioxide mass fraction, with fluid density having minimal impact. The model's explicit formulation facilitates real-world deployment. Furthermore, Shapley additive explanations enhance interpretability and validate feature importance, making the model user-friendly for carbon dioxide injection applications. Physical validation via trend analysis confirms the paradigm's ability to maintain physical relationships related to independent variable variations. The implemented model can contribute to the improved management and decision-making processes for CO2-based enhanced oil recovery (EOR) projects, which have the potential to enhance oil extraction and facilitate carbon dioxide capture and storage.
{"title":"Improved explicit data-driven frameworks for accurate prediction of carbon dioxide diffusivity in high viscosity hydrocarbon systems","authors":"Saad Alatefi , Okorie Ekwe Agwu , Hakim Djema , Menad Nait Amar","doi":"10.1016/j.uncres.2025.100270","DOIUrl":"10.1016/j.uncres.2025.100270","url":null,"abstract":"<div><div>Precise estimation of CO<sub>2</sub> diffusivity in hydrocarbon mixtures is fundamental for designing effective injection schemes, controlling displacement efficiency, and optimizing the overall performance of CO<sub>2</sub>-enhanced oil recovery processes. This study introduces a novel approach, integrating genetic programming with explainable AI, to accurately predict CO<sub>2</sub> diffusivity in viscous hydrocarbon systems. Utilizing an experimental dataset of 260 samples from the literature, explicit data-driven predictive correlations were developed using the well-established genetic programming (GP) technique. The results confirmed the strong performance of the developed GP-based frameworks, with a coefficient of determination of 0.995 and a root mean square error of 0.17 × 10-10 m/s<sup>2</sup>. Compared to some existing correlations, this approach offers improved accuracy and reduced computational demands. Data quality was confirmed via leverage analysis, and model reliability was established. Shapley plot-based sensitivity analysis revealed pressure as the primary influence on carbon dioxide diffusivity, followed by temperature and carbon dioxide mass fraction, with fluid density having minimal impact. The model's explicit formulation facilitates real-world deployment. Furthermore, Shapley additive explanations enhance interpretability and validate feature importance, making the model user-friendly for carbon dioxide injection applications. Physical validation via trend analysis confirms the paradigm's ability to maintain physical relationships related to independent variable variations. The implemented model can contribute to the improved management and decision-making processes for CO<sub>2</sub>-based enhanced oil recovery (EOR) projects, which have the potential to enhance oil extraction and facilitate carbon dioxide capture and storage.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"9 ","pages":"Article 100270"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419126","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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