Open AccessEditorial New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition College of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China Energies 2026, 19(12), 2762; https://doi.org/10.3390/en19122762 (registering DOI) Submission received: 22 May 2026 / Accepted: 5 June 2026 / Published: 9 June 2026 With the gradual depletion of global fossil resources, optimizing the development of mature oilfields and improving the efficiency of unconventional oil and gas reservoirs have become key priorities for the oil and gas industry [ 1, 2, 3]. Traditional oilfields are facing declining recovery rates and production capacity [ 4, 5]. Unconventional reservoirs, such as tight oil, shale gas, and heavy oil, require the development of more advanced technologies [ 6, 7]. To enhance overall recovery efficiency, various Improved Oil Recovery (IOR) and production optimization strategies have been proposed. These include thermal recovery, chemical flooding, microbial flooding, gas flooding, and nano-flooding technologies [ 8, 9, 10, 11, 12]. Simultaneously, the integration of detailed geological characterization and digital reservoir engineering provides new approaches for predicting production performance in complex geological bodies and designing comprehensive development plans. This enables the maximization of oil and gas resource development [ 13]. Natural gas and hydrogen, as clean and efficient energy sources, are playing an increasingly important role in the global energy transition [ 21]. Achieving stable storage and transportation, as well as secure geological storage, has become a key area of research and engineering practice [ 22, 23, 24]. Underground gas storage, salt cavern storage, and Liquefied Natural Gas (LNG) peak-shaving systems provide effective means to ensure seasonal supply–demand balance. Concurrently, advances in CO 2 and H 2 geological storage technologies are promoting the synergistic development of gas resource management and carbon emission reduction strategies [ 25, 26]. Innovations in areas such as high-temperature and high-pressure phase behavior studies, leakage monitoring, and geological safety assessment help ensure the long-term reliability of storage systems. With the continuous improvement of numerical simulation, sensor monitoring, and risk analysis methods, natural gas and hydrogen storage, transportation, and sequestration technologies are rapidly evolving towards greater efficiency, safety, and sustainability [ 27, 28]. This collection aligns with the theme of the Special Issue “New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition” in Energies and emphasizes foundational innovation. This edition comprises eight new research papers that highlight the original application of new concepts and methodological studies expected to drive new advances in the field of oil, gas, and geothermal reservoirs. These papers primarily revolve around three research directions: first, the development and optimization of mature and unconventional reservoirs; second, the application of big data and artificial intelligence in oil and gas field development; and third, natural gas storage, transportation, and sequestration technologies. This editorial provides an overview of the main findings and contributions of each paper, organized according to their respective themes. The first category focuses on the development and recovery optimization of mature and unconventional oil and gas reservoirs. Waterflooding efficiency and injection–production management in complex reservoirs remain key research topics. Jiang et al. [ 29] proposed a zoned water-coning calculation method using a multi-mode flow model for fault-controlled fractured-vuggy reservoirs. By distinguishing high-permeability core zones, low-speed damaged zones, and transition zones, this method accurately describes differences in oil–water distribution and water-coning behavior, providing a theoretical basis for staged completions and targeted water control. Yuan et al. [ 30] studied waterflood sweep efficiency prediction in deep-water turbidite channel reservoirs. Injection–production well groups are classified according to connectivity patterns into contemporaneous, cross-segment, and hybrid types. Using time-series seismic data analysis, a production data-based prediction model is established, enabling quantitative evaluation of waterflood efficiency under different connectivity patterns. Regarding heavy-oil thermal recovery, Wang et al. [ 31] systematically investigated the low-temperature oxidation behavior and non-isothermal exothermic patterns of heavy oil under oxygen-reduced air injection. The influence of oxygen partial pressure on low-temperature oxidation and subsequent high-temperature reactions is revealed. The improvement in pore connectivity due to microstructure evolution of rock cuttings is also analyzed. Cai et al. [ 32] focused on oil–water emulsification mechanisms during steam flooding. The formation of oil–water emulsions under high-temperature and high-salinity conditions and their impact on recovery are clarified, providing practical guidance for early chemical injection, oil–water film disruption, and phase inversion promotion in steam flooding. An et al. [ 33] studied the time-varying characteristics of reservoir properties through long-term waterflooding experiments. Different evolution features of pore-throat networks and clay migration in high- and low-permeability cores during water injection are revealed, offering a theoretical foundation for optimizing long-term waterflooding schemes. The second category focuses on the application of big data and artificial intelligence in oil and gas field development. Big data and AI technologies are increasingly playing a central role in production optimization and safety management. Li et al. [ 34] investigated gas transmission capacity allocation using the open access model of natural gas pipelines. A multi-dimensional quantitative evaluation model based on the analytic hierarchy process is constructed, enabling comprehensive assessment and decision support for different schemes. A program that automatically generates scheme scores is developed, providing a data-driven tool for pipeline operation and regulation. This research demonstrates that real-time data analysis of production and transportation systems can significantly improve resource management efficiency and operational decision-making. The third category focuses on natural gas storage, transportation, and sequestration technologies. Water control and sequestration technologies for high-temperature, high-pressure gas reservoirs and complex pore structures are continuously advancing. Song et al. [ 35] reported a successful case of polymer gel water control in a high-temperature gas reservoir. Through large-volume gel placement in structurally low wells combined with excessive water drainage, effective protection of high-structural gas-producing wells is achieved, verifying the feasibility of non-selective gels for water control in complex gas reservoirs. Additionally, Chen et al. [ 36] proposed a semi-analytical Buckley–Leverett model for fractured-vuggy composite reservoirs. By combining Navier–Stokes and Darcy flow descriptions, a theoretical framework and numerical simulation reference are provided for waterflood optimization in complex pore–cavity–pore systems. In summary, this Special Issue, “New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition”, focuses on new advances in the development optimization of complex and unconventional reservoirs, application of data and AI in oil and gas production management, and technologies for natural gas storage, transportation, and sequestration. The papers in this collection not only showcase recovery optimization strategies across high/low-permeability, deep-water, and heavy-oil reservoirs, but also reflect how modern intelligent and simulation technologies assist oil and gas field management. They provide scientific references and technical guidance for the efficient, safe, and sustainable development of future energy resources. Conflicts of Interest The author declares no conflict of interest. Gao, Z.-H.; Wang, D.-D.; Liu, J.-F.; Hao, S.-J.; Zhang, Q. Planning and operation optimization for oilfield negative carbon integrated energy system considering source-load interaction. Pet. Sci. 2026; in press. [ CrossRef] Abdulhadi, D.; Ali, J.A.; Hama, S.M. Advanced techniques for improving the production of natural resources from unconventional reservoirs: A state-of-the-art review. Energy Fuels 2025, 39, 10853–10876. [ Google Scholar] [ CrossRef] Li, Z.; Zhou, B.; Lu, Y.; Yang, H.; Jiang, H.; Kang, W.; Xing, Y.; Gui, X. Advances of self-assembly behaviors in polymer systems for improved oil recovery (IOR) in unconventional reservoirs. Adv. Colloid Interface Sci. 2025, 345, 103622. [ Google Scholar] [ CrossRef] Zhu, D.; Bai, B.; Hou, J. Polymer gel systems for water management in high-temperature petroleum reservoirs: A chemical review. Energy Fuels 2017, 31, 13063–13087. [ Google Scholar] [ CrossRef] Gao, Y.-Q.; Cheng, H.-B.; Li, G.-H.; Li, H.-Y.; Tan, H.-G.; Zhang, J.; Zhu, D.-Y. Gelation and reservoir conformance control performance of in situ crosslinked polymer gels prepared with different crosslinkers. Pet. Sci. 2026, 23, 2075–2085. [ Google Scholar] [ CrossRef] Li, P.; Shen, B.-J.; Liu, Y.-L.; Bi, H.; Liu, Z.-B.; Bian, R.-K.; Wang, P.-W.; Li, P. The fractal characteristics of the pore throat structure of tight sandstone and its influence on oil content: A case study of the Chang 7 Member of the Ordos Basin, China. Pet. Sci. 2025, 22, 2262–2273. [ Google Scholar] [ CrossRef] Zhu, C.-F.; Zhang, T.-L.; Pan, J.-F.; Li, Y.-W.; Sheng, J.J.; Ge, D.; Jia, R.; Guo, W. Evolution of the 3D pore structure of organic-rich shale with temperature based on micro-nano CT. Pet. Sci. 2025, 22, 2339–2352. [ Google Scholar] [ CrossRef] Zhu, Y.-J.; Wan, Y.-Y.; Tian, Y.; Mu, H.-M.; Zhang, T.-G. Biomass thermal decomposition induced hydrogen sulfide blooming in thermal recovery reservoirs. Pet. Sci. 2025, 22, 1802–1810. [ Google Scholar] [ CrossRef] Guo, S.; Zhang, J.; Gao, Y.-Q.; Tan, H.-G.; Cheng, H.-B.; Li, H.-Y.; Zhu, D.-Y. Imbibition mechanism analysis of modified black nanosheet and low salinity water composite system for enhanced oil recovery by NMR method. Pet. Sci. 2025, 22, 5166–5175. [ Google Scholar] [ CrossRef] Shahzar, M.; Sharma, S.; Saxena, A.; Chowdhury, S. Performance Assessment of Graphene Oxide Nanosheets for Enhancing Oilwell Cement Integrity Under High-Pressure High-Temperature Conditions. SPE J. 2025, 30, 1867–1883. [ Google Scholar] [ CrossRef] Tao, Z.; Ji, B.; Sarsenbekuly, B.; Kang, W.; Yang, H.; Wu, W.; Tian, Y.; Turtabayev, S.; Ismailova, J.; Beisenbayeva, A. A Review of Nanomaterials in Heavy-Oil Viscosity Reduction: The Transition from Thermal Recovery to Cold Recovery. Nanomaterials 2026, 16, 452. [ Google Scholar] [ CrossRef] Tao, Z.; Sarsenbekuly, B.; Kang, W.; Wu, W.; Zhang, G.; Yang, H.; Tian, Y.; Wang, Y.; Guo, S.; Liu, D. Synthesis and Evaluation of Novel Graphene Oxide-Based Comb-Polymer Viscosity Reducer for Heavy Oil. Energy Fuels 2026, 40, 2489–2500. [ Google Scholar] [ CrossRef] Rajput, S.; Pathak, R.K. Reservoir Engineering Excellence: A Geoscientist’s Blueprint. In Seismic Exploration to Reservoir Excellence; Springer: Berlin/Heidelberg, Germany, 2025; pp. 461–505. [ Google Scholar] Mkono, C.N.; Chuanbo, S.; Mulashani, A.K.; Abelly, E.N.; Kasala, E.E.; Shanghvi, E.R.; Emmanuely, B.L.; Mokobodi, T. Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence. SPE J. 2025, 30, 1922–1940. [ Google Scholar] [ CrossRef] Muhammad, R.B.; Cheraghi, Y.; Alyaev, S.; Srivastava, A.; Bratvold, R.B. Geosteering robot powered by multiple probabilistic interpretation and artificial intelligence: Benchmarking against human experts. SPE J. 2025, 30, 995–1009. [ Google Scholar] [ CrossRef] Hui, G.; Ren, Y.; Bi, J.; Wang, M.; Liu, C. Artificial intelligence applications and challenges in oil and gas exploration and development. Adv. Geo-Energy Res. 2025, 17, 179–183. [ Google Scholar] [ CrossRef] Aslam, B.; Zhang, Y.; Hoteit, I.; Yan, B. A Deep Neural Network–Based History-Matching Method for Deterministic Estimation of Heterogeneous Model Parameters. SPE J. 2025, 30, 4448–4468. [ Google Scholar] [ CrossRef] Ahn, S.; Choe, J. Applications of geological features style mixing for reservoir history matching. SPE J. 2025, 30, 1651–1669. [ Google Scholar] [ CrossRef] Yasutra, A. Two Decades of Smart Field Evolution (2005–2025): Global Insights and Indonesian Perspectives. Sci. Contrib. Oil Gas 2025, 48, 271–279. [ Google Scholar] [ CrossRef] Li, S.; Wei, Y.; Zuo, Y.; Yang, W. Nanomaterial-based sand control technology and smart oilfields: Principles, integration, and future prospects. Adv. Resour. Res. 2025, 5, 1299–1317. [ Google Scholar] Ouyang, Z.; Jackson, R.B.; Saunois, M.; Canadell, J.G.; Zhao, Y.; Morfopoulos, C.; Krummel, P.B.; Patra, P.K.; Peters, G.P.; Dennison, F.; et al. The global hydrogen budget. Nature 2025, 648, 616–624. [ Google Scholar] [ CrossRef] Zhu, D.; Yang, Y.; Tan, H.; Guo, S.; Lu, J.; Zhang, T.; Zhang, J.; Gao, Y.; Li, H.; Cheng, H. CO 2 Conformance Control and Carbon Storage Performance of Particle Gels in Fractured Reservoirs Using Nuclear Magnetic Resonance Methods. SPE J. 2026, 31, 588–601. [ Google Scholar] [ CrossRef] Zhu, D.; Zhao, Q.; Chen, P.; Lu, J.; Yang, Y.; Guo, S.; Zhang, T. Laboratory evaluation of antileakage performance against CO 2 of alkali-activated gel-reinforced cement for carbon capture, utilization, and storage. SPE J. 2025, 30, 3776–3791. [ Google Scholar] [ CrossRef] Ali, M.; Isah, A.; Yekeen, N.; Hassanpouryouzband, A.; Sarmadivaleh, M.; Okoroafor, E.R.; Al Kobaisi, M.; Mahmoud, M.; Vahrenkamp, V.; Hoteit, H. Recent progress in underground hydrogen storage. Energy Environ. Sci. 2025, 18, 5740–5810. [ Google Scholar] [ CrossRef] Zhu, D.; Shi, C.; Guo, S.; Yang, Y.; Lu, J.; Zhang, T.; Zhang, J.; Gao, Y.; Tan, H.; Cheng, H.; et al. Black nanosheet-reinforced CO 2 foam for conformance control in CO 2-enhanced oil recovery and storage process. Geoenergy Sci. Eng. 2026, 263, 214509. [ Google Scholar] [ CrossRef] Yang, H.; Peng, L.; Wang, R.; Zhang, L.; Xu, H.; Kadirov, A.; Kang, W.; Sarsenbekuly, B. Development and performance evaluation of acid-resistant preformed particle gel for anti-CO2 channeling in high-temperature reservoirs. Fuel 2026, 425, 139457. [ Google Scholar] [ CrossRef] Sun, H.; Wang, Z.; Meng, Q.; White, S. Advancements in hydrogen storage technologies: Enhancing efficiency, safety, and economic viability for sustainable energy transition. Int. J. Hydrogen Energy 2025, 105, 10–22. [ Google Scholar] [ CrossRef] Sun, B.; Zhang, M.; Sun, Q.; Zhong, J.; Shao, G. Review on natural hydrogen wells safety. Nat. Commun. 2025, 16, 369. [ Google Scholar] [ CrossRef] Jiang, X.; Zhao, X.; Huang, Z.; Yan, T.; Xiao, C.; Wei, G.; He, Y. Water Coning Calculation and Application Analysis for Fault-Controlled Fractured–Vuggy Reservoirs Based on a Multi-Modal Flow Model. Energies 2026, 19, 1780. [ Google Scholar] [ CrossRef] Yuan, Z.; Yang, L.; Liu, X.; Li, Y. A Method for Predicting the Waterflood Sweep Efficiency in Deepwater Turbidite Channel Oil Reservoirs. Energies 2026, 19, 1605. [ Google Scholar] [ CrossRef] Wang, W.; Chen, D.; Pan, Z.; He, J.; Shen, J.; Liu, M.; Li, Y.; Lan, M.; Zhao, S. Low-Temperature Oxidation Behavior and Non-Isothermal Heat Release of Heavy Oil During Oxygen-Reduced Air Injection. Energies 2025, 19, 225. [ Google Scholar] [ CrossRef] Cai, H.; Qi, Z.; Liu, Y.; Liu, D.; Du, C.; Tian, J.; Yan, W.; Luo, T. An Experimental Study on Oil–Water Emulsification Mechanism During Steam Injection Process in Heavy Oil Thermal Recovery. Energies 2025, 18, 6250. [ Google Scholar] [ CrossRef] An, X.; Zhu, Y.; Tan, X.; Bi, J.; Tan, C. Study on Time-Varying Mechanism of Reservoir Properties During Long-Term Water Flooding. Energies 2025, 18, 6488. [ Google Scholar] [ CrossRef] Li, X.; Wang, D.; Shi, Y.; Jia, J.; Wang, Z. Research on the Evaluation Model for Natural Gas Pipeline Capacity Allocation Under Fair and Open Access Mode. Energies 2025, 18, 5544. [ Google Scholar] [ CrossRef] Song, T.; Wu, H.; Liu, P.; Wu, J.; Wang, C.; Zhang, H.; Zhang, S.; Li, M.; Wang, J.; Ding, B.; et al. Water Shutoff with Polymer Gels in a High-Temperature Gas Reservoir in China: A Success Story. Energies 2025, 18, 6554. [ Google Scholar] [ CrossRef] Chen, F.-F.; Jiang, X.-J.; Yan, T.; Ma, X.-P.; Zhang, Z.-Y.; Li, M.-J.; Huang, Z.-Q. Buckley–Leverett Solution for Two-Phase Displacement in a Composite Porous-Cavernous-Porous System. Energies 2026, 19, 2463. [ Google Scholar] [ CrossRef] © 2026 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Share and Cite MDPI and ACS Style Zhu, D. New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition. Energies 2026, 19, 2762. https://doi.org/10.3390/en19122762 AMA Style Zhu D. New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition. Energies. 2026; 19(12):2762. https://doi.org/10.3390/en19122762 Chicago/Turabian Style Zhu, Daoyi. 2026. "New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition" Energies 19, no. 12: 2762. https://doi.org/10.3390/en19122762 APA Style Zhu, D. (2026). New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition. Energies, 19(12), 2762. https://doi.org/10.3390/en19122762 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details . Article Metrics Article metric data becomes available approximately 24 hours after publication online.
