New Advances in Oil, Gas and Geothermal Reservoirs: 2nd Edition

Oil, gas, and geothermal resources, including conventional fossil fuels (oil and natural gas) and unconventional resources (geothermal, shale gas, and tight oil), are key to meeting global energy demands [1,2]. These resources are divided into extractive (e.g., hydrocarbons) and renewable (e.g., geothermal) sources, both essential for sustaining energy systems and supporting environmental sustainability [3,4].

The exploitation of unconventional and depleted reservoirs—particularly heavy oil formations and tight reservoirs—represents a critical frontier in modern hydrocarbon production [5,6]. Improved oil recovery (IOR) techniques, such as thermal stimulation for viscous crude and advanced conformance control (e.g., water shutoff and profile modification), are essential to maximize extraction from challenging reserves with minimal energy expenditure [7,8,9,10,11]. Innovations like steam-assisted gravity drainage (SAGD) and cost-effective hydraulic fracturing have significantly improved the economic viability of unconventional plays [12,13,14,15,16]. As reservoirs age, optimizing waterflood management, reservoir characterization, and recovery rates remains a focal point of research [17,18,19,20].

The integration of big data analytics and artificial intelligence (AI) is transforming upstream operations [21,22]. Machine learning-based predictive models and real-time surveillance systems enable production optimization, hazard mitigation, and performance enhancement, while concurrently addressing challenges in reservoir stewardship and cost optimization. AI-powered decision-support tools further facilitate operational efficiency gains and lifecycle cost reductions [23,24].

Gas storage and sequestration technologies are crucial for resource management and reducing environmental impacts [25,26]. Storing natural gas and injecting CO₂ underground helps cut greenhouse gas emissions and mitigate climate change. Advanced methods for CO₂ or H₂ sequestration in depleted oil and gas fields and saline aquifers are key to meeting climate goals [27]. These technologies are evolving rapidly, with innovations in modeling, monitoring, and safety assurance.

This collection, which is in conversation with the Special Issue of Energies, emphasizes fundamental innovations and has compiled eight new publications on the original application of new ideas and on methodologies that will lead to new advances in oil, gas, and geothermal reservoirs.

The papers are organized into three major directions: development of mature and unconventional reservoirs, big data and artificial intelligence in oil and gas fields, and gas storage and sequestration technologies. Below, we provide an overview of the core findings from each paper, organized by these thematic categories.

The first category focuses on the challenges and innovations associated with improving recovery in both conventional and unconventional oil reservoirs, including heavy oil fields and water-flooded mature fields. Yu et al. [28] investigate the microscopic flow channels in ultra-high water-cut reservoirs of the Shengli Oilfield. Their study visualizes the evolution of water flooding in reservoirs, highlighting how the dominant flow channels and residual oil distribution evolve as the water cut increases. Their research indicates that strategic adjustments in liquid extraction and flow direction can enhance oil recovery, especially in the later stages of ultra-high water-cut development, achieving an impressive recovery rate of 68.02%. This study offers valuable insights for optimizing water flooding practices in mature oilfields.

In recent years, the development of heavy oil reservoirs has gained significant attention. Tian et al. [29] investigated the changes in the thermal and physical properties of reservoir rock surfaces during the pre-heating phase of SAGD. Fu et al. [30] investigate supercritical multicomponent thermal fluids (scMCTF) for offshore heavy oil recovery. Their research reveals that the composition of thermal fluids, especially the ratio of water to organic matter, significantly impacts the recovery efficiency. By exploring reaction conditions, they find that crude oil can be effectively used instead of diesel to generate supercritical fluids, with notable economic and technical advantages. This opens new possibilities for improving thermal recovery in offshore heavy oil reservoirs. Yang et al. [31] build upon the previous study with a focus on optimizing the composition and injection rate of scMCTF for offshore heavy oil recovery. Through molecular simulation, they identify how factors such as temperature, pressure, and organic matter concentration influence the yield and composition of the thermal fluids. They propose a model for controlling the injection rate and fluid composition, providing an effective approach to enhancing the thermal recovery of heavy oil. Zhang et al. [32] examine the influence of reaction conditions on the yield of scMCTF. Their findings suggest that temperature, reaction time, and catalyst concentration have a positive correlation with product yield, while the raw material concentration negatively affects the production rate. This study contributes to the understanding of how various factors influence the efficiency of supercritical fluid generation in oil recovery.

The second category emphasizes the integration of big data and artificial intelligence (AI) technologies to optimize production in oil and gas fields, particularly through predictive modeling and fault detection. Zhang et al. [33] developed a data-driven natural gas production prediction model for volcanic reservoirs. By considering multiple factors such as formation pressure, effective reservoir thickness, and gas well production data, they establish a predictive model with high accuracy (R² = 0.99). This model provides valuable tools for optimizing gas production in volcanic reservoirs, enabling more efficient resource management. Zhang et al. [34] also contribute to AI applications in oil production by developing a hybrid AI model for fault prediction in rod pumping systems. Using deep learning algorithms, their model achieves a remarkable prediction accuracy of 98.61%, significantly improving the reliability and safety of rod pumping operations in Xinjiang Oilfield. The study underscores the potential of AI in enhancing production efficiency and reducing downtime due to mechanical failures.

The third category focuses on advancements in gas storage, particularly for the purposes of sequestration, which is crucial for reducing greenhouse gas emissions. Chen et al. [35] provide insights into the design and performance of a liquid helium Dewar system for thermal insulation. While not directly related to gas sequestration, the study’s findings on thermal efficiency and heat leakage in cryogenic storage systems could have implications for gas storage technologies, especially in the context of developing more efficient storage solutions for gases like CO₂.

This second edition of New Advances in Oil, Gas and Geothermal Reservoirs highlights key advancements in the development of mature and unconventional (especially heavy oil) reservoirs, the application of big data and artificial intelligence in oil and gas fields, and innovative gas storage and sequestration technologies. The contributions presented in this issue offer new insights into enhancing recovery from challenging reservoirs, optimizing field operations through data-driven approaches, and advancing gas storage methods. These breakthroughs provide valuable knowledge to support the efficient and sustainable management of energy resources, guiding future research and industrial applications in the evolving energy landscape.