Oil and gas reserves are important resources for human survival, directly related to future oil and gas production and the sustainable development and utilization of energy. It is crucial to strengthen the understanding and judgment of the growth trend of oil and gas reserves. The prediction of the growth trend of oil and gas reserves is a forward-looking research work, and its prediction results will directly affect the direction of future oil and gas exploration and investment. To explore new methods for predicting oil and gas reserves, promote sustainable development and utilization of energy, and provide theoretical basis for oil and gas exploration and development. For this purpose, taking the Llanos Basin in South America as an example, combined with comprehensive data such as oil and gas reserve growth data and various geological characteristics, a combination of Analytic Hierarchy Process and ARIMA algorithm was proposed to predict and verify the oil and gas reserves in the Llanos Basin. Firstly, the Analytic Hierarchy Process is used to perform weight analysis on various geological factors in the Llanos Basin. Analysis shows that structural evolution factors have a significant impact on the growth of oil and gas reserves. On this basis, ARIMA algorithm is applied to perform hierarchical prediction verification on each construction unit of Llanos Basin. The results indicate that the combination prediction method has been validated to have good prediction performance.

How to cite: Li, H. and Zhang, L.: oil and gas reserve prediction method based on Analytic Hierarchy Process and ARIMA algorithm: A case study of the Llanos Basin in South America, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2507, https://doi.org/10.5194/egusphere-egu25-2507, 2025.

The traditional atmospheric correction models employed with the near-infrared iterative schemes inaccurately estimate aerosol radiance at high solar zenith angles (SZAs), leading to a substantial loss of valid products for dawn or dusk observations by the geostationary satellite ocean color sensor. To overcome this issue, we previously developed an atmospheric correction model suitable for open ocean waters observed by the first geostationary satellite ocean color imager (GOCI) under high SZAs. This model was constructed based on a dataset from stable open ocean waters, which makes it less suitable for coastal waters. In this study, we developed a specialized atmospheric correction model (GOCI-II-NN) capable of accurately retrieving the water-leaving radiance from GOCI-II observations in coastal oceans under high SZAs. We utilized multiple observations from GOCI-II throughout the day to develop the selection criteria for extracting the stable coastal water pixels and created a new training dataset for the proposed model. The performance of the GOCI-II-NN model was validated by in-situ data collected from coastal/shelf waters. The results showed an Average Percentage Difference (APD) of less than 23% across the entire visible spectrum. In terms of the valid data and retrieval accuracy, the GOCI-II-NN model was superior to the traditional near-infrared and ultraviolet atmospheric correction models in terms of accurately retrieving the ocean color products for various applications, such as tracking/monitoring of algal blooms, sediment dynamics, and water quality among other applications.

How to cite: Li, H., He, X., Palanisamy, S., Bai, Y., and Xuchen, J.: Atmospheric correction of geostationary ocean color imager data over turbid coastal waters under high solar zenith angles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2102, https://doi.org/10.5194/egusphere-egu25-2102, 2025.

Hydrogen production through autothermal reforming with carbon capture and storage (ATR-CCS) is often considered more reliable and scalable than renewable energy-based hydrogen production, especially when intermittent sources struggle to provide a constant power supply. However, ATR-CCS presents challenges related to the cost and complexity of carbon capture and storage, as well as dependence on fossil fuels, limiting its long-term sustainability. It also requires significant infrastructure and a large amount of energy, which can impact its efficiency and profitability in regions aiming to reduce carbon emissions.
Hydrogen production through renewable energy electrolysis faces obstacles due to intermittency. For instance, solar production varies with temperature and cloud cover, wind energy is unpredictable, and marine sources (waves, tides) present fluctuations, although tidal energy is more predictable. Biomass energy is more stable but depends on raw material availability, while geothermal energy, though stable, can experience variations due to operational issues or resource availability.
Proton exchange membrane water electrolyzers (PEMWE) and alkaline electrolyzers are well-suited for renewable energy sources, as they adjust well to rapid energy supply variations. PEMWE use an electric current to split water into hydrogen and oxygen, offering high hydrogen purity due to a solid polymer membrane. However, they are more expensive and sensitive to impurities in the water. Alkaline electrolyzers, developed earlier and more robust, are less responsive to energy variations but provide a stable solution when energy supply is consistent. They are less expensive in the long run and suitable for large-scale installations.
However, these sudden or irregular variations in energy supply present several technical challenges. First, when energy supply changes abruptly, the temperature inside the electrolyzer can exceed optimal levels (thermal spike) or fall below them (thermal dip), potentially damaging internal components and reducing the overall efficiency of the electrolysis process. Moreover, after an energy fluctuation, the system takes time to stabilize its temperature and pressure, leading to irregular hydrogen production and efficiency losses. These challenges require the use of advanced control strategies capable of real-time regulation of key system parameters (such as current, voltage, and temperature), accounting for different energy fluctuation scenarios (progressive or abrupt). Unlike traditional control systems (simple thermostats or Proportional-Integral-Derivative “PID” control), these approaches (such as model-free control, H-infinity, or optimized PID) ensure better responsiveness and accuracy, guaranteeing stable efficiency even with fluctuations, thereby reducing temperature overshoots and speeding up the stabilization time for electrolyzers. For example, model-free control reduces temperature overshoots and accelerates stabilization time by at least 15 minutes for alkaline electrolyzers.

How to cite: Bouramdane, A.-A., Ait Ziane, M., and Zasadzinski, M.: Strategies for Controlling Blue and Green Hydrogen Flow Rate for Optimal Integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3697, https://doi.org/10.5194/egusphere-egu25-3697, 2025.

The industrial sector is a major contributor to greenhouse gas emissions, responsible for around 24% of global emissions in 2019. According to the World Resources Institute (WRI), to meet short-term climate targets aligned with a 1.5°C increase in global temperatures, the share of electricity in the final energy demand of the industrial sector must increase to 35-43% by 2030, 51-54% by 2040, and 60-69% by 2050.
CITIC Dicastal, the world’s largest producer of automotive aluminum wheels, operates 21 manufacturing facilities globally. These facilities, which collectively produce around 80 million aluminum wheels and 120,000 tons of aluminum castings annually, have significant energy needs due to their high-volume production. For instance, the newly opened plant in Morocco is designed to operate using green energy instead of relying solely on natural gas, utilizing high-temperature furnaces for aluminum alloy melting. This requires a reliable energy source to meet the plant's energy demands.
This study provides tailored recommendations for enhancing efficiency and reducing environmental impact by exploring cogeneration, where both heat and electricity are produced simultaneously. Renewable electricity from photovoltaic and wind sources is used, while water for hydrogen electrolysis is sourced from a water treatment station. For energy storage, batteries are employed for short-term storage, while hydrogen storage is utilized for long-term storage. A portion of the hydrogen produced is burned to generate heat, while the remaining hydrogen is used in a fuel cell to generate electricity. We compare different hydrogen combustion systems and green hydrogen technologies using a multi-scenario analysis approach.
We find that direct-fired systems are prioritized for processes requiring rapid heating, while indirect-fired systems are suitable for applications sensitive to direct flame contact. Fluidized bed combustion systems are effective for burning various fuels, including low-quality fuels. For CITIC Dicastal's decarbonization strategy, selecting electrolyzer technology should consider hydrogen production scale, purity requirements, and integration with existing processes. Alkaline electrolyzers are recommended for large-scale hydrogen production due to their cost-effectiveness and maturity. Proton Exchange Membrane (PEM) electrolyzers are ideal for applications requiring high-purity hydrogen and quick response times. Solid Oxide Electrolyzer Cells (SOECs) offer promising solutions in environments where waste heat can be utilized. We also find that compressed hydrogen storage is particularly advantageous for immediate energy needs, while liquid and solid-state options provide solutions for long-term storage and safety. The study indicates that PEM fuel cells offer quick response times ideal for backup power but come with higher costs. Alkaline Fuel Cells (AFCs) provide a lower-cost alternative but are sensitive to carbon dioxide. Phosphoric Acid Fuel Cells (PAFCs) are suitable for cogeneration but have longer start-up times. Molten Carbonate Fuel Cells (MCFCs) and Solid Oxide Fuel Cells (SOFCs) excel in efficiency, but face challenges related to high-temperature operations.
Overall, this research underscores the potential of integrating advanced hydrogen technologies into CITIC Dicastal’s operations to achieve significant decarbonization goals.

How to cite: Bouramdane, A.-A. and Degiovanni, A.: Cost-Effective and Sustainable Pathways for Green Industrial Cogeneration: Replacing Natural Gas with Hydrogen in Dicastal's Operations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3246, https://doi.org/10.5194/egusphere-egu25-3246, 2025.

The sustainable development of microalgae bioenergy systems faces dual challenges: identifying suitable cultivation locations and optimizing production parameters across diverse environmental conditions. Building upon our previous research on global marginal land assessment and machine learning applications in microalgae cultivation, this study presents a novel multi-modal artificial intelligence framework that combines deep learning, machine learning, and large language models (LLMs) to address these challenges comprehensively. Our approach integrates three key components: (1) a hybrid deep learning network with attention mechanisms for biomass productivity prediction across different geographical and climatic conditions, (2) LLM-powered intelligent analysis of historical experimental data (1980-2024) for parameter optimization and pattern discovery, and (3) advanced machine learning algorithms for identifying and assessing marginal land suitability. Initial spatial analysis has identified approximately 7.37 million square kilometers of marginal lands suitable for microalgae cultivation, particularly in equatorial and low-latitude regions, with Australia, Kazakhstan, Sudan, Brazil, the United States, and China showing significant potential. Our previous machine learning models demonstrated that Photobioreactors (PBRs) achieved a global average daily biomass productivity of 142.81mgL−1d−1, while Open Ponds reached 122.57mgL−1d−1. Building on these findings, our new deep learning framework shows a 35% improvement in productivity prediction accuracy compared to traditional methods, achieving a test R² of 0.94. The LLM-based data mining approach reveals novel correlations between cultivation parameters and system performance across different geographical contexts, while accounting for various cultivation methods. The framework suggests that optimal cultivation strategies could potentially increase biomass yields by 40% while minimizing resource inputs, with projected annual production reaching 99.54 gigatons of microalgae biomass when utilizing suitable marginal lands. This biomass could be transformed into 64.70 gigatons of biodiesel, equivalent to 58.68 gigatons of traditional diesel, while sequestering 182.16 gigatons of CO₂. The integration of LLMs for experimental data analysis represents a significant advancement in understanding complex parameter interactions and optimization opportunities. This integrated approach not only advances our understanding of microalgae cultivation optimization but also provides practical insights for sustainable land management and renewable energy development, while addressing critical challenges in climate change mitigation through bioenergy production and carbon sequestration.

How to cite: Chen, M., Ngo, H. H., and Zhang, Q.: Unlocking Global Bioenergy Potential: Multi-Modal AI Framework for Microalgae Cultivation on Marginal Lands with Intelligent Data Mining, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1447, https://doi.org/10.5194/egusphere-egu25-1447, 2025.

Urban mobility is undergoing significant technological transformations, with the application of sharing and autonomous driving technologies. It will reshape people's travel behavior patterns. However, the direction of the change is heavily influenced by urban spatial features, including the density of population, the distance between residents and job, the public transportation infrastructure, the diversity of local place, as well as the urban form. In response to this evolving landscape, this study integrates macro-level predictions from IAM with micro-level features of urban space to reassess the trends in travel demand in China up to the years 2030 and 2060. The findings indicate that, when considering the micro-features of existing urban spaces, projections based on future comprehensive system evaluation models may significantly overestimate the volume of car travel, so as to the demands on private cars. Variations between different regions and within the same city, particularly between new and old neighborhoods, further reveal the substantial potential for reducing car travel through urban planning and management. Consequently, this research proposes the design and experimentation of new business models for intelligent and shared mobility that align with the micro-spatial configuration of cities. It explores more sustainable pathways for the low-carbon transformation of urban transportation, aiming to harness the unique spatial attributes of cities to foster innovative solutions.

How to cite: Tong, X. and Wang, T.: Rethinking Future Travel Demand in China: Integrating IAM with Local Context for Sustainable Future Mobility, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5507, https://doi.org/10.5194/egusphere-egu25-5507, 2025.

Over the past fifty years, the Indian Ocean has experienced a pervasive warming trend, prompting investigations into the causative factors and consequential impacts at a basin-wide scale. Research analyzing sea surface temperature (SST) suggests that the western Indian Ocean has been undergoing warming for more than a century. The increase in SST has triggered a range of effects, including alterations in surface pressure distribution, resulting in variable wind patterns, sea-level rise, and other associated outcomes. Understanding the variability in wind speed holds practical significance, including estimating wind power potential for specific geographic regions and developing future projections for wind wave climates to aid in the planning of coastal activities and coastal zone management. The World Climate Research Programme (WCRP) within the Intergovernmental Panel on Climate Change (IPCC) plays a pivotal role in disseminating comprehensive insights into the past, present, and future trajectories of climate change for the scientific community. The CMIP6 project, introduces a spectrum of shared socio-economic pathways (SSP) projecting radiative forcing values ranging from 1.9 to 8.5 W/m² by the end of the century. For a comprehensive understanding of future climate projections, a thorough evaluation and skill assessment of General Circulation Models (GCMs) within the CMIP6 project, specifically regarding their ability to simulate wind speed, is imperative. In this study, BCC-CSM2-MR model has been leveraged to project future changes in the offshore wind energy potential over the Arabian sea. The projections of wind speed at a height of 50 meters using the BCC-CSM2-MR, on the Arabian Sea within a span of three distinct periods: Near-future (2026-2050), Mid-future (2051- 60 2075), and Far-future (2076-2100) and for two distinct Shared Socio-economic Pathway (SSP) scenarios, namely SSP1-2.6 and SSP3-7.0, have been estimated in this study. The overall trend indicates that wind speed over the Arabian Sea remains relatively constant, showing no significant changes. However, a subtle increase is discernible on the western side of the Arabian Sea, particularly near the Oman coast, evident in the SSP3-7.0 scenario. The Projected change in the wind power density (WPD) for the three distinct period change are evaluated keeping the historical wind data from 1990-2014 as a reference. The WPD is increasing by 10% over the Arabian sea for SSP1-2.6 for near-future (2026- 2050), 8% for mid-future (2051-2075) and 6% for far-future (2076-2100) with respect to historical wind speed (1990-2014). But for SSP3-7.0 the wind speed is showing a decline of 2%to 4 % from near-future to far-future. Correspondingly, wind power density exhibited spatial changes over the Arabian Sea, with the western side showing an increase under SSP1-2.6 and a decrease under SSP3-7.0

Keywords: Wind Speed, Wind Power Density, CMIP6, Arabian Sea, Offshore Wind Energy, Climate Change.

How to cite: Ansari, S. and Behera, M. R.: Impact of Climate Change on Offshore Wind Energy Potential over the Arabian Sea using CMIP6 Future Projection., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2114, https://doi.org/10.5194/egusphere-egu25-2114, 2025.

A new wave energy atlas database for French Polynesia

A wave reanalysis was carried out over the whole French Polynesian ZEE, using Météo-France's MFWAM model at 0.05° resolution, derived from the WAM model with a spatial resolution of 5 km, a three-hourly step, and a temporal depth of 30 years. These results have been published on the ODATIS open data platform.

Compared with existing reanalysis, the islands are better modelled, and it leads to better estimates of wave conditions and propagation.

It has been applied to wave energy evaluation over the whole French Polynesia.

Simulating the integration of 10 MW wave energy in Tahiti: challenges and opportunities

Sea state data from the atlas were used as input for simulating the production of 10 MW wave energy through several systems. These simulations were carried out for integration into the Tahitian power grid at several injection points and considering existing and planned renewable energies plants.

Over the same past time period, the island's electricity mix was modelled, showing the complementarity of wave energy with the other renewable energies found on the island, in particular photovoltaics and hydropower.

Tahiti presents (as in 2022) an electrical mix of 64% Oil, 29% Hydro, 7% PV (including roofs and plants connected to the Grid). New PV plants + batteries are studied by local stakeholders for the next years, involving other types of issues (land & recycling).

Our work highlights the advantages and challenges of integrating wave energy into the grid and raises the question of the methodology's replicability on other islands.

How to cite: Dubois, C., Chabbert, H., Reveil, M., and Vitrac, V.:  Wave atlas of French Polynesia – Application on wave energy integration into the electrical mix , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9146, https://doi.org/10.5194/egusphere-egu25-9146, 2025.

     With the continuous development of deep oil and gas exploration, the phenomenon of oil and gas enrichment near prematurely failed source faults in deep formations has been revealed. However, the mechanism of how these prematurely failed faults open to transport hydrocarbon is not yet clearly understood, and there is a lack of quantitative evaluation of their transport capacity. This study takes the Lufeng 13 Sag in the Pearl River Mouth Basin as an example. Based on 3D seismic data, software simulation, and mudstone plastic deformation experiments, it analyzes the reactivation mechanism of prematurely failed faults and evaluates their vertical transport capacity, revealing the role of these faults in deep hydrocarbon enrichment. The study shows that the transport capacity of prematurely failed faults is negatively correlated with the normal stress on the fault plane during the reservoir-forming period and positively correlated with the ultimate pressure for mudstone plastic deformation. When the normal stress on the fault plane during the reservoir-forming period is less than 13.9 MPa, the buoyancy of hydrocarbon can overcome the normal stress on the fault plane at the upper interface of the source rock, allowing hydrocarbon to migrate upward along the fault. When the ultimate pressure for mudstone plastic deformation is greater than 18.5 MPa, the pressure on the fault plane is less than the ultimate pressure for mudstone plastic deformation, and the argillaceous components in the fault zone do not undergo plastic deformation and flow. The leakage spaces left in the fault zone are not blocked, and no seal is formed vertically. Based on the normal stress on the fault plane during the reservoir-forming period and the ultimate pressure for mudstone plastic deformation, a vertical transport coefficient (K) for prematurely failed faults is established. When K is less than 1.1, the prematurely failed fault has vertical transport capacity during the reservoir-forming period.

How to cite: Cao, X., Liu, H., Peng, G., and Long, Z.: Study on the Vertical Transport Capacity of Prematurely Failed Faults in Deep Oil and Gas Enriched Areas: A Case Study of Lufeng 13 Sag in the Pearl River Mouth Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4960, https://doi.org/10.5194/egusphere-egu25-4960, 2025.

In the fields of geological research and engineering applications, fault identification is of great significance for understanding geological structure evolution, predicting geological disasters, and guiding resource exploration and development. Traditional fault identification methods based on manual interpretation and seismic attributes struggle to meet the requirements in terms of efficiency and accuracy when faced with complex geological conditions and massive amounts of data. With the development of deep learning technology, convolutional neural networks have demonstrated excellent performance in image recognition and segmentation tasks. However, the multi-scale characteristics of faults, that is, the fault structures in seismic images are diverse in size, shape, and complexity, pose severe challenges to image recognition. This paper innovatively proposes a fault identification method based on an improved U-Net neural network. Focusing on the multi-scale characteristics of faults, it aims to enhance the accuracy and robustness of fault identification. The model introduces a multi-scale feature fusion mechanism, skillfully integrating encoder feature maps with different spatial resolutions, which significantly improves the ability to express fault features. In addition, in view of the insufficient representativeness of synthetic datasets, this study adopts data augmentation techniques, performing operations such as rotation, flipping, and scaling on the training data to effectively expand data diversity and enhance the generalization ability of the model. Experimental results show that when the improved U-Net model is tested on the publicly available F3 seismic data of the Dutch North Sea and the data of an oilfield in the Junggar Basin, China, compared with the traditional U-Net model, it has achieved significant improvements in key evaluation indicators such as recognition accuracy, recall rate, IOU, and PR curve. Especially in complex geological backgrounds, the improved model can more accurately identify the location and shape of faults, providing a more reliable and efficient fault identification technical means for fields such as geological structure research, oil exploration, and underground engineering construction. It has important theoretical significance and practical application value.

How to cite: Huang, Y., Cui, L., Niu, Y., Tao, Y., Liu, Y., and Chen, Y.: 3D Fault Identification Based on Improved U-Net with Multi-Scale Feature Fusion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16619, https://doi.org/10.5194/egusphere-egu25-16619, 2025.

Abstract: A large strike-slip fault system has been found in the central Sichuan Basin, although its effects on the pre-Mesozoic tight dolomite gas reservoirs in the deep (>4500 m) subsurface are uncertain. By integrating 3D seismic fault mapping, detailed fracture characterization, and well production data, this study demonstrates that strike-slip faults are extensively developed as vertically stratified arrays within the Ediacaran, Cambrian, and Permian dolomite intervals. These faults connect Lower Cambrian source rocks to multiple reservoir horizons, thereby establishing both lateral and vertical hydrocarbon migration pathways. A defining element of this system is the spatiotemporal coupling of “source-fault-reservoir,” which underpins the formation of a large-scale, pre-Mesozoic fault-controlled gas accumulation. Seismic evidence shows that many of these faults exhibit near-vertical geometries, en echelon arrangements, and step-over structures, all of which foster intense fracturing in the adjacent dolomites. Such fracturing substantially enhances porosity and permeability, yielding localized “sweet spots” with improved storage capacity and fluid flow properties, particularly within slope areas where structural conditions favor gas trapping. Production data strongly corroborate the geological and seismic observations, with wells that intersect or closely adjoin these fault zones typically exhibiting higher flow rates and more stable production profiles. This phenomenon highlights the pivotal role of fault-induced fractures in reservoir performance and underscores the need for detailed fault mapping and fracture network analysis in deep, tight carbonate plays. Furthermore, the recognition of this large-scale, strike-slip fault-controlled dolomite reservoir in a deep intracratonic setting underscores its considerable exploitation potential and points to broader implications for petroleum geology. Consequently, this study provides a robust framework for understanding the interplay between fault architecture and reservoir quality, offering valuable insights for guiding future exploration and development in analogous deep carbonate basins worldwide.

Key words: Strike-slip fault; Deep tight dolomite reservoir; Strike-slip fault-related petroleum system; Migration and accumulation; Exploration; Sichuan Basin

How to cite: Tian, W., Jiang, T., and Wu, G.: Impact of Pre-Mesozoic Strike-Slip Faults on Dolomite Gas Reservoir in the Central Sichuan Basin and Its Exploration Potential, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10984, https://doi.org/10.5194/egusphere-egu25-10984, 2025.

The superimposed basins in western China have undergone multiple periods of tectonic changes and cycles of oil and gas accumulation, and the distribution patterns of oil and gas are very complex, which limits the accurate understanding of the mechanisms of oil and gas accumulation. In this paper, Yuqi area in Tarim Basin is taken as the research area, and based on the geological background, fluid inclusion-homogenization temperature, hydrocarbon inclusion abundance analysis, reservoir quantitative fluorescence technology, infrared spectrum, crude oil geochemical analysis, reservoir asphalt identification and other technologies, the Ordovician-Triassic oil and gas accumulation, migration and adjustment process in Yuqi area is studied. The results indicate that the Ordovician system in the study area developed oil injection during the Late Caledonian, Yanshanian, and Himalayan periods. The Triassic system only had oil injection during the Himalayan period, slightly later than the Ordovician system during the same period. The crude oil injected by the Ordovician in the late Caledonian period was biodegraded into heavy oil and carbonaceous bitumen due to tectonic uplift. Light oil from the Yuertus Formation source rock during the Yanshan-Himalayan period was vertically injected into the Ordovician reservoir along activated faults, and then mixed and transformed early heavy oil reservoirs through lateral adjustment along karst. A certain range of light oil reservoirs were formed in the heavy oil reservoir area. In the late Himalayan period, the light/heavy oil reservoirs mixed and filled by the Ordovician system were locally adjusted upwards along faults to the Triassic system, making the crude oil of the Triassic system, which had stable structures and no degradation conditions, similar to the crude oil of the Ordovician system in terms of crude oil density, maturity, inclusion abundance, biodegradation characteristics, and partially mix with late mature oil and gas that migrated along the Luntai fault-sand body, forming the sporadic distribution characteristics of light and heavy oil reservoirs in the Triassic system today. Therefore, a reservoir formation model of "vertical transport along faults, lateral adjustment along karst, strong degradation, and differential superposition" was established for the Ordovician, and " T-shaped transport along fault-sand and late stage reservoir formation " was established for the Triassic in the Yuqi area.The research have important guiding and reference significance for shallow-deep oil and gas exploration in the Yuqi area.

How to cite: Su, Y., Liu, H., Wang, S., Wang, J., and Zhao, Z.: Multi-layer Hydrocarbon Accumulation Model in Yuqi area, Tarim Basin, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1746, https://doi.org/10.5194/egusphere-egu25-1746, 2025.

Land is not considered in its entirety in mitigation modelling, especially for India. Even when reported as an output, competing land demands from renewable energy (RE), urbanisation, agriculture, and forestry and the resultant trade-offs are not adequately captured in existing models. In this study, we augmented the Sustainable Alternative Futures for India (SAFARI) model to determine the feasibility of a net-zero transition from a land availability perspective. SAFARI is a system dynamics simulation model that captures the dynamic interactions among various land types and therefore their competition. Using SAFARI, we developed illustrative net-zero scenarios for India to understand the land implications of the transition. We find that while India might have just enough land at a national aggregate level to support the transition to net-zero emissions, local constraints and land conflicts owing to acquisition challenges are more likely to occur in a high electrification scenario where there is increased focus on RE. Alternatively, a scenario with a focus on use of alternative fuels, nuclear power, behavioural changes, and efficiency improvements in addition to electrification and RE, would be more inclusive and optimal for a country like India.

How to cite: Sundaresan, A., Ashok, K., Natarajan, R., Gangopadhyay, A., and Murthy, I. K.: Modelling land-use dynamics for net-zero emissions: a framework for informed decision-making in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16313, https://doi.org/10.5194/egusphere-egu25-16313, 2025.