Geo-Energy Transition and Carbon Management: Table of Contents

Modeling thermal front dynamics in geothermal reservoirs using an open-source MRST–MATLAB simulation framework

Memon, Abdul Rashid — 2026-02-20T00:00:00Z

Geo-Energy Transition and Carbon Management, Vol. 4, Issue 1, 2026, p. 55-60.
Abdul Rashid Memon, Pijus Makauskas
Geothermal energy is a renewable, continuous, globally accessible resource which also helps in reducing carbon emissions. Subsurface aquifers containing thermal water are therefore highly attractive, not only as a sustainable energy source but also as potential reservoirs for large-scale energy storage – an increasingly important function to mitigate the seasonal imbalance native to renewable energy utilization. The detailed and profound knowledge of these processes requires accurate, efficient, and adaptable numerical simulation frameworks. In this study, we present a geothermal simulation workflow implemented in MATLAB, specifically targeting low- to moderate-enthalpy geothermal systems. The accuracy and robustness of this workflow are assessed through benchmarking the MRST-based geothermal module against T-NAVIGATOR, a widely used commercial reservoir simulator. Furthermore, we demonstrate the applicability of this approach by conducting geothermal simulations for selected Lithuanian aquifer complexes, thereby highlighting the potential of geothermal modeling for both energy production and underground energy storage applications.

Cumulative information on the status of H2 production and subsurface storage in India

Verma, Apoorv — 2026-06-16T00:00:00Z

Geo-Energy Transition and Carbon Management, Vol. 4, Issue 1, 2026, p. 1-12.
Apoorv Verma
This review article consolidates the status and prospects of hydrogen (H2) production and underground storage (UHS) in India, emphasizing its role in the energy transition and climate commitments. Studies suggest that H2 production is currently dominated by carbon-intensive methods such as steam methane reforming and coal gasification, transitioning to renewable-powered electrolysis is critical for sustainability. Reports reveals that India’s National Green H2 Mission targets 5 million metric tons (MMT) of green H2 production annually by 2030, supported by 125 GW of renewable energy capacity, to decarbonize sectors like transportation, heavy industry, and chemical manufacturing. India’s geographical advantages, including high solar irradiance and wind potential, provide a strong foundation for green hydrogen production. Additionally, UHS in geological formations such as salt caverns and depleted reservoirs offer large-scale storage potential, with sedimentary basins like Mumbai Offshore and Krishna-Godavari identified as key regions. However, in India, it is essential to evaluate challenges such as high costs, safety concerns, and the regulatory framework associated with UHS. Work on UHS in India has been negligible; whatever limited progress has been made remains purely conceptual, necessitating experimental and simulation-based studies to translate these concepts into practical reality. Overall, there is a tremendous need for targeted research, pilot projects, and policy cooperation to establish India as a global leader in the production and underground storage of H2.

CFD analysis of hydrogen fast-filling process

Panwar, Yuvraj Singh — 2026-06-23T00:00:00Z

Geo-Energy Transition and Carbon Management, Vol. 4, Issue 1, 2026, p. 27-54.
Yuvraj Singh Panwar, Mayur Pal, Rajesh Kumar
The surge in hydrogen-fueled electric vehicles has increased the need for reliable, safe, and efficient refueling methods. During filling of high-pressure Type IV hydrogen tanks, the gas temperature rises significantly due to compression and turbulence, sometimes exceeding the safe limit of 358 Kelvin specified by fueling standards. Elevated temperatures can compromise the cylinder’s structural integrity and reduce the available volume during refuelling. Consequently, thermal management in hydrogen storage systems is crucial. This study employed Computational Fluid Dynamics using ANSYS Fluent to analyse thermal performance during rapid cylinder filling. The model was two-dimensional, axisymmetric, simulating a composite Type IV cylinder, and solved governing equations for mass, momentum, and energy. A realisable k-ε turbulence model and hydrogen’s real gas properties were used, based on established thermodynamic principles. The transient simulation reveals how temperature, pressure, and flow evolve over time during filling. High gas temperatures arise from thermal accumulation and limited convection time, occurring last in the cylinder. The peak temperature, reaching about 390-420 K, occurs early during filling and remains stable through the first stage. As heat transfers to the cylinder wall, the gas temperature drops slightly due to the time lag in heat transfer between injection and the wall. The study emphasises the importance of pre-cooling mechanisms in hydrogen refuelling systems. Without suitable pre-cooling, temperatures can exceed safe levels during fast filling, impairing efficiency and commercial viability. It recommends developing a B2B ecosystem incorporating chiller-based pre-cooling, optimised refuelling protocols, and hydrogen tank manufacturing integration. Such an ecosystem is essential for safe, cost-effective, large-scale adoption, especially in high-demand sectors like heavy-duty transport. Findings underline the necessity of pre-cooling strategies, including inlet pre-cooling and pressure ramp-up techniques, to prevent temperature surges and ensure safe, efficient hydrogen refilling.

AI-driven optimization of CO2 efficiency in CCUS projects

Wagia-Alla, Ahmed — 2026-06-30T00:00:00Z

Geo-Energy Transition and Carbon Management, Vol. 4, Issue 1, 2026, p. 13-26.
Ahmed Wagia-Alla, Mohamed Alghazal, Turki Alzahrani
Designing effective Water-Alternating-Gas (WAG) injection schemes is central to improving oil recovery while simultaneously enhancing CO2 storage outcomes in CO2-EOR operations. Operational choices such as injection rate, gas-to-water ratio (GWR), and cumulative CO2 throughput exert strong control on displacement efficiency, recycle rates, and the fraction of CO2 ultimately retained in the reservoir. Although numerical simulation remains the standard tool for WAG optimization, its high computational cost limits its usefulness for rapid evaluation of multiple operational scenarios, particularly under data uncertainty. To address this limitation, this study introduces a machine-learning-based forecasting workflow using the Temporal Fusion Transformer (TFT) to assess short-term CO2-EOR response and guide WAG optimization within a CCUS framework. Monthly injection and production records were digitized for six mature CO2-EOR fields in the United States, spanning a wide range of reservoir properties and development strategies. Pre-CO2 waterflood behavior was characterized using exponential decline functions to separate baseline trends from incremental oil production induced by CO2 injection. The TFT model was trained on multivariate time-series inputs – including water and CO2 injection rates-to predict oil and CO2 production over a 12-month horizon for alternative WAG configurations. Model robustness and predictive skill were improved through systematic hyperparameter tuning. Across most fields, the trained model demonstrated strong performance, achieving coefficients of determination greater than 0.87 for oil forecasts and above 0.91 for CO2 production. The forecasted results were subsequently used to quantify CO2 utilization and short-term retention, enabling field-specific operational insights. In the Denver Unit, for instance, increasing the GWR from 0.9 to 1.7 resulted in a 48 % increase in retained CO2 and a 38 % improvement in utilization efficiency. Conversely, reducing the GWR in the East Vacuum field from 0.7 to 0.2 enhanced near-term sequestration efficiency despite lower injection volumes. These findings highlight the strong sensitivity of CO2 storage performance to tailored WAG design and demonstrate the potential to reduce recycle losses through targeted operational adjustments. This work represents the first application of a Temporal Fusion Transformer model for multi-field CO2 EOR forecasting and WAG optimization from a CCUS perspective. Unlike prior machine-learning studies that focus primarily on production prediction, this framework directly links AI-based forecasts to operational CO2 utilization and retention metrics, enabling rapid scenario screening without full-physics simulation. The proposed efficiency score provides a novel, field-deployable indicator for balancing short-term oil recovery with carbon storage objectives, offering a scalable digital workflow to support CCUS operational decision-making.