PICO
It is societal consensus that adequate and reliable supplies of affordable resources need to be obtained in environmentally sustainable ways [1]. The prevailing view in the scientific community is that large amounts of carbon dioxide (CO2) from the atmosphere and highly radioactive waste must be disposed of safely - kept away from human habitat for a very long time. In that regard, studies on natural processes that extend over very long periods help to understand the long-term behaviour of deep geological repositories. You can't carry out experiments over thousands of years. However, processes similar to those that occur at depth can also be found in nature. In combination with short-term laboratory experiments and field tests, it is possible to provide a comprehensive and reliable picture of the situation and to give a prognosis on the long-term.
In the context of climate policy, the storage of CO2 in deep geological formations is seen as a potential means to reduce anthropogenic greenhouse gas emissions and mitigate respective global warming effects. However, questions arise: is it feasible to store CO2 safely without endangering humans or the environment? Science and technology did provide answers to this question. In this context the GFZ driven experimental pilot site at Ketzin demonstrated the safe and reliable injection of CO2 into a saline aquifer on the research scale [1].
The search for a site for the disposal of highly radioactive waste is an intergenerational social and political task with a geoscientific core. The first challenge is to narrow down suitable areas. The second is to analyse the subsurface using geoscientific knowledge, methods and data to determine its suitability in detail. It is the famous search for needles in the haystack. In order to identify the site with the best possible safety the search has to be carried out systematic and specific [2].
The question to be answered is: how accurate, reliable and robust must be our knowledge for a decision where and how to dispose CO2 or nuclear waste in the subsurface? Ultimately, it is necessary to clarify which data is needed in order to reduce the uncertainties of our conceptual thinking and ensure the development of repositories in practice.
[1] Kühn, M., Bruckman, V. J., Martens, S., Miocic, J., Stasi, G. (2024): Preface to the special issue of the Division Energy, Resources and the Environment at the EGU General Assembly 2023. - Advances in Geosciences, 62, 67-69. https://doi.org/10.5194/adgeo-62-67-2024
[2] Kühn, M., Kempka, T., Liebscher, A., Lüth, S., Martens, S., Schmidt-Hattenberger, C. (2011): Geologische CO2-Speicherung am Pilotstandort in Ketzin - sicher und verlässlich. - System Erde, 1, 2, 44-51. https://doi.org/10.2312/GFZ.syserde.01.02.4
[3] Kühn, M., Heidbach, O., Heumann, A., Zens, J. (2021): Nadeln im Heuhaufen. - System Erde, 11, 2, 6-11. https://doi.org/10.48440/GFZ.syserde.11.02.1
How to cite: Kühn, M.: Copied from nature - locked up for eternity - storage of carbon dioxide and nuclear waste at depth, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18801, https://doi.org/10.5194/egusphere-egu25-18801, 2025.
Achieving net-zero emissions and combating climate change necessitate the adoption of negative emission technologies. Direct Air Capture (DAC), which removes CO2 directly from ambient air, shows great promise but requires significant energy, particularly in the form of heat. Geothermal doublets are traditionally used to supply heat for district heating networks, which operate at lower temperatures than those required for DAC. This study explores integrating geothermal heat from a doublet with DAC using a heat pump, while repurposing waste heat from the DAC process to support district heating. We propose multiple operational configurations for this system and conduct a life cycle emissions analysis, demonstrating the potential to capture tens to hundreds of thousands of tonnes of CO2 over its lifetime. The results show that higher geothermal fluid temperatures and flow rates substantially lower the cost per tonne of CO2 captured. Conversely, higher fluid temperatures combined with lower flow rates reduce overall system costs. These findings highlight the critical role of optimal subsurface conditions in maximizing system efficiency.
How to cite: Miocic, J.: Linking geothermal district heating with Direct Air Capture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12969, https://doi.org/10.5194/egusphere-egu25-12969, 2025.
Waiwera is a small coastal village with a 50 °C warm geothermal reservoir of 400 metres thickness directly underneath [1-2]. Hydrogeological models support water management by providing insights into sustainable extraction of water from the reservoir. We analysed the system in several studies over the past decades, mainly the last ten years [3-12].
Geothermal waters at Waiwera originate from rainwater percolating downward and heating by the background geothermal gradient. The system is fed along a fault zone located at the base of the reservoir. New radiocarbon dating shows the upwelling water to be >20,000 years old [13]. The present contribution gives an overview of the current research status, as well as the ongoing reinterpretation of recharge models for meteoric water.
[1] Auckland Regional Water Board (1987): Waiwera thermal groundwater allocation and management plan 1986. - Auckland (NZ), Auckland Regional Water Board. 85 p. (AWRB technical publication; 39).
[2] Zemansky, G. (2005): Hydrogeological evaluation of the Waiwera geothermal aquifer. - Lower Hutt (NZ), Institute of Geological & Nuclear Science Limited. 56 p. Client Report 2005/131. Prepared for Waiwera Infinity Limited.
[3] Kühn, M., Stöfen, H. (2005): A reactive flow model of the geothermal reservoir Waiwera, New Zealand. - Hydrogeology Journal, 13, 4, 606-626. https://doi.org/10.1007/s10040-004-0377-6
[4] Kühn, M., Altmannsberger, C. (2016): Assessment of Data Driven and Process Based Water Management Tools for the Geothermal Reservoir Waiwera (New Zealand). - Energy Procedia, 97, 403-410. https://doi.org/10.1016/j.egypro.2016.10.034
[5] Kühn, M., Schöne, T. (2017): Multivariate regression model from water level and production rate time series for the geothermal reservoir Waiwera (New Zealand). - Energy Procedia, 125, 571-579. https://doi.org/10.1016/j.egypro.2017.08.196
[6] Kühn, M., Schöne, T. (2018): Investigation of the influence of earthquakes on the water level in the geothermal reservoir of Waiwera (New Zealand). - Advances in Geosciences, 45, 235-241. https://doi.org/10.5194/adgeo-45-235-2018
[7] Somogyvári, M., Kühn, M., Reich, S. (2019): Reservoir-scale transdimensional fracture network inversion. - Advances in Geosciences, 49, 207-214.
https://doi.org/10.5194/adgeo-49-207-2019
[8] Präg, M., Becker, I., Hilgers, C., Walter, T. R., Kühn, M. (2020): Thermal UAS survey of reactivated hot spring activity in Waiwera, New Zealand. - Advances in Geosciences, 54, 165-171. https://doi.org/10.5194/adgeo-54-165-2020
[9] Kühn, M., Grabow, L. (2021): Deconvolution well test analysis applied to a long-term data set of the Waiwera geothermal reservoir (New Zealand). - Advances in Geosciences, 56, 107-116. https://doi.org/10.5194/adgeo-56-107-2021
[10] Kühn, M., Präg, M., Becker, I., Hilgers, C., Grafe, A., Kempka, T. (2022): Geographic Information System (GIS) as a basis for the next generation of hydrogeological models to manage the geothermal area Waiwera (New Zealand). - Advances in Geosciences, 58, 31-39. https://doi.org/10.5194/adgeo-58-31-2022
[11] Kempka, T., Kühn, M. (2023): Numerical simulation of spatial temperature and salinity distribution in the Waiwera geothermal reservoir, New Zealand. - Grundwasser, 28, 243-254. https://doi.org/10.1007/s00767-023-00551-8
[12] Kühn, M., Stagpoole, V., Viskovic, G. P. D., Kempka, T. (2024): New data for a model update of the Waiwera geothermalreservoir in New Zealand. - Advances in Geosciences, 65, 1-7. https://doi.org/10.5194/adgeo-65-1-2024
[13] Viskovic, G. P. D., Stagpoole, V. M., Morgenstern, U. (2023): Results of microgravity survey and ground water sampling, Waiwera, Auckland. - GNS Science Report, 2023/33.
How to cite: Kühn, M., Schöne, T., Grabow, L., Viskovic, G. P. D., and Kempka, T.: Waiwera: evolving understanding of a New Zealand geothermal system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18360, https://doi.org/10.5194/egusphere-egu25-18360, 2025.
The requirement and push for decarbonising the global energy system are becoming increasingly critical. One area that has entered this conversation is the use of Micro and Pico scale energy solutions like flow-induced energy harvesters to utilise the untapped energy potential within pre-existing infrastructure, like the European water infrastructure where the unutilised energy potential is estimated at 10 TWh/y [1]. Understanding and assessing the overall application feasibility of these types of technologies is vital to the successful deployment and development of these Micro and Pico energy generation solutions. This assessment needs to include a feasibility assessment that integrates technical and economic feasibility alongside society acceptance, energy security, and environmental impacts of the devices in relation to the integration of current water and energy system infrastructure. Notably, a limited number of assessment tools carry out a comprehensive feasibility assessment of these technologies regarding their deployment as secondary energy generation technologies and integration of current water or energy systems.
This research applies a comprehensive feasibility assessment tool which is being designed as part of the H-Hope Horizon Project (https://h-hope.eu) to assess the feasibility of one prototype design of H-Hope vortex-induced vibration energy harvesters (VIV-EH) in urban settings.
The results demonstrate that the current prototype design of the VIV-EH has a power output comparable to the energy generation output of other hydropower energy harvester (H-EH) devices. In that regard the prototype illustrates a positive technical feasibility regarding power generation. The assessment has defined factors such as device designs and manufacturing quality, as well as high water velocities and sediments in the water channels, as the most significant technical and operational risks of the VIV-EH. Furthermore, the current VIV-EH prototype cannot be considered economically feasible since LCOE is revealed to be up to 20 times higher in comparison with other H-EH prototypes and small-scale renewable energy technologies. On the other hand, the current design of the VIV-EH prototype presents a low ecological and environmental impact regarding material selection, manufacturing and installation in pre-existing water channels, and the assessment demonstrates that further optimisation to improve the efficiency of the VIV-EH prototype does further decrease the environmental impact per unit of energy produced by the device.
Overall, the results highlight that further development and optimisation of the VIV-EH will improve the device's ability to harness power potential in the system and enhance the device's resilience to mitigate the impact of the aforementioned risks. Therefore, it improves power output, reduces the LCOE and environmental effects of energy generation, and makes it more attractive for deployment and affordable as a secondary energy generation technology for off-grid and urban applications. This work showcases an assessment framework, which can potentially be applied to assess the feasibility of various types of micro or pico energy generation technologies as secondary energy sources to unlock unutilised energy sources in our modern infrastructure networks.
[1] Quaranta, E., Bódis, K., Kasiulis, E. et al. Is There a Residual and Hidden Potential for Small and Micro Hydropower in Europe? A Screening-Level Regional Assessment. Water Resour Manage 36, 1745–1762 (2022). https://doi.org/10.1007/s11269-022-03084-6
How to cite: Guðlaugsson, B., Marguerite Bronkema, B., Stepanovic, I., Secnik, M., Hočevar, M., and Christian Finger, D.: Unlocking Hidden Energy: Assessing Micro and Pico Solutions for Sustainable Power Generation in Water Infrastructure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17847, https://doi.org/10.5194/egusphere-egu25-17847, 2025.
This paper introduces HomeSHIFT, a framework designed to minimize personal energy carbon footprints through easily implementable algorithms for household energy management. HomeSHIFT leverages continuous monitoring of grid carbon intensity, which fluctuates based on the availability of renewable energy sources such as wind and solar. During periods of high wind and solar generation, grid CO₂ emissions are lower, while reliance on fossil fuels during other times results in higher emissions. Using grid carbon intensity data from CAISO and household energy data from utility smart meters, high-emission periods—particularly evening ramps—were identified.
HomeSHIFT optimizes battery discharge and EV charging schedules within the same time-of-use windows, aligning energy use with periods of lower grid intensity. The system operates seamlessly in the background, requiring no behavioral changes from the consumer, making it highly scalable and user-friendly. Under the Pacific Gas & Electric Time-of-Use tariff in Northern California, the high-price period runs from 4 PM to 10 PM on weekdays. Standard settings in the Tesla Powerwall application discharge the battery at maximum power starting at 4 PM, depleting it within two hours. Consequently, from 6 PM onward, the household must rely on grid electricity, often during peak carbon intensity. HomeSHIFT shifted the battery discharge to 7–9 PM, reducing grid carbon intensity by 46% compared to the 4–6 PM window.
By prioritizing clean energy use and reducing consumption during high-intensity periods, HomeSHIFT offers a scalable and practical method for cutting household carbon emissions through small programming changes. These adjustments can be seamlessly integrated into existing applications without requiring consumer behavioral shifts. Scaling HomeSHIFT across all batteries, electric cars, and homes holds the potential to reduce millions of tons of emissions globally.
How to cite: Narayan, A.: HomeSHIFT: Automated Coordination of Home Distributed Energy Resources for Minimizing Personal Energy Carbon Footprint, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14204, https://doi.org/10.5194/egusphere-egu25-14204, 2025.
This study explores a multi-objective optimization framework for energy retrofits, integrating future climate scenarios to evaluate their impact on economic, environmental, and human comfort objectives. As climate change is expected to alter temperature patterns, heating and cooling demands, and extreme weather conditions, conventional retrofit strategies may not be sufficient to maintain long-term building performance. Many existing retrofits rely on historical climate data, which may not accurately represent future energy needs. To address this, the study employs Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) to generate future weather projections, ensuring a more forward-looking approach to retrofit assessment. The research utilizes EnergyPlus simulations, along with Honeybee/Ladybug, to model energy performance across various retrofit strategies. Multi-objective optimization, specifically NSGA-II (Non-Dominated Sorting Genetic Algorithm II), is applied to explore trade-offs between Life Cycle Cost (LCC), Life Cycle Carbon Emissions (LCCE), and occupant thermal comfort. The optimization process identifies Pareto-optimal solutions, balancing cost-effectiveness, energy efficiency, and indoor comfort. Retrofit measures considered include building envelope improvements, such as enhanced insulation, advanced glazing, improved airtightness, and cool roofs. The integration of cool roof technology is particularly relevant as it has the potential to reduce cooling loads, lower peak energy demand, and mitigate urban heat island effects, contributing to improved indoor comfort and reduced energy consumption. Preliminary findings suggest that climate-adaptive retrofit strategies, including cool roofs, could help improve energy performance and cost efficiency under changing climate conditions. The study provides insights for building designers, policymakers, and stakeholders, helping them develop more sustainable and resilient retrofit solutions. By integrating future climate data into retrofit planning, this research contributes to the long-term sustainability of buildings and supports efforts to reduce carbon emissions, optimize energy efficiency, and enhance occupant well-being. Future work could explore additional retrofit strategies, broader climate scenarios, and alternative optimization methods to further refine climate-adaptive retrofit planning.
How to cite: Le, T. D. and Lin, S.-Y.: Multi-Objective Optimization of Energy Retrofit Strategies under Future Climate Scenarios: Balancing Economic, Environmental, and Human Comfort Objectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7839, https://doi.org/10.5194/egusphere-egu25-7839, 2025.
Background: Appropriate bioprocessing of lignocellulosic materials into ethanol could address the world’s insatiable appetite for energy while mitigating greenhouse gases. Bioethanol is an ideal gasoline extender and is widely used in many countries in blended form with gasoline at specific ratios to improve fuel characteristics and engine performance. Finding a suitable microbial agent for the efficient conversion of lignocellulose is still an active field of study.
Objective: To enhance the bioethanol production with effective lignin degradation and utilization of pentose and hexose sugars in an economical way.
Methods: Ryegrass (Lolium perenne L.) biomass was the substrate. Microbial strains Bacillus mobilis, Bacillus velezensis, and Bacillus cereus, were isolated, identified, determined for their lignin degradation capability, and used as pretreatment agents for the lignin degradation. Various modern spectroscopic analyses, ligninolytic activity, sugar estimation, enzymatic hydrolysis, and liquefaction and fermentation process were conducted. The final data was statistically validated with post-hoc Tukey test, R software and SPSS Statistics 26.
Results: The proximate and ultimate analyses of raw biomass showed that it comprised of total solids 96.54%, volatile solids 92.82%, carbon 48.22%, and sulphur 0.28%. After the application of bacterial pretreatments, the lignin content was considerably reduced to 6.78%, and the cellulose share increased to 57.31%. The LiP and MnP like activity was highest in alkaline lignin culture source with an amount of 0.67 ± 0.1 U/mL and 1.03 ± 0.08 U/mL, respectively. The optimum sugar utilization efficiency was reached at 93.46 %, with the highest bioethanol production of 0.51 g/g and 85.78 % bioethanol yield after the anaerobic fermentation.
Conclusion: In this study, successful delignification of the ryegrass biomass was achieved by bacterial pretreatments and maximum bioethanol was produced. The integration of bacterial pretreatments and C5 and C6 sugar utilizing microbial strains could enhance the commercial bioethanol production. The ryegrass biomass was selected since it is a common agricultural waste in China. The transformation of this biomass into industrial products like ethanol, would not only utilize waste but also accord to environmental safety. However, to meet global energy demand further studies to develop sustainable and cost-effective approaches are still required.
How to cite: Malik, K., Li, C., and Arshad, A.: Application of ligninolytic enzyme producing bacteria for enhanced bioconversion of ryegrass biomass into bioethanol, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-60, https://doi.org/10.5194/egusphere-egu25-60, 2025.
Anaerobic digestion (AD) offers a sustainable solution for managing sugarcane vinasse, producing renewable energy and nutrient-rich effluents. However, the seasonal availability of vinasse (7–8 months annually) causes operational interruptions during the sugarcane off-season, undermining the stability and energy output of AD systems. This study addresses these limitations by evaluating glycerol as an alternative substrate during the sugarcane off-season in an anaerobic structured bed reactor (AnSTBR). The reactor was operated for 370 days at 30°C with polyurethane foam as support material. The system was inoculated with UASB granular sludge and transitioned between four operating stages: vinasse mono-digestion (V-moD), vinasse-glycerol co-digestion (VG-coD), glycerol mono-digestion (G-moD) and vinasse mono-digestion (V-moD), respectively. During Stage I, vinasse AmoD achieved 81.1 ± 1.5% COD removal at the highest organic loading rate (OLR) of 5.0 kg-COD m⁻3 d⁻1, with volumetric methane production (VMP) of 1027 NmL-CH4 L⁻1 d⁻1 and a methane yield (MY) of 250.6 ± 13.2 NmL-CH4 g⁻1-CODrem. Supplementing glycerol up to 50% (in terms of mass COD) in Stage II increased COD removal to 84.7 ± 1.1% and VMP to 1106.9 ± 70.2 NmL-CH4 L⁻1 d⁻1, though methane content declined by 5%. Glycerol AmoD (Stage III) yielded stable COD removal (93.1 ± 0.6%) and VMP (1075.1 ± 95.0 NmL-CH4 L⁻1 d⁻1), despite partial alkalinity reductions (881 to 253 mg-CaCO3 L⁻1) linked to higher metabolite accumulation (20 to 277 mg L⁻1). Returning to vinasse AmoD restored COD removal to 82.3 ± 0.6% with enhanced methane content (69.6 ± 0.6%) due to increased homoacetogenic activity. Substrate switching showed no significant impact (α = 0.05) on vinasse MY. Microbial analysis revealed shifts from Clostridium dominance during glycerol use to Bacteroides and Porphyromonas during vinasse digestion. Methanosaeta (47.6–74.2%) and Methanolinea (11.1–30.5%) dominated acetoclastic and hydrogenotrophic methanogenesis, respectively, supporting balanced acidogenesis-methanogenesis through acetogen-methanogen syntrophy
How to cite: Damianovic, M. H. R. Z., Takeda, P. Y., and Borges, A. D. V.: Glycerol Supplementation for Seasonal Stability in Anaerobic Digestion of Sugarcane Vinasse: Performance and Microbial Shifts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13931, https://doi.org/10.5194/egusphere-egu25-13931, 2025.
Seismic attributes contain a wealth of reservoir information, and the integration of various seismic attributes can enhance the accuracy of reservoir prediction. Due to the complex and heterogeneous underground geological structures, the suitable fusion algorithms vary not only among different oil fields but also at different locations or layers within the same oil field. Therefore, there is an urgent need to explore a multi-algorithm ensemble approach for attribute fusion to improve the generalization capability of seismic attribute integration methods. To improve the accuracy of reservoir prediction, an improved Stacking ensemble model-based method for predicting turbidite reservoirs has been proposed. Firstly, well log seismic attributes are optimized based on correlation analysis and unsupervised clustering techniques to construct a relationship model between seismic attributes and the thickness of turbidite reservoirs, reducing the ambiguity of seismic attributes. Then, hyperparameter optimization of the model is conducted using Optuna, and several types of models with good application effects and significant differences in the field of reservoir prediction are selected as the base learners of the Stacking ensemble model based on root mean square error (RMSE), mean absolute error (MAE), and correlation analysis. Finally, corresponding weights are assigned to the prediction results based on the test accuracy of the base learners, and the original dataset is also included in the meta-learner training, enabling the meta-learner to learn the implicit relationship between the original and new training sets, thereby enhancing the model's predictive performance. This method is applied to the prediction of turbidite reservoirs in the NZ Subsag, and the results show that compared with single prediction models and traditional Stacking ensemble models, the improved Stacking ensemble model significantly reduces the root mean square error in the prediction of turbidite reservoir thickness, and the correlation coefficient between the integrated attributes and sand thickness reaches 0.92, proving that the method has good application prospects.
How to cite: Zhang, S., Zhang, W., and Zhang, J.: The method of turbidite reservoir prediction based on improved Stacking ensemble model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2170, https://doi.org/10.5194/egusphere-egu25-2170, 2025.
The premise of porosity measurement with convention-al methods is to expose all the pores in the shale, which requires oil extraction to remove the retained oil in the shale. Shale oil reservoirs are characterized by oil-bearing, tight (low porosity and permeability), rich in organic matter, and clay swelling. The interactions between the solvents and shale minerals, organic matter, and pores have significant impacts on the pore system. Some researchers tried to evaluate porosity after removing all organic matter from the shale with solvents. Kuila et al.[1] used sodium hypochlorite to extract organic matter from five shales. The results showed that the pore volume increased within certain pore size ranges and decreased within others, which could be attributed to the decrease of organic pores after extraction. Similarly, DiStefano et al.[2] used solvent extraction to remove the organic matter from Eagle Ford shale samples. The results pointed out that the porosity did not always increase with the amount of extraction. This study used different organic solvents to conduct the solvent extraction on clay-rich and carbonate-rich shales. Nuclear magnetic resonance, nitrogen adsorption, and gas measurement were applied to reveal the changes of the solvent extraction efficiency, porosity, and the pore size distributions to investigate the impact of solvent extraction on shale pore system.
The samples used in this study were taken from the fourth member of the Shahejie Formation in the Jiyang Depression of the Bohai Bay Basin and the first member of the Qingshankou Formation in the Gulong Depression of the Songliao Basin. Sample A is a carbonate-rich, low-maturity shale and Sample B is a clay-rich, high-maturity shale.
During the organic solvent extraction, the changes in shale pore systems may result from the combination of the following three mechanisms: (1) The removal of internal fluids (oil and water) and soluble organic matter in shale is the primary reason for the increase of shale pores. (2) During the organic solvent extraction, interactions between shale components and the organic solvents, including clay swelling, extraction of bitumen, and the dissolution of minerals, change in the original pore structure. (3) The kerogen would expand when contact with organic solvents, which will fill partial pores. The expansion coefficient depends on the maturity of the kerogen and the solubility of the organic solvent.
How to cite: Zhao, X. and Wang, M.: The impact of organic solvent oil extraction on shale pore system-Key Issues in Shale Pore Evaluation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8076, https://doi.org/10.5194/egusphere-egu25-8076, 2025.
Abstract: In tight reservoirs, fractures and pores play a crucial role in oil migration, accumulation, and production. However, there is a scarcity of research that concurrently examines both pores and fractures to understand their combined impact on the migration and enrichment of shale oil (tight oil). This study, guided by multidisciplinary theories such as petroleum geology, structural geology, and unconventional oil and gas accumulation theory, innovatively introduces the concept of "fracture-pore facies" and categorizes the reservoir fracture-pore facies into 20 distinct types. Through X-ray diffraction, the mineral content of the Lucaogou Formation reservoir was analyzed, and different mineral contents were observed. The fracture-pore coupling relationships in the reservoir were investigated through core observation, high-pressure mercury injection, cast thin sections, scanning electron microscopy, conventional logging, and image logging, identifying the types of fracture-pore facies in the study area. Physical simulation experiments were conducted to observe the migration and enrichment characteristics of oil in different fracture-pore facies. Based on experimental phenomena related to oil saturation, fracture opening, fracture density, permeability, porosity, and physical simulation of oil migration and accumulation, the fracture-pore facies in the study area were divided into three categories: the first category includes seamless-macropore facies and microfracture-medium-large pore facies; the second category includes multistage microfracture-mesopore facies and seamless-small mesoporous facies; the third category includes microfracture-micropore facies and seamless-micropore facies. Through the enrichment and accumulation models of different types of fracture-pore facies, combined with actual geological data from the study area, three oil enrichment patterns were summarized from the spatial perspective of different types of fracture-pore facies coupling: ① Seamless and medium-large pore coupling rich oil; ② Composite fracture-pore coupling rich oil mode; ③ Single microfracture-micropore coupling rich oil mode. The fracture network or fracture-pore network formed by the coupling of fractures with matrix pores in shale oil (tight oil) can significantly improve reservoir properties and control oil migration, enrichment, and exploitation. The types and classification of fracture-pore facies in shale oil (tight oil) provide important guidance for the study of shale oil (tight oil) migration, accumulation, and production.
How to cite: Zhang, Z. and Wang, M.: Fracture-pore facies types and enrichment models of shale oil (tight oil)- A case study of Lucaogou Formation in Jimusaer Sag, Junggar Basin, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9068, https://doi.org/10.5194/egusphere-egu25-9068, 2025.
One of the key points of oil and gas exploration is the accurate description of reservoir thickness. However, due to the complexity of sand overlapping structure, the actual well earthquake relationship is poor, and the correlation between single seismic attribute and sand body thickness is weak, so the sand body thickness cannot be accurately predicted. In this paper, 8 kinds of seismic attribute information are extracted and selected, and the LightGBM model optimized by Newton-raphson-based optimizer (NRBO) is used to predict reservoir thickness with multiple attribute combination. It is found that the arc length, average amplitude, bandwidth, energy half an hour and other attributes of the selected working area are strongly correlated with the thickness. Meanwhile, the influence of the ratio of validation machine on the prediction results is studied. When the ratio of verification set is 20%, the best prediction effect is obtained, and the effect of the optimized model is significantly improved compared with the traditional machine learning methods such as LightGBM. The study of NRBO-LightGBM model in the prediction of sand body thickness has great popularization value and reference significance.
Newton-raphson-based optimizer (NRBO) is a new meta-heuristic optimization method, which is inspired by two key principles: Newton-Raphson search rule (NRSR) and trap avoidance operator (TAO). NRSR uses Newton-Raphson method to improve the exploration capability of NRBO and increase the convergence rate to achieve improved search space position. TAO helps NRBO avoid the local optimal trap. NRBO has the characteristics of strong evolutionary ability, fast search speed and strong optimization ability. This algorithm was proposed by Sowmya et al in 2024.
A large sample of machine learning reservoir thickness prediction research is carried out. Part of the data samples selected in this paper are actual samples, and part are thickness information predicted by SVM model. Fig.1 shows the prediction results when the proportion of test sets is 20%.
Fig.1 Comparison of well point thickness prediction between NRBO-LightGBM and LightGBM
How to cite: Zhang, W., Zhang, S., Zhang, J., and Gui, Z.: Research on reservoir thickness prediction of river channel sandstone based on ensemble learning model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2756, https://doi.org/10.5194/egusphere-egu25-2756, 2025.
Huizhou 26 sub-sag is rich in oil and gas resources. The oil and gas phases are complex and variable, with unknown origins. Based on the data of crude oil properties, well fluid components, and organic geochemistry, empirical statistical method and PVT phase diagram were used to identify the phase types in the study area. The spatial distribution characteristics and genesis mechanisms were analyzed. The results indicate that the crude oil from Huizhou 26 sub-sag has the characteristics of low density, low viscosity, low sulfur content, and high wax content. It also has three types of hydrocarbon phases: black oil, volatile oil, and condensate gas, among them black oil is the most widely distributed, volatile oil and condensate gas only distribute in the Huizhou 26-6 and 21-8 structural zones. Vertically, the phase types of Enping Formation are mainly volatile oil and condensate gas, while black oil, volatile oil, and condensate gas coexist in Wenchang Formation. Some wells exhibit a characteristic of vertical distribution of oil and gas alternation. The spatial differences of oil and gas phases are mainly controlled by the thermal evolution degree of source rocks and the filling process of oil and gas. Among them, the maturity of oil and gas corresponding to the main filling period and the proportion of late high maturity oil and gas mixing are the main reasons. Based on a comprehensive analysis of the formation stages and processes of different types reservoirs in the study area, three phase evolution models were established, namely "First stage oil phase filling", "Two stages oil phase superposition", and "Early oil phase, late gas phase". This study seeks to provide theoretical guidance for efficient exploration of deep hydrocarbons.
Keywords: Huizhou depression; oil and gas phases; condensate gas; differential filling of oil and gas
How to cite: Han, X., Liu, H., and Hou, M.: Characteristics and Genetic Mechanisms of Oil and Gas Phases in the Paleogene of Huizhou 26 Sub-sag in Huizhou Depression, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5201, https://doi.org/10.5194/egusphere-egu25-5201, 2025.
Development of methods of artificial intelligence in geophysics often require heavy computations using high-performance computing (HPC) with graphics processing units (GPUs). Given the ongoing energy crisis and exponentially growing demand for data storage and computational resources, the concept of Frugal AI has emerged recently. While there are attempts to use renewable energy for supplying data centres and HPC clusters, globally the energy supply remains being dominated by fossil fuels [1].
Frugal AI proposes to analyse and optimise the use of energy and water resources for data centres, computational power for data processing, AI model training and deployment for environmental sustainability of AI.
In support of the EU AI Act [2], there are significant efforts to develop standardisation documents for environmentally sustainable AI solutions (see draft technical reports ISO 20226 [3] and CEN/CENELEC 18145 [4]). The purpose of these standards is to offer AI stakeholders a practical approach to quantification of the environmental impact of AI solutions. In particular, carbon emissions of scopes 1,2,3 can be quantified with associated uncertainties. Scope 1 are direct emissions [5] which may occur in data centres that use reserve fossil-fuel generators (this is usually a temporary solution for autonomous power supply). Scope 2 are indirect emissions that are produced due to electricity consumption. It is possible to quantity such carbon emissions using a dynamic carbon factor based on real-time fuel mix in electricity generation, known power of used hardware modules (processors, memory modules, and GPUs), and duration of training and deployment of an AI solution. Scope 3 carbon emissions [6] are based on life-cycle assessment of an AI solution (“from cradle to grave”), which includes hardware manufacturing and software development and deployment.
It is not seldom that AI models are deployed in default non-optimised mode for trial-and-error experiments, which may take a lot of resources and produce a large carbon footprint. The output data of large-scale geophysical models augmented with AI may be stored in an inefficent way, which can be improved using good practices (in appropriate formats, suitable temporal and spatial resolution).
We will discuss various aspects of Frugal AI in geophysics and suggest what optimised techniques can be used at each stage of AI development for geophysical applications. Frugal AI development may include simpler algorithms that can achieve comparable results with significantly smaller energy consumption; refined processes of data gathering for capturing essential information for AI training (for example, sparse datasets); optimised designs of training models; and deployment of AI models for energy-saving technologies, such as Demand-Side Response (DSR). Further intervention may include use of energy-saving hardware units and analysis of AI life cycle, which would identify stages of AI use that require most resources, and how those can be reduced.
References
[1] World energy report 2023, https://www.energyinst.org/__data/assets/pdf_file/0006/1542714/684_EI_Stat_Review_V16_DIGITAL.pdf
[2] The EU Artificial Intelligence Act, https://artificialintelligenceact.eu/
[3] ISO DTR 20226 Information technology --- Artificial intelligence --- Environmental sustainability aspects of AI systems.
[4] CEN/CENELEC DTR 18145 Environmentally Sustainable AI
[5] ISO 20181 Stationary source emissions. Quality assurance of automated measuring systems.
[6] ISO 5338 Information technology --- Artificial intelligence --- AI system life cycle processes.
How to cite: Livina, V. N.: Frugal AI in geophysics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14712, https://doi.org/10.5194/egusphere-egu25-14712, 2025.
The rapid advancement of artificial intelligence (AI) technology is driving transformative changes across society. However, this progress also entails significant resource and energy demand, posing substantial new challenges to the Earth’s ecosystems. Specifically, the environmental impacts arising from AI model training and inference, data center operations, and the manufacturing and disposal of electronic devices threaten the balance of ecosystem material cycles and could exacerbate climate change. Therefore, it is urgently needed to understand the effects of generative AI technology growth on ecosystem material cycles and to identify sustainable AI technology development and application strategies. This study aims to quantitatively assess the resource consumption (including metals, plastics, and water), exergy use (primarily through electricity demand and fossil fuels), and greenhouse gas emissions associated with the anticipated growth of generative AI technology and its consequent impacts on ecosystem material cycles. First, we analyze resource and exergy use within the generative AI industry, encompassing AI model training and inference, data center operations, and the production of AI chips and devices. We quantify the consumption of key elements and water, alongside the exergy demand for electricity and fossil fuels. We employ a Life Cycle Assessment (LCA) methodology to evaluate the comprehensive environmental footprint of AI technology. Second, we examine the environmental impact of AI-related waste by evaluating the generation, treatment processes, and ecosystem effects of electronic waste (including AI chips, devices, and data center equipment). This analysis focuses on the environmental leakage pathways of hazardous and plastic waste and the patterns of material movement within the ecosystem, particularly with regards to soil and water pollution and biodiversity loss. Third, we model the impact of generative AI technology on key ecosystem material cycles, such as carbon, nitrogen, and phosphorus. We estimate changes in resource use, exergy consumption, and waste generation under multiple AI technology growth scenarios. Finally, we propose strategies for the sustainable development and application of AI technologies. Based on our findings, we will formulate concrete policy and technical recommendations for developing and implementing resource-efficient and low-exergy-consuming AI technologies.
How to cite: Park, H., Song, C., and Lee, W.-K.: A Study on the Impact of Generative Artificial Intelligence Growth on Ecosystem Material Cycles: Analyzing Resource Use, Exergy Use, and Greenhouse Gas Emissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19301, https://doi.org/10.5194/egusphere-egu25-19301, 2025.
TETRA – the Toolbox and mEthodology for waTeR based AI projects
The development of modern and efficient tools for monitoring water resources is crucial for ensuring the sustainable availability of this essential resource, which is of great value to both humanity and the environment. Events like the fish die-off in the Oder River underscore the pressing need for improved river protection. The TETRA project aims to enable and accelerate the use of artificial intelligence (AI) in water management. Additionally, the bilateral collaboration of both German and French companies fosters the development of a shared European ecosystem for AI applications in the water sector.
The project’s goal is to develop and provide tools and methods that enable the successful implementation of AI projects in the field of water management. These will be made publicly accessible to both German and French stakeholders to facilitate and promote collaboration with a common toolkit and approach for AI projects. The evaluation of the tools and methods will be based on two use cases: monitoring the water quality of rivers and river restoration.
The TETRA methodology is based on the already available PAISE process model, which was specifically developed for the integration of AI methods into industrial processes and is being adapted for application in water management. Within the scope of PAISE, a toolbox with specific AI tools will be developed. Several applications will be utilized in this context.
The FROST server, provided by Fraunhofer IOSB, is an open-source implementation of the OGC SensorThingsAPI that manages and stores sensor data needed for analysis by AI algorithms. FROST will be extended to FROST-AI within the project to meet specific AI integration requirements. The developed algorithms will be integrated into PERMA, an open-source software developed by Fraunhofer IOSB that enables the management and parameterization of algorithms.
GLUON, a tool for creating and managing ontologies, enables the integration of expert knowledge into AI algorithms. If facilitates semantic search and knowledge modeling in water management.
Edge AI analysis employs technologies to analyze data directly on sensors (edge computing) to reduce latency and ensure data privacy.
Godot Search is a semantic search module that can be used to understand user queries through ontologies to find relevant information more efficiently, and will be improved throughout the project.
Case-Based Reasoning (CBR) for river restoration utilizes case studies and expert knowledge to improve restoration measures in water management.
The ontology, knowledge base, and data from the FROST server will be made available in collaboration with all partners in the TETRA Showcase via WebGenesis (IOSB) in a web portal for demonstration purposes. Through TETRA, a framework for the integration of the AI algorithms and standardized data storage will be created, forming a starting point for a shared ecosystem dedicated to AI-based water projects.
This research has received funding from the BMBF’s (Bundesministerium für Bildung und Forschung) directive on the funding of Franco-German projects on the topic of artificial intelligence, Federal Gazette of 20th June 2022.
How to cite: Zenner, M., Hellmund, T., and Moßgraber, J.: TETRA – the Toolbox and mEthodology for waTeR based AI projects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20110, https://doi.org/10.5194/egusphere-egu25-20110, 2025.
The extensive use of petroleum-based plastics has led to severe environmental pollution, emphasizing the need for sustainable alternatives such as biodegradable polyhydroxybutyrate (PHB). Conventional PHB production using heterotrophic microorganisms requires external organic carbon sources, contributing to high production costs. Additionally, the separation of the biomass growth phase and the PHB accumulation phase in conventional systems limits production efficiency. This study introduces a dual-phase hybrid cultivation system designed to achieve simultaneous biomass growth and PHB accumulation in the cyanobacerium Synechococcus sp. The system alternates between a light phase that supports biomass growth via photosynthesis and a dark phase that promotes PHB synthesis. Synechococcus sp. was selected for its ability to fix CO2 as a carbon source, reducing the need for external organic carbon supplementation. To further enhance PHB accumulation, carbon by products extracted from cyanobacteria cell debris—including fatty acids, polysaccharides, and amino acids—were supplemented during cultivation. During the 14-day cultivation period, the hybrid system maintained biomass growth at a level similar to the conventional system. Meanwhile, PHB accumulation reached 7% (w/w DCW), over three times higher than the 2% in the conventional system. This demonstrates the system’s ability to enhance PHB synthesis without compromising biomass growth. carbon by products increased PHB production by approximately 40% compared to the conventional system without supplementation. This represents increases of 13% in PHB yield under non-supplemented conditions. These results indicate that the dual-phase hybrid cultivation system using internal carbon sources improves PHB production and offers a promising approach for bioplastics manufacturing.
How to cite: Shim, G.: Development of Dual Phase Hybrid Cultivation System to Enhance Polyhydroxybutyrate (PHB) Production by Synechococcus sp., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7798, https://doi.org/10.5194/egusphere-egu25-7798, 2025.
